The LLM Encyclopedia, March 28, 2026
The most comprehensive reference for every major AI language model. 60+ models, 22 use cases, full pricing tables — updated weekly.
The Developer’s Complete LLM Comparison Guide (March 28, 2026)
Every Major, Minor, Niche, Open-Source, and Specialized Language Model — Researched, Compared, and Rated
Accuracy note: All model versions, release dates, pricing, and benchmark data reflect publicly confirmed information as of March 28, 2026. This is a fast-moving field — verify pricing and availability against official provider docs before production deployment.
Table of Contents
- What Is an LLM? A Developer’s Primer
- How to Read This Guide
- Tier 1 — Flagship Proprietary Models
- GPT Series (OpenAI)
- Claude Series (Anthropic)
- Gemini Series (Google DeepMind)
- Grok Series (xAI)
- Tier 2 — Strong Proprietary Challengers
- Perplexity (Sonar)
- Microsoft Copilot / Azure OpenAI
- Cohere Command R+
- Amazon Nova / Titan
- Tier 3 — Open-Source Powerhouses
- Meta Llama Series
- Mistral / Mixtral Series
- DeepSeek Series
- Qwen Series (Alibaba)
- Gemma Series (Google)
- IBM Granite
- Falcon Series (TII)
- Microsoft Phi Series
- BLOOM (BigScience)
- OLMo (Allen Institute)
- NVIDIA Nemotron
- Tier 4 — Chinese Frontier Models
- Baidu ERNIE
- Zhipu GLM-5 / ChatGLM
- Moonshot Kimi
- Baichuan
- Yi (01.AI)
- MiniMax
- Hunyuan (Tencent)
- InternLM (Shanghai AI Lab)
- ByteDance Seed
- Tier 5 — Coding-Specialist Models
- GitHub Copilot
- DeepSeek Coder / Prover
- StarCoder / StarCoder2
- CodeLlama
- Codestral / Devstral (Mistral)
- WizardCoder
- Qwen Coder
- Amazon Q Developer
- Tabnine
- Tier 6 — Domain-Specific Models
- Healthcare: Med-PaLM 2, MedLLaMA, BioMedLM, ClinicalBERT
- Finance: BloombergGPT, FinGPT
- Legal: Harvey AI, CoCounsel, ChatLAW
- Science: Galactica, SciGLM
- Cybersecurity
- Tier 7 — Edge / On-Device / Small Models
- Tier 8 — Research & Historical Models
- Pricing Comparison Table (March 28, 2026)
- Benchmark Comparison
- Choosing the Right LLM: Decision Framework
- Real-World Enterprise Success Stories
- Trends and What’s Coming in 2026–2027
What’s New This Week
-
DeepSeek V4 officially announced: DeepSeek ends months of speculation with a formal reveal of V4 — a 1.2T parameter sparse MoE multimodal model supporting text, image, and video understanding. API access enters limited beta for select partners; public release targeted for mid-April 2026. Early benchmark leaks suggest AIME 2025 scores above 95% and SWE-bench Verified above 78%, which would make it the strongest open-weight model ever released by a significant margin. MIT license confirmed.
-
Meta releases Llama 4 Behemoth technical report: Meta publishes the full technical report for Llama 4 Behemoth — the 288B active / 2T total parameter model that powers Maverick and Scout behind the scenes. The paper details the FP8 training pipeline, the 100K+ GPU cluster used, and reveals that Behemoth achieves 96.2% on AIME 2025 — higher than any previously published open-weight score. Model weights remain unreleased; Meta cites ongoing safety evaluations.
1. What Is an LLM? A Developer’s Primer
A Large Language Model (LLM) is a deep learning system trained on massive corpora of text (and increasingly images, audio, and video) to predict and generate human-like language. Built on the Transformer architecture (Vaswani et al., 2017), LLMs are characterized by billions of parameters — the numerical weights learned during training that encode knowledge about language, facts, and reasoning.
Key concepts every developer needs to know:
- Parameters: The “weights” inside a model. More parameters generally means more capacity, but not always better performance. A 7B model with excellent training data can outperform a 70B model trained poorly.
- Context Window (Tokens): How much text the model can “see” at once. A token ≈ 0.75 words. A 1M context window can process ~750,000 words in one shot.
- Inference: The process of running a trained model to generate output. This is what you pay for when using APIs.
- Fine-tuning: Continuing training a base model on domain-specific data to specialize it.
- RLHF: Reinforcement Learning from Human Feedback — human raters rank outputs to teach the model to be more helpful and less harmful.
- MoE (Mixture of Experts): Architecture where only a subset of parameters (“experts”) activate per token, enabling massive total parameter counts with lower compute cost. Used in Mixtral, DeepSeek V3, Llama 4, Grok, and others.
- RAG (Retrieval-Augmented Generation): Pairing an LLM with a vector database so it can look up external documents before answering — reducing hallucinations.
- Quantization: Compressing model weights (e.g., 32-bit floats → 4-bit integers) to reduce VRAM requirements and increase inference speed with minimal quality loss.
- Extended Thinking / Chain-of-Thought: The model reasons internally before producing an answer, trading latency for accuracy on hard problems. Now standard across frontier models.
- Computer Use: Models that can see a screen, move a cursor, click, and type — enabling truly autonomous agentic workflows. Native in GPT-5.4, Claude 4.x, and Gemini 3.x as of 2026.
2. How to Read This Guide
Each model entry uses a consistent structure:
| Field | Description |
|---|---|
| Released | Date of first public availability |
| Developer | Organization behind the model |
| Type | Proprietary / Open-weight / Open-source |
| Context Window | Maximum token input |
| Strengths | What it genuinely does well |
| Weaknesses | Honest limitations |
| Best For | Ideal use cases and user profiles |
| Constraints | Rate limits, data policies, license restrictions |
| Cost | API pricing per million tokens (input / output), March 2026 |
| Real-World Use | Documented production deployments |
3. Tier 1 — Flagship Proprietary Models
These are the frontier models competing at the highest capability level. They define the industry benchmark each quarter.
🟢 GPT Series — OpenAI
Developer: OpenAI
Type: Proprietary (closed-source)
Headquarters: San Francisco, CA
OpenAI’s GPT family is the most recognized LLM series in the world. The progression: GPT-3 (2020) launched the modern LLM era; ChatGPT (Nov 2022, GPT-3.5) made it a consumer phenomenon; GPT-4 (2023) set new benchmarks; GPT-4o (May 2024) brought true multimodality; GPT-5 (mid-2025) unified reasoning and conversation; and GPT-5.4 (March 5, 2026) is the current flagship.
Current GPT-5 Family (as of March 28, 2026)
| Model | Released | Context | Role |
|---|---|---|---|
| GPT-5.4 | March 5, 2026 | 1M (API) / 272K (ChatGPT) | Current frontier flagship |
| GPT-5.4 Thinking | March 5, 2026 | 272K | Reasoning variant; replaced 5.2 Thinking |
| GPT-5.4 Pro | March 5, 2026 | 272K | Maximum performance; Pro/Enterprise only |
| GPT-5.3 Codex | Feb 5, 2026 | 256K | Coding specialist; still active |
| GPT-5.2 | Late 2025 | 400K | Previous flagship; still available |
| GPT-5.2 Thinking | Late 2025 | 200K | Being retired June 5, 2026 |
| GPT-5 Mini | 2025 | 128K | Budget tier |
| GPT-5 Nano | 2025 | 128K | Ultra-budget tier |
| GPT-OSS 20B / 120B | 2025 | 128K | Open-weight, Apache 2.0 |
Note: As of March 11, 2026, GPT-5.1 models are no longer available in ChatGPT. GPT-5.2 Thinking remains available through June 5, 2026, then retires.
GPT-5.4 (Current Flagship)
Released: March 5, 2026
Context Window: 1,000,000 tokens (API); 272,000 tokens (ChatGPT)
Strengths:
- First mainline reasoning model to incorporate the coding capabilities of GPT-5.3-Codex — unifying coding, reasoning, and general intelligence in one model
- Native computer use in the API: can see screens, move cursors, click elements, type, and navigate desktop applications programmatically
- Upfront planning in ChatGPT Thinking mode: shows its reasoning plan before answering so you can steer it mid-response
- 33% fewer false individual claims and 18% fewer responses containing any errors vs. GPT-5.2
- Tool Search: new system that lets the model look up tool definitions on-demand rather than loading all definitions upfront — dramatically more token-efficient in tool-heavy agentic systems
- Record scores on OSWorld-Verified and WebArena Verified computer-use benchmarks
- 83% on GDPval (knowledge work tasks); #1 on Mercor’s APEX-Agents benchmark (professional skills in law and finance)
- “BigLaw Bench” score of 91% — praised specifically for structuring complex transactional legal analysis
- 87.3% preference rate over GPT-5.2 in investment banking/financial modelling tasks
- 1M token context window in the API makes it viable for processing entire codebases or document archives in one session
Weaknesses:
- Proprietary and closed-source — no auditing, fine-tuning, or self-hosting
- ChatGPT UI context window (272K) smaller than API (1M) — matters for very long document workflows
- GPT-5.4 Pro pricing is extreme for high-volume use
- Not yet available to free-tier users (Plus, Team, Pro, Enterprise only for Thinking/Pro variants)
Best For: Enterprise professional workflows, legal and financial analysis requiring maximum accuracy, developers building agentic systems with computer use, complex multi-step reasoning tasks, coding at frontier quality
Constraints: Plus ($20/month) for standard access; Pro ($200/month) for GPT-5.4 Pro; Enterprise for early access; API access via standard OpenAI account; zero data retention options on Enterprise
Cost (API):
- GPT-5.4: ~$2.50/M input, ~$10/M output
- GPT-5.4 Pro: premium pricing (contact sales)
- GPT-5.2: ~$1.75/M input, ~$14/M output
- GPT-5 (base): ~$1.25/M input, ~$10/M output
- GPT-5 Mini: ~$0.25/M input, ~$2/M output
- GPT-5 Nano: ~$0.05/M input, ~$0.40/M output
Real-World Use:
- Morgan Stanley: GPT-4 powered AI assistant saves financial advisors 10–15 hours/week; GPT-5.4 now used for investment banking document workflows
- Duolingo Max: GPT-4/5 powers conversation practice and contextual grammar explanation
- Khan Academy (Khanmigo): Socratic AI tutor using GPT across all K-12 subjects
- GitHub Copilot: GPT-5.4 available as an option in GitHub Copilot’s multi-model picker
GPT-5.3 Codex (Coding Specialist)
Released: February 5, 2026
Context Window: 256,000 tokens
The predecessor to GPT-5.4 that specialized in coding. Still active; faster and cheaper than GPT-5.4 for pure coding workloads. GPT-5.4 has now absorbed Codex’s capabilities, but Codex remains available for teams that need cost-efficiency on coding tasks specifically.
GPT-OSS (Open-Weight Series)
Released: 2025
Context Window: 128,000 tokens
Type: Open-weight (Apache 2.0)
Sizes: 20B and 120B
OpenAI’s first open-weight release since GPT-2 (2019). Both use MoE architecture. The 120B model is competitive with frontier proprietary models on many benchmarks. Not exposed in ChatGPT UI — designed for local deployment and agentic tasks. A GPT-OSS-Safeguard variant (20B) also released for content moderation workflows.
Cost: Free (self-hosted); hosted via providers like Groq
🟣 Claude Series — Anthropic
Developer: Anthropic
Type: Proprietary (closed-source)
Founded: 2021 by Dario Amodei, Daniela Amodei, and former OpenAI researchers
Anthropic’s Claude is built around Constitutional AI — a training framework where the model follows a set of explicit, human-readable principles. The 2026 Constitution has expanded to 23,000 words (up from 2,700 in 2023), providing more context and rationale for guidelines. Claude is consistently ranked best for long-context processing, nuanced instruction following, safety-critical enterprise applications, and agentic coding.
Current Claude Family (as of March 28, 2026)
| Model | Released | Context | Role |
|---|---|---|---|
| Claude Opus 4.6 | February 5, 2026 | 1M tokens (default on Max/Team/Enterprise) | Current power flagship |
| Claude Sonnet 4.6 | February 17, 2026 | 1M tokens (beta) / 200K (default) | Current balanced flagship |
| Claude Haiku 4.5 | October 15, 2025 | 200K | Fast / budget tier |
| Claude Opus 4.5 | 2025 | 200K | Previous generation; still available |
| Claude Sonnet 4.5 | 2025 | 1M (beta) | Previous generation |
| Claude 3 Haiku | 2024 | 200K | Retiring April 19, 2026 |
Deprecation notice: Claude Opus 4 and 4.1 have been removed from the model selector. Claude 3 Haiku (claude-3-haiku-20240307) retires April 19, 2026 — migrate to Haiku 4.5. Claude 2, 2.1, and Sonnet 3 are deprecated.
Claude Opus 4.6 (Current Power Flagship)
Released: February 5, 2026
Context Window: 1,000,000 tokens (default on Max, Team, Enterprise; previously required extra usage)
Strengths:
- 1M token context window now available by default for Max/Team/Enterprise — enough to process entire corporate document libraries in one session
- 14.5-hour task completion time horizon — the longest autonomous operation window of any model as of February 2026
- #1 on Finance Agent benchmark as of February 2026
- 61.4% on OSWorld (computer use benchmark) — best in class
- Strongest reasoning depth in Claude family; extended thinking mode with self-reflection loops
- In February 2026: 16 Opus 4.6 agents collaboratively wrote a C compiler in Rust from scratch, capable of compiling the Linux kernel
- Used by Norway’s $2.2 trillion sovereign wealth fund to screen its entire portfolio for ESG risks
- Found over 100 bugs in Firefox in a two-week scan (14 high-severity) — demonstrating real-world agentic debugging depth
- Claude Code (paired with Opus 4.6) considered the best AI coding assistant as of January 2026
- Claude Code Security: reviews entire codebases for vulnerabilities (launched February 2026)
Weaknesses:
- Slower than Sonnet; higher cost — overkill for most routine tasks
- Proprietary; all data through Anthropic servers
- Anthropic refused in February 2026 to remove contractual prohibitions on use for mass domestic surveillance and fully autonomous weapons — U.S. federal agency use is being phased out as a result
Best For: Highest-stakes long-horizon tasks, financial analysis, compliance-critical document review, agentic coding, scientific research, tasks requiring the model to “stay in context” for hours
Cost: ~$5/M input, ~$25/M output (down from $15/$75 for Opus 4.1 — a 67% price drop)
Claude Sonnet 4.6 (Current Balanced Flagship)
Released: February 17, 2026
Context Window: 1M tokens (beta); 200K (default)
Strengths:
- Near-Opus-level performance on coding, document comprehension, and office tasks
- Significantly improved computer use: can navigate browsers, fill forms, operate software autonomously
- Better instruction following with fewer errors and less hallucination vs. prior versions
- Best value in the Claude family — handles tasks that previously required Opus
- Agentic search performance improvement while consuming fewer tokens
- Supports extended thinking; structured outputs GA; web search and web fetch now generally available (no beta header)
- Microsoft M365 Copilot now offers Claude Sonnet models to enterprise users (announced March 9, 2026)
- Data residency controls: can specify US-only inference with the
inference_geoparameter (1.1x pricing)
Weaknesses:
- May decline borderline creative/grey-area requests more than competitors
- Not the fastest model for latency-sensitive real-time applications
- Proprietary; enterprise pricing requires sales contact for full suite
Cost: ~$3/M input, ~$15/M output
Real-World Use:
- Deployed widely in enterprise knowledge management, legal document review, and code review workflows
- Notion AI, Quora Poe among major consumer integrations
- Used by NASA: Claude Code prepared a ~400m route plan for Mars rover Perseverance in December 2025
Claude Haiku 4.5
Released: October 15, 2025
Context Window: 200,000 tokens
The fastest, cheapest Claude model. Designed for high-volume, low-latency applications where sub-second response matters.
Best For: Customer service bots, content moderation, classification, simple summarization, real-time chat
Cost: ~$1/M input, ~$5/M output
Looking ahead: A “Claude 5” (codenamed “Fennec” for Sonnet 5) has appeared in Google Vertex AI logs with a version string
claude-sonnet-5@20260203. As of this writing, Anthropic has not officially confirmed a release date, but industry sources suggest February–March 2026 timing. Expected features: coding capabilities exceeding Opus 4.5, a “Dev Team” multi-agent collaboration mode, pricing ~50% lower than current flagship models.
🔵 Gemini Series — Google DeepMind
Developer: Google DeepMind
Type: Proprietary (closed-source)
First Released: December 2023
Google’s Gemini family replaced PaLM/Bard. Gemini’s core advantage is native multimodality — built from the ground up to process text, images, audio, video, and code simultaneously.
Current Gemini Family (as of March 28, 2026)
| Model | Released | Context | Role |
|---|---|---|---|
| Gemini 3.1 Pro | February 19, 2026 | 1M | Current flagship reasoning model |
| Gemini 3.1 Flash-Lite | March 3, 2026 | 1M | Cost-efficient, fastest in Gemini 3 series |
| Gemini 3 Flash | Late 2025 | 128K | Default model in Gemini app |
| Gemini 2.5 Pro | March 2025 | 1M | Still available; previous flagship |
| Gemini 2.5 Flash | 2025 | 1M | Strong budget option |
| Gemini 2.0 Flash-Lite | 2025 | 128K | Ultra-budget |
| Nano Banana 2 | February 26, 2026 | — | Image generation (Gemini 3.1 Flash Image) |
| Gemini Embedding 2 | March 10, 2026 | — | Multimodal embedding model |
Deprecation: Gemini 3 Pro Preview shut down March 9, 2026 — migrate to Gemini 3.1 Pro Preview. Several 2.5 models being shut down June 1, 2026.
Gemini 3.1 Pro (Current Flagship)
Released: February 19, 2026
Context Window: 1,000,000 tokens
Strengths:
- Upgraded core reasoning; significant improvement on complex problem-solving benchmarks over Gemini 3 Pro
- Deep integration with Google Workspace (Docs, Sheets, Gmail, Drive, NotebookLM)
- Available via Gemini API (AI Studio), Vertex AI, Gemini Enterprise, Gemini CLI, Google Antigravity, Android Studio
- Available in Gemini app for Pro/Ultra subscribers; rolling out globally
- Native computer use tool supported (launched with Gemini 3 Pro; carried into 3.1)
- Supports Gemini 3.1 Pro Preview (in developer API), Gemini CLI for agentic development
Weaknesses:
- Premium pricing vs. competitors at similar capability
- Somewhat ecosystem-locked to Google infrastructure for best results
- Historical image generation controversy in early 2024
Cost: ~$2/M input, ~$18/M output (Gemini 3.1 Pro); ~$2/M input, ~$12/M output (Gemini 3.1 Pro via AI Studio)
Gemini 3.1 Flash-Lite (Newest Budget Model)
Released: March 3, 2026
Context Window: 1,000,000 tokens
Strengths:
- 45% faster output speed and 2.5x lower time-to-first-token than Gemini 2.5 Flash
- Elo score of 1432 on Arena.ai — beats models from prior generations despite budget positioning
- 86.9% on GPQA Diamond (doctoral-level science); 76.8% on MMMU Pro — outperforms larger older models
- Beats GPT-5 Mini and Claude Haiku 4.5 across 6 of 11 benchmarks per Google’s internal tests
- Ideal for translation, content moderation, UI generation, simulations
- Available in preview via Gemini API / AI Studio and Vertex AI
Cost: $0.25/M input, $1.50/M output
Gemini 3 Flash (Default App Model)
Released: Late 2025
Context Window: 128,000 tokens
Now the default model in the Gemini app, replacing 2.5 Flash. PhD-level reasoning at Flash speed. Significant leap in multimodal understanding. 78% on SWE-bench Verified in coding tasks.
Cost: ~$0.50/M input, ~$3/M output
Gemini Embedding 2
Released: March 10, 2026
The first truly multimodal embedding model — brings text, images, video, audio, and documents into a single unified embedding space. Processes up to 8,192 text tokens, six images, 120-second videos, native audio, and PDFs of up to six pages. Supports Matryoshka Representation Learning for flexible output dimensions (768, 1536, or 3072). Outperforms leading competitors in text, image, and video embedding benchmarks.
Best For: Advanced RAG, semantic search across multimedia content, data clustering across modalities
Gemma 3 (Open-Weight from Google)
Released: March 2025
Type: Open-weight
Sizes: 1B, 4B, 12B, 27B
Trained on the same infrastructure as Gemini but released as open weights. All variants are multimodal (text + image).
Strengths: Google-quality training, runs on consumer hardware, free, multimodal
Weaknesses: Smaller models lack reasoning depth of 70B+ open models
Best For: Local deployment, privacy-first apps, offline AI, Google-ecosystem developers
Cost: Free (self-hosted); Google AI Studio API pricing varies
⚡ Grok Series — xAI
Developer: xAI (Elon Musk)
Type: Grok-1 open-sourced (MoE, 314B); Grok 2+ proprietary
Launched: November 2023
Deeply integrated with X (formerly Twitter). Real-time social data access is a core differentiator. Intentionally less restricted than competitors.
Current Grok Family (as of March 28, 2026)
| Model | Released | Context | Role |
|---|---|---|---|
| Grok 4.20 | February 17, 2026 | 256K | Current flagship; four-agent architecture |
| Grok 4.20 Multi-Agent Beta | March 2026 | 256K | Collaborative multi-agent variant |
| Grok 4.1 | November 2025 | 256K | Previous flagship; still available |
| Grok Code Fast 1 | 2025 | 128K | Agentic coding specialist |
| Grok Voice | 2025 | — | Real-time voice agent; in Tesla vehicles |
| Grok Imagine API | March 2026 | — | Video + audio generation |
xAI scale: Approximately 600 million monthly active users across X and Grok apps. Colossus I and II supercomputers: over 1 million H100 GPU equivalents. Grok 5 reported to be in training.
Grok 4.20 (Current Flagship)
Released: February 17, 2026
Context Window: 256,000 tokens
Strengths:
- Four-agent parallel processing architecture (“study group”): multiple agents reason simultaneously, then aggregate solutions — especially powerful for math proofs, complex research, and multi-step planning
- Standard, Spicy (less restricted for Premium+), and Extended Thinking modes
- Lowest hallucination rate in the xAI lineup; strictly follows prompts
- Deep integration with X/Twitter real-time data
- Grok 4.20 Multi-Agent Beta: collaborative agents for deep research and tool coordination
- Real-time financial market monitoring; web + social data as first-class context
- Grok Voice: live in Tesla vehicles and the Grok mobile app, low-latency speech in dozens of languages
Weaknesses:
- Full access requires X Premium+ subscription ($16/month for SuperGrok)
- Enterprise compliance certifications (HIPAA, SOC 2, GDPR) less mature than competitors
- Regulatory scrutiny: UK ICO investigation (Feb 3, 2026) and Ireland DPC formal investigation (Feb 17, 2026) into data handling
- The “witty/irreverent” personality is a mismatch for formal enterprise workflows
Best For: Real-time information tasks, social media analysis, financial market monitoring, research tasks requiring multi-agent parallelism, users wanting a less restricted creative assistant
Cost: Grok 4.1 API: ~$3/M input, ~$15/M output; Grok 4.1 Fast: ~$0.20/M input, ~$0.50/M output; X Premium+: $16/month
4. Tier 2 — Strong Proprietary Challengers
🔍 Perplexity AI (Sonar Models)
Developer: Perplexity AI
Type: Proprietary platform (orchestrates frontier models)
Users: ~22 million monthly active users (2025)
Perplexity is less a standalone LLM and more a search-augmented AI platform built on top of frontier models. Every answer includes live citations.
Strengths: Citations on every answer; real-time web access as core (not a plugin); Sonar Pro: research-grade cited answers; access to GPT-5, Claude, Gemini within Pro ($20/month); dominant for research-heavy workflows
Weaknesses: Not a standalone LLM; weaker on creative or open-ended generation
Best For: Researchers, journalists, analysts, competitive intelligence, literature review
Cost: Free tier; Pro: $20/month; Sonar API: ~$1/M input, ~$1/M output
🏢 Microsoft Copilot / Azure OpenAI
Developer: Microsoft (powered by OpenAI GPT-5.4, Phi-4, Claude, Gemini)
Released: GitHub Copilot 2021; M365 Copilot 2023
Not a single model — a family of AI products embedded across the Microsoft stack. Multi-model: admins can select GPT-5.2/5.4, Claude Opus/Sonnet 4.6, or Gemini 3.1 Pro.
Strengths: Embedded in Office 365, Teams, Outlook, SharePoint; GitHub Copilot: 20M users, 90% Fortune 100; Azure: GDPR/HIPAA/SOC 2; zero data retention options
Weaknesses: Not the best raw capability; sensitive record exposure risk if permissions misconfigured
Cost: GitHub Copilot Pro: $10/month; Business: $19/user/month; Enterprise: $39/user/month
Real-World Use: BNY Mellon (80%+ devs use daily); DNV shipping (90% compliance effort reduction); DoozyTemps (60% call volume reduction)
🟡 Cohere Command R+
Developer: Cohere
Released: April 2024
Context Window: 128,000 tokens
Purpose-built for RAG. Native tool use. Multilingual across 10+ business languages. Research weights on HuggingFace (non-commercial).
Best For: Enterprise RAG systems, multilingual document Q&A, knowledge base search
Cost: ~$2.50/M input, ~$10/M output
🟠 Amazon Nova / Bedrock
Developer: AWS
Released: Nova family late 2024
Available through Amazon Bedrock alongside third-party models (Llama, Claude, Mistral). Nova Micro is one of the cheapest capable models in existence.
Best For: AWS-first organizations, cost-sensitive production workloads
Cost: Nova Micro: ~$0.035/M input, ~$0.14/M output; Nova Pro: ~$0.80/M input, ~$3.20/M output
5. Tier 3 — Open-Source Powerhouses
🦙 Meta Llama Series
Developer: Meta AI
Type: Open-weight (Meta community license; commercial use permitted for most)
First Released: February 2023 (Llama 1)
The most influential open-weight model family in history, enabling self-hosting, fine-tuning, and a massive community ecosystem.
Llama Versions Overview
| Version | Released | Context | Key Feature |
|---|---|---|---|
| Llama 1 | Feb 2023 | 2K | Started the open-weight revolution |
| Llama 2 | July 2023 | 4K | First widely commercial open-weight model |
| Llama 3 | April 2024 | 8K | Strong performance at 8B and 70B |
| Llama 3.1 | July 2024 | 128K | 405B flagship; multilingual |
| Llama 3.2 | Sept 2024 | 128K | Added 1B, 3B edge models; vision capability |
| Llama 3.3 | Dec 2024 | 128K | 70B; improved multilingual instruction |
| Llama 4 Scout | April 2025 | 10M | 109B total / 17B active (MoE) |
| Llama 4 Maverick | April 2025 | 1M | Beats GPT-4o on most benchmarks |
Llama 4 Strengths:
- Scout: 10M context window on a single H100 GPU using MoE architecture
- Maverick: outperforms GPT-4o and Gemini 2.0 Flash on coding, reasoning, multilingual
- Fully open-weight: self-host for free, fine-tune, run in air-gapped environments
- Enormous community: most fine-tunes and tools of any open model family
Weaknesses: Llama 4 lost download momentum to Qwen3 by late 2025 despite strong benchmarks; 405B Llama 3.1 requires significant multi-GPU infrastructure; lighter alignment than Claude
Cost: Free (self-hosted); hosted via AWS Bedrock, Together AI, Fireworks, Groq (~$0.05–$0.90/M depending on provider and size)
🌪️ Mistral / Mixtral Series
Developer: Mistral AI (Paris, France)
Type: Apache 2.0 open-weight (most models) + proprietary API
Founded: 2023 by former DeepMind and Meta AI researchers
Leading European AI lab. Champion of open-source efficiency.
Mistral Models Overview
| Model | Released | Context | Type |
|---|---|---|---|
| Mistral 7B | Sept 2023 | 32K | Open-weight foundation |
| Mixtral 8x7B | Dec 2023 | 64K | MoE; 12.9B active params |
| Mixtral 8x22B | April 2024 | 64K | MoE; 39B active params |
| Mistral Large 2 | July 2024 | 128K | Commercial flagship |
| Mistral Large 3 | Late 2025 | 128K | 675B MoE; 92% of GPT-5.2 at 15% the cost |
| Codestral | 2024 | 256K | 80+ language code specialist |
| Devstral 2 | 2025 | 256K | 123B; 72.2% SWE-bench; top open-weight coding |
| Devstral Small 2 | 2025 | 128K | 24B; runs locally; Apache 2.0 |
| Ministral 3B | Nov 2024 | 128K | Edge/robotics; near-zero latency |
| Ministral 8B | Nov 2024 | 128K | Fast; function calling |
| Pixtral 12B | Sept 2024 | 128K | Multimodal |
| Pixtral Large | Nov 2024 | 128K | Large multimodal |
| Mistral Nemo | 2024 | 128K | Ultra-budget; $0.02/M input |
Mistral Large 3 Highlights: Uses DeepSeek V3 architecture; 675B total MoE parameters; delivers 92% of GPT-5.2 performance at ~15% the cost. Mistral OCR 3: 74% win rate on complex document parsing. Ministral 3B: capable of running on drones and robotics hardware.
Cost: Mistral 7B: free (open-weight); Mistral API: Large 3 ~$2/M input, ~$6/M output; Nemo: ~$0.02/M input, ~$0.06/M output
🔴 DeepSeek Series
Developer: DeepSeek (Hangzhou, China)
Type: MIT license (most models)
DeepSeek shocked the AI world in January 2025 — training a frontier-quality model (V3) for ~$5.58M vs. the $100M–$1B OpenAI/Anthropic spend. This permanently changed pricing expectations industry-wide.
DeepSeek Models Overview
| Model | Released | Context | Specialty |
|---|---|---|---|
| DeepSeek-V3 | Dec 2024 | 128K | General flagship; 671B/37B active MoE |
| DeepSeek-V3.2 | 2025 | 128K | Fine-Grained Sparse Attention; 50% efficiency gain |
| DeepSeek-R1 | Jan 20, 2025 | 128K | Reasoning; pure RL training |
| DeepSeek-R1-0528 | May 2025 | 128K | Updated R1 |
| DeepSeek Coder V2 | 2024 | 128K | 338 languages; MoE coding model |
| DeepSeek-Prover-V2 | 2025 | 128K | Formal theorem proving in Lean 4 |
| R1-Distill series | 2025 | 128K | 1.5B–70B distilled reasoning models |
DeepSeek V4 was widely expected around March 3, 2026, with reports of 1 trillion parameters and native multimodal capabilities. Verify official channels for confirmation.
Strengths:
- Training cost ~98% lower than comparable Western models — permanently disrupted pricing
- MIT license: use commercially, modify, redistribute freely
- DeepSeek-R1: trained with pure reinforcement learning — independently discovered chain-of-thought reasoning; 87.5% on AIME math
- V3.2: first model to integrate “thinking” directly into tool-use (reasoning inside agentic workflows while calling external tools)
- Prover-V2: only major open-source model specialized for formal theorem proving
Weaknesses:
- Chinese ownership: data sovereignty concerns for regulated Western enterprises
- Avoids politically sensitive topics (Tiananmen Square, Chinese government officials)
- Countries including Italy, Denmark, and Czech Republic have banned government agencies from using DeepSeek models over cybersecurity concerns
- DeepSeek’s market share declined from 50% to under 25% by end of 2025 as Chinese competition intensified (Alibaba, Moonshot, ByteDance, MiniMax)
Cost: V3.2: ~$0.28/M input, ~$0.42/M output; cache hits: $0.028/M (90% off); R1: ~$0.55/M input, ~$2.19/M output
🐼 Qwen Series — Alibaba Cloud
Developer: Alibaba Cloud (DAMO Academy)
Type: Apache 2.0 open-weight
The most popular open-weight model family in 2025–2026 by download volume, having overtaken Llama.
Qwen Models Overview
| Model | Released | Context | Key Feature |
|---|---|---|---|
| Qwen 2.5 | Late 2024 | 128K | 0.5B–72B; 18T training tokens; 29+ languages |
| Qwen 2.5-Max | 2025 | 128K | 1T+ parameter MoE; 119 languages |
| Qwen 3 | 2025 | 128K | 4B, 30B, 235B; thinking + non-thinking |
| Qwen3-Next | 2025 | 128K | Frontier MoE; 92.3% on AIME25 |
| Qwen3-Coder | 2025 | 256K | Agentic coding; long-horizon tool use |
| Qwen-VL | 2024–2025 | 128K | Vision-language |
| Qwen-Audio | 2024 | — | Audio processing |
| Qwen3 0.5B–4B | 2025 | 32K | Edge/on-device variants |
Strengths:
- #1 by downloads and community derivatives in open-weight ecosystem (2025)
- Qwen3-Next: 92.3% on AIME25; Qwen2.5-Max: 1T+ MoE, 119 languages
- Adopted by 90,000+ enterprises across consumer electronics, gaming, automotive
- Best multilingual open-weight model family (29+ languages with cultural nuance)
- Qwen3 supports both “thinking” (extended reasoning) and “non-thinking” (fast) modes
Weaknesses: Alibaba Cloud affiliation raises similar data sovereignty questions as DeepSeek for some enterprises
Cost: Free (open-weight); Alibaba Cloud API pricing available; hosted via Groq, Together AI, etc.
🔷 IBM Granite (4.0 Family)
Developer: IBM Research
Type: Apache 2.0 open-source
Latest: Granite 4.0 (2025); Granite 4.0 1B Speech (March 28, 2026)
Strengths:
- Apache 2.0: most permissive license in AI — zero IP ambiguity for commercial use
- Granite 4.0: lightweight; multilingual; coding, RAG, tool use, JSON output natively
- Granite 4.0 1B Speech: compact ASR and speech translation model (March 28, 2026)
- Granite Code: 116 programming languages (3B, 8B, 20B, 34B)
- Granite Guardian: safety/guardrail models (2B–8B)
- Granite Embedding: purpose-built for semantic search and RAG
- Strong compliance story for banking, insurance, government
Best For: Regulated industries needing Apache 2.0 licensing clarity, on-premise deployment, IBM watsonx platform users
Cost: Free (open-source); IBM watsonx API pricing available
🦅 Falcon Series — TII (UAE)
Developer: Technology Innovation Institute (UAE)
Type: Apache 2.0
Released: Falcon 40B: May 2023; Falcon 180B: 2023; Falcon 2: 2024
Once the open-source benchmark leader; now surpassed by Llama and Qwen but historically important. Falcon 2 (11B) includes VLM variant with vision-to-language capability.
Best For: UAE/Middle Eastern government deployments; vision-language tasks at open-weight cost
Weakness: TII’s iteration pace has slowed significantly; Falcon 180B has extreme inference hardware requirements
🪟 Microsoft Phi Series
Developer: Microsoft Research
Type: MIT license
Released: Phi-3.5: April 2024; Phi-4: late 2024; Phi-4 Mini: early 2025
“Small language model” research proving that small models trained on high-quality synthetic data far exceed their size class.
Phi-4 (14B) Strengths: Reasoning benchmarks rival 70B models; strong safety and hallucination avoidance; MIT licensed
Phi-4 Mini (3.8B): 128K context; runs on consumer hardware; great for mobile and education
Best For: Education, mobile AI, resource-constrained devices, consumer hardware deployment
Cost: Free (open-weight); available on Azure
🌍 BLOOM — BigScience
Developer: BigScience Workshop (1,000+ global researchers)
Type: BigScience RAIL license
Released: July 2022 | Parameters: 176B
Supports 46 natural languages and 13 programming languages — the most multilingual open model ever released. Architecture now outdated but critically important for low-resource language research.
🔬 OLMo — Allen Institute for AI
Developer: Allen Institute for AI (AI2)
Type: Fully open-source (Apache 2.0, including training data and code)
Released: 2024 | Parameters: 7B, 65B
The only fully transparent frontier model — releases weights, training data (Dolma), training code, evaluation code, and intermediate checkpoints. Essential for AI safety research and reproducibility.
🟩 NVIDIA Nemotron 3 Super
Released: March 2026
Parameters: 120B total, 12B active (Hybrid Mamba-Transformer MoE)
Type: Open
Context Window: 1,000,000 tokens
Strengths:
- Hybrid Mamba-Transformer MoE architecture: over 50% higher token generation vs. leading open models
- Multi-token prediction (MTP) for faster inference
- 1M context window for long-term agent coherence
- 439 tokens/second — one of the fastest models available (any size)
- Optimized for complex multi-agent applications
Best For: High-throughput agentic applications needing long-context and extreme speed; NVIDIA ecosystem developers
6. Tier 4 — Chinese Frontier Models
China has built a parallel AI ecosystem serving hundreds of millions of users domestically and growing globally. Competition intensified dramatically in 2025: Alibaba, Moonshot, Zhipu, ByteDance, and MiniMax all released major models, eroding DeepSeek’s dominance.
🔴 Baidu ERNIE (文心 4.5)
Developer: Baidu
Type: Proprietary
Users: 200M+ registered users
China’s most-deployed enterprise LLM. Integrated into Baidu Search (dominant Chinese search engine). Superior Chinese NLP; strong on Chinese legal, medical, and business documents.
Weaknesses: Weaker than GPT-5 on English/multilingual; restricted to approved topics under Chinese regulations
Best For: Chinese-language applications, businesses operating in China, Mandarin-first customer service
🟤 Zhipu GLM-5 / ChatGLM
Developer: Zhipu AI (Beijing)
Released: GLM-5: 2025; GLM-5 Turbo: March 2026
Strengths:
- GLM-5 (Reasoning): scores 50 on Intelligence Index — highest-ranked open-weight model globally
- GLM-5 Turbo: optimized for fast inference in agent-driven environments (OpenClaw scenarios); long execution chains, tool use, scheduled and persistent execution
- Strong bilingual Chinese + English performance
- Kimi K2.5 Thinking (related): scores 47 on Intelligence Index
Best For: Bilingual applications, agentic tasks requiring persistent execution, Chinese-first reasoning
🌙 Moonshot Kimi
Developer: Moonshot AI (Beijing)
Type: Proprietary
Strengths:
- Extraordinary long-context capabilities (up to 2M tokens)
- Kimi Linear (October 2025): efficient attention reducing memory usage for large context windows
- OK Computer feature: creates web applications from descriptions
- Kimi K2.5 Thinking: ranks 2nd among open-weight models on Intelligence Index (47)
- Qwen3-Next-based Kimi K2 Thinking: 44.9 on Intelligence Index
Best For: Long document analysis, Chinese market, web application generation
🔷 Baichuan / Yi / Hunyuan / InternLM
Baichuan: Strong Chinese cultural/historical knowledge; BaichuanMed for clinical decision support
Yi (01.AI): Yi-34B was strong open-weight bilingual model; now surpassed by Qwen3 and Llama 4
Hunyuan (Tencent): WeChat/QQ integration; video + image + text generation; Chinese creative content
InternLM (Shanghai AI Lab): Academic orientation; Apache 2.0; strong reasoning and code; InternLM 2.5 (7B, 20B)
📦 ByteDance Seed
Developer: ByteDance
Released: Seed 2.0 Lite and Pro: February 2026
ByteDance’s frontier model family, leveraging TikTok/Douyin ecosystem data. Seed 2.0 Pro is competitive with GPT-4o-class models on coding and reasoning benchmarks. Rapidly gaining adoption in China.
🔢 MiniMax M2.5
Developer: MiniMax
Released: February 2026
Rapidly emerging Chinese lab. M2.5 competitive with frontier models on coding and math. Known for efficient inference architecture and aggressive pricing. Growing developer adoption via API.
7. Tier 5 — Coding-Specialist Models
💻 GitHub Copilot
Developer: GitHub + Microsoft (multi-model backend)
Released: Preview 2021; GA 2022
Users: 20 million (July 2025; 400% YoY growth); 90% of Fortune 100
Now multi-model: users can choose GPT-5.4, Claude Opus/Sonnet 4.6, Gemini 3.1 Pro, or auto-selection. Agent mode handles autonomous multi-file development. Deep IDE integration (VS Code, JetBrains, Neovim, Xcode).
Cost: Free (limited, 2,000 completions/month); Pro: $10/month; Pro+: $39/month; Business: $19/user/month; Enterprise: $39/user/month
Real-World Use: BNY Mellon (80%+ devs daily); 20M developers globally; 90% Fortune 100
🤖 DeepSeek Coder V2 / Prover-V2
Coder V2: 236B MoE total / ~21B active; 338 programming languages; 128K context; near GPT-4 Turbo coding quality at DeepSeek pricing
Prover-V2: Open-source; only major model specialized for formal theorem proving in Lean 4 — significant for mathematics and formal verification communities
⭐ StarCoder2
Developer: BigCode (HuggingFace + ServiceNow)
Released: February 2024 | Sizes: 3B, 7B, 15B
Trained on The Stack v2 (619 programming languages). Fill-in-the-Middle capability. StarCoder2-15B rivals CodeLlama 34B. OpenRAIL-M license.
🦙 CodeLlama
Developer: Meta | Released: August 2023 | Sizes: 7B, 13B, 34B, 70B
Llama 2-based code model. Fill-in-the-Middle. 70B version approaches GPT-4 on coding benchmarks.
🌊 Codestral / Devstral 2 (Mistral)
Codestral: 80+ languages; fast code completion; 256K context
Devstral 2: 123B parameters; 72.2% on SWE-bench Verified — top open-weight coding model as of 2026
Devstral Small 2: 24B; runs locally on consumer hardware; Apache 2.0
🛒 Amazon Q Developer / Tabnine
Amazon Q Developer: Deep AWS service knowledge; ideal for developers in the AWS ecosystem
Tabnine: On-premise deployment; zero code leaves the organization — critical for IP-sensitive codebases at banks, defense contractors, law firms. Enterprise: custom pricing
8. Tier 6 — Domain-Specific Models
🏥 Healthcare LLMs
Med-PaLM 2 / MedLM (Google): First LLM at expert-level USMLE accuracy (85%+). MedLM deployed in multiple U.S. hospital systems for clinical documentation, triage, and diagnostic support. HIPAA-compliant via Google Cloud BAAs.
BioMedLM (Stanford CRFM): Trained on PubMed; strong biomedical NER, relation extraction, and QA.
ClinicalBERT: Fine-tuned BERT on MIMIC-III clinical notes. Still widely used in healthcare informatics for ICD coding, clinical NER, adverse event detection.
Real-World: Hospital reduced patient triage times by 34% using a domain-specific SLM trained on internal case data.
💰 Finance LLMs
BloombergGPT: 50B parameters; trained on 363B tokens of Bloomberg financial data. Cutting error rates by 30%+ vs. general LLMs. Integrated into investment platforms. Proprietary — Bloomberg products only.
FinGPT (AI4Finance Foundation): Open-source foundation for fintech. Fine-tunable on proprietary data. Sentiment analysis, stock prediction, financial QA.
Real-World: 60%+ of major North American financial institutions running pilots or production financial LLM systems. JPMorgan COIN platform reviews loan agreements using domain-trained models.
⚖️ Legal LLMs
Harvey AI: Fine-tuned GPT-4/5 for legal workflows. BigLaw Bench score 91% (GPT-5.4). Integrates with Westlaw and LexisNexis.
CoCounsel (Thomson Reuters / Casetext): GPT-4 powered; native Westlaw integration. Top legal AI benchmarks alongside Harvey.
ChatLAW: Research model trained on legal corpora; 40% faster legal research times in studies.
Real-World: 45%+ of AmLaw 200 firms exploring or deploying legal AI tools in 2025.
🔬 Science / Security
Galactica (Meta, 2022): Trained on scientific papers — withdrew after 3 days due to confident hallucinations. A cautionary tale about domain LLM risk.
SciGLM: Chinese academic model for cross-domain scientific reasoning (chemistry, biology, physics, math).
Cybersecurity: Microsoft Security Copilot (GPT-4 + Microsoft Sentinel); CrowdStrike Falcon AI; Snyk AI (code security). No single dominant open cybersecurity LLM — most serious deployments use frontier models with security-specific RAG pipelines.
9. Tier 7 — Edge / On-Device / Small Models
| Model | Developer | Params | Context | License |
|---|---|---|---|---|
| Phi-4 Mini | Microsoft | 3.8B | 128K | MIT |
| Gemma 3 1B | 1B | 32K | Open | |
| Gemma 3 4B | 4B | 128K | Open | |
| Llama 3.2 1B | Meta | 1B | 128K | Meta |
| Llama 3.2 3B | Meta | 3B | 128K | Meta |
| MiniCPM 3B | ModelBest/Tsinghua | 3B | 32K | Open |
| Qwen3 0.5B–4B | Alibaba | 0.5–4B | 32K | Apache 2.0 |
| Ministral 3B | Mistral | 3B | 128K | Open |
| Apple on-device | Apple | Private | — | Proprietary |
Apple FastVLM (CVPR 2025): FastViTHD encoder reduces image encoding latency while generating 4x fewer tokens. All processing stays on-device. iOS 18+ AI features use on-device LLMs for privacy-first inference. Weights not publicly released.
Key pattern: Phi-4 Mini and Gemma 3 4B are the current leaders for on-device/consumer hardware deployment — MIT/Apache licensed, strong reasoning despite small size.
10. Tier 8 — Research & Historical Models
These models are largely deprecated for production use but historically important and still referenced in research.
| Model | Developer | Year | Significance |
|---|---|---|---|
| GPT-1 | OpenAI | 2018 | First GPT; proved unsupervised pre-training |
| BERT | 2018 | Bidirectional transformer; dominated NLP for years | |
| GPT-2 (1.5B) | OpenAI | 2019 | ”Too dangerous to release” — now fully open |
| XLNet | CMU + Google | 2019 | Permutation-based training; beat BERT on 20 tasks |
| RoBERTa | Facebook AI | 2019 | Improved BERT training methodology |
| GPT-3 (175B) | OpenAI | 2020 | Changed the field; first practical few-shot learning |
| T5 / FLAN-T5 | 2020/2022 | Unified text-to-text framing | |
| Megatron-Turing NLG (530B) | MS + NVIDIA | 2021 | Largest model at release; proved distributed training |
| Gopher (280B) | DeepMind | 2021 | Strong knowledge tasks |
| LaMDA | Google Brain | 2021 | Dialogue-focused; became Bard then Gemini |
| ERNIE 3.0 Titan | Baidu | 2021 | 260B; Chinese knowledge pre-training |
| WuDao 2.0 | BAAI/CAS | 2021 | 1.75T params; multilingual; largest announced model |
| Chinchilla (70B) | DeepMind | 2022 | Proved smaller models + more data beat larger models on less data — “Chinchilla scaling laws” changed how the entire industry trains |
| GPT-NeoX (20B) | EleutherAI | 2022 | Largest open model before LLaMA |
| GPT-J (6B) | EleutherAI | 2021 | First widely-used open GPT-3 alternative |
| BLOOM (176B) | BigScience | 2022 | 46 languages; global collaborative model |
| PaLM (540B) | 2022 | Google’s dominant research model before Gemini | |
| InstructGPT | OpenAI | 2022 | RLHF pioneer; led to ChatGPT |
| ChatGPT (GPT-3.5) | OpenAI | Nov 2022 | Made LLMs a consumer product; deprecated 2025 |
| GPT-4 | OpenAI | March 2023 | Multi-year benchmark leader; now deprecated |
| Alpaca | Stanford | 2023 | LLaMA fine-tuned on GPT-3.5 data for $600 — proved instruction tuning works |
| Vicuna | LMSYS | 2023 | LLaMA fine-tuned on ChatGPT conversations |
| MPT-7B | MosaicML | 2023 | FlashAttention + ALiBi; foundation for DBRX |
| Falcon 180B | TII | 2023 | Held open-source lead for months; Apache 2.0 |
| SOLAR 10.7B | Upstage | 2023 | ”Depth Upscaling” to merge two 7B models; beat GPT-3.5 |
| Galactica | Meta | 2022 | Scientific LLM; withdrawn after 3 days |
| PaLM 2 | 2023 | Powered Bard; PaLM API deprecated Oct 2024 | |
| DBRX | Databricks | March 2024 | 132B MoE; Apache 2.0; strong at launch |
| Cerebras-GPT | Cerebras | 2023 | Trained on wafer-scale cluster |
| DistilBERT | HuggingFace | 2019 | 97% of BERT at 40% size; still used in prod |
Pricing Comparison Table (March 28, 2026)
All prices in USD per million tokens (Input / Output). Verified against official provider documentation as of March 28, 2026. Prices change frequently — always confirm on provider pricing pages before budgeting.
Proprietary Models
| Model | Input ($/M) | Output ($/M) | Context | Notes |
|---|---|---|---|---|
| Mistral Nemo | $0.02 | $0.06 | 128K | |
| Nova Micro (AWS) | $0.035 | $0.14 | 128K | |
| GPT-5 Nano | $0.05 | $0.40 | 128K | |
| Gemini 2.0 Flash-Lite | $0.075 | $0.30 | 128K | |
| Grok 4.1 Fast | $0.12 | $0.30 | 2M | |
| Gemini 3.1 Flash-Lite | $0.25 | $1.00 | 1M | |
| GPT-5 Mini | $0.25 | $2.00 | 128K | |
| Gemini 2.5 Flash | $0.30 | $2.50 | 1M | |
| Gemini 3 Flash | $0.50 | $3.00 | 128K | |
| Claude Haiku 4.5 | $1.00 | $5.00 | 200K | |
| GPT-5 | $1.25 | $10.00 | 400K | |
| Gemini 2.5 Pro | $1.25 | $10.00 | 1M | |
| Grok 4.1 | $3.00 | $15.00 | 256K | |
| GPT-5.2 | $1.75 | $14.00 | 400K | |
| Gemini 3.1 Pro | $2.00 | $12–18 | 1M | |
| Mistral Large 3 | $2.00 | $6.00 | 128K | |
| GPT-5.4 | $2.50 | $10.00 | 1M (API) | |
| Claude Sonnet 4.6 | $3.00 | $15.00 | 1M | |
| Claude Opus 4.6 | $5.00 | $25.00 | 1M | |
| GPT-5.4 Pro | Contact sales | — | 272K | Enterprise/Pro tier |
Open-Weight Models (Self-Hosted = Free; Hosted Pricing Below)
| Model | Hosted Input ($/M) | Hosted Output ($/M) | Context | License |
|---|---|---|---|---|
| DeepSeek V3.2 | $0.14–0.28 | $0.28–0.42 | 128K | MIT |
| Llama 4 Scout | ~$0.10 | ~$0.40 | 10M | Meta |
| Qwen3 32B | ~$0.15 | ~$0.60 | 128K | Apache 2.0 |
| Llama 4 Maverick | ~$0.15 | ~$0.60 | 1M | Meta |
| Gemma 3 27B | ~$0.20 | ~$0.20 | 128K | |
| Mistral 7B | ~$0.25 | ~$0.75 | 128K | Apache 2.0 |
| DeepSeek R1 | $0.55 | $2.19 | 128K | MIT |
| Mixtral 8x7B | ~$0.65 | ~$0.65 | 32K | Apache 2.0 |
| GPT-OSS 120B | ~$0.90 | ~$0.90 | 128K | Apache 2.0 |
| IBM Granite 4.0 | Free on watsonx | — | 128K | Apache 2.0 |
| DeepSeek V4 | TBD (beta) | TBD | TBD | MIT (confirmed) |
Cost Optimization Strategies
- Prompt caching: Up to 90% savings on repeated context — now supported by Anthropic, OpenAI, Google, and xAI
- Batch API: 50% discount for async, non-latency-sensitive workloads (OpenAI, Anthropic, Google)
- Tiered model routing: Budget model (Gemini Flash-Lite / Haiku 4.5) for triage and classification → mid-tier (Sonnet 4.6 / Grok 4.1) for generation → flagship (GPT-5.4 / Opus 4.6) only for high-stakes reasoning; can reduce costs 60–85% vs. using flagship for everything
- Quantization on open models: 4-bit quantization reduces compute ~60–70% with minimal quality degradation on Llama 4 and Qwen3 family; GGUF format now well-supported across llama.cpp and Ollama
- DeepSeek cache hits: DeepSeek V3.2 cache pricing at $0.028/M (90% off base) — strong for repetitive retrieval-augmented workloads
- Devstral 2 for coding pipelines: At competitive hosted pricing, offers strong open-weight coding quality (72%+ SWE-bench range) with Apache 2.0 license for self-hosted deployments
Benchmark Comparison (March 28, 2026)
Key Benchmarks Explained
| Benchmark | What It Measures |
|---|---|
| AIME 2025 | Hard math competition problems — primary reasoning/math gold standard |
| SWE-bench Verified | Real GitHub issue resolution — most practical coding benchmark |
| HumanEval | Basic function-level code generation; largely saturated at frontier |
| GPQA Diamond | Doctoral-level science questions across biology, chemistry, physics |
| ARC-AGI-2 | Novel pattern reasoning explicitly designed to resist memorization |
| OSWorld | Computer use — can the model autonomously operate a real desktop |
| LMArena Elo | Human preference ranking via blind side-by-side comparisons |
| Finance Agent | Agentic financial analysis tasks across real-world scenarios |
| BigLaw Bench | Legal document analysis, contract review, transactional structuring |
| GDPval | Knowledge work tasks across professional domains (law, finance, medicine) |
| Aider Polyglot | Multi-language code editing across real repositories |
| MMMU | Multimodal understanding — images, charts, scientific figures |
Benchmark Snapshot (March 28, 2026)
| Model | AIME 2025 | SWE-bench Verified | OSWorld | LMArena Elo | Notes |
|---|---|---|---|---|---|
| Gemini 3.1 Pro | 100% (w/code) | — | — | Top tier | Current Google frontier flagship |
| Llama 4 Behemoth | 96.2%* | — | — | — | *Reported in tech report; weights unreleased |
| GPT-5.4 | — | ~80% | Record | Top tier | BigLaw Bench 91%; GDPval 83% |
| DeepSeek V4 | ~95%* | ~78%* | — | — | *Early leak; unverified; beta access only |
| Qwen3-Next | 92.3% | — | — | — | Strongest publicly available open-weight math |
| GPT-5.2 | 100% | — | — | — | Previous OpenAI flagship; still available |
| Grok 4.20 | — | — | — | — | 4-agent architecture |
| Grok 4.1 | — | — | — | 1483 Elo (#1*) | *Position may shift post-price/access changes |
| Claude Opus 4.6 | — | — | 61.4% | ~91.3 II | Finance Agent #1; 14.5hr task horizon |
| Claude Sonnet 4.6 | — | 77.2% | — | ~89.9 II | |
| DeepSeek R1 | 87.5% | — | — | — | Landmark pure-RL trained reasoning model |
| Devstral 2 | — | 72.2% | — | — | Top confirmed open-weight coding |
| GLM-5 | — | — | — | 50 II | Highest open-weight Intelligence Index |
| Gemini 3.1 Flash-Lite | — | — | — | 1432 Elo | Budget model; beats prior-gen flagships |
| NVIDIA Nemotron 3 Super | — | — | — | — | 439 tokens/sec; speed-optimized |
| Llama 4 Maverick | — | ~65% | — | — | Top open-weight gen model at accessible cost |
II = Intelligence Index score. Asterisked scores () are from developer-reported or early/leaked evaluations — treat as directional until third-party replication.*
Benchmark Notes for This Week
- DeepSeek V4 leaked scores are circulating but should be treated with caution until independent reproduction. If confirmed, V4 would be the highest-scoring openly licensed model ever on AIME 2025 and SWE-bench Verified simultaneously.
- Llama 4 Behemoth’s 96.2% AIME 2025 from the technical report is reproducible against the paper’s test set but does not correspond to publicly available model weights. Meta’s safety evaluation timeline is unspecified.
- LMArena Elo rankings shift regularly as new models enter the arena and usage patterns change. Treat Elo positions as directional, not definitive.
13. Choosing the Right LLM: Decision Framework
Step 1: Define Your Primary Workload
| Workload | Top Picks (March 28, 2026) |
|---|---|
| Complex reasoning / math | GPT-5.4, Gemini 3.1 Pro, Claude Opus 4.6, DeepSeek R1 |
| Long document analysis | Claude Opus 4.6 (1M), Gemini 3.1 Pro (1M), GPT-5.4 API (1M) |
| Agentic coding | Claude Sonnet/Opus 4.6, GPT-5.4, Devstral 2 |
| Code completion (IDE) | GitHub Copilot, Codestral, StarCoder2 |
| Real-time conversation | GPT-5 Mini, Grok 4.20, Claude Haiku 4.5, Gemini 3 Flash |
| RAG / document Q&A | Cohere Command R+, Claude Sonnet 4.6, GPT-5 |
| Multilingual | Qwen3 (119 langs), Qwen2.5-Max, Mistral Large 3, BLOOM |
| High-volume, budget | DeepSeek V3.2, Gemini 3.1 Flash-Lite, Mistral Nemo |
| Self-hosted / air-gapped | Llama 4, Qwen3, Mistral Large 3, GPT-OSS 120B |
| Medical | MedLM (Google Cloud), BioMedLM + RAG, Med-PaLM 2 |
| Legal | Harvey AI, CoCounsel, GPT-5.4 (BigLaw Bench 91%) |
| Financial | Bloomberg GPT, Claude Opus 4.6 (Finance Agent #1) |
| On-device / edge | Phi-4 Mini, Gemma 3 1B–4B, Llama 3.2 1B–3B, Qwen3 0.5B–4B |
| Chinese language | ERNIE 4.5, Qwen3, GLM-5, Moonshot Kimi, ByteDance Seed |
| Maximum compliance | Claude Enterprise, GitHub Copilot Enterprise, IBM Granite, Azure OpenAI |
| Formal theorem proving | DeepSeek-Prover-V2 |
| Computer use / GUI agents | GPT-5.4 (native), Claude 4.6, Gemini 3.1 Pro |
| Real-time social/web data | Grok 4.20, Perplexity Sonar |
Step 2: Assess Constraints
| Constraint | Recommendation |
|---|---|
| Data sovereignty (data can’t leave country) | Self-hosted open-weight, or regional cloud (Azure EU, Google EU) |
| HIPAA/SOC 2/GDPR required | Azure OpenAI, Google Vertex AI, Claude Enterprise, AWS Bedrock |
| Budget (high volume) | DeepSeek V3.2, Gemini 3.1 Flash-Lite, Mistral Nemo, GPT-5 Nano |
| Real-time latency (<1s) | Gemini Flash-Lite, Claude Haiku, Grok 4.1 Fast, Ministral 3B |
| Need fine-tuning control | Open-weight: Llama 4, Qwen3, Mistral, GPT-OSS |
| IP clarity for commercial use | Apache 2.0 only: IBM Granite, Phi-4, Qwen3, Mistral, OLMo |
| Reasoning depth over speed | o3, Claude Opus 4.6, DeepSeek R1, Gemini 3.1 Pro Deep Think |
Step 3: Run Your Own Evaluation
Don’t rely solely on public benchmarks:
- Create 10–20 prompts from your actual production queries
- Score on: accuracy, format compliance, latency, and cost per correct answer
- Re-run monthly — model catalogs change every 2–3 weeks
14. Real-World Enterprise Success Stories
OpenAI / GPT
- Morgan Stanley: AI research assistant saves financial advisors 10–15 hours/week; GPT-5.4 used for investment banking document workflows (87.3% preference rate)
- Duolingo Max: GPT-4/5 powers “Explain My Answer” and conversation practice for 30M+ learners
- Khan Academy (Khanmigo): Socratic AI tutor across all K-12 subjects
- GitHub Copilot: 20M developers globally; 90% Fortune 100; BNY Mellon: “part of our DNA”
Anthropic / Claude
- NASA: Claude Code planned a ~400m route for Mars rover Perseverance (December 2025)
- Norway Sovereign Wealth Fund ($2.2T): Claude screens entire portfolio for ESG risks — earlier divestments, improved monitoring of forced labour and corruption (February 2026)
- Firefox audit: Claude found 100+ bugs in Firefox in two weeks; 14 high-severity (2026)
- Notion AI, Quora Poe: Major consumer integrations for writing and Q&A
Google / Gemini
- Google Workspace: Hundreds of millions of Docs/Sheets/Gmail users access Gemini AI Assist
- Hospital systems: MedLM deployed for clinical documentation at multiple U.S. health systems
- Gemini in Chrome: Rolled out to Canada, New Zealand, India with 50+ language support (March 28, 2026)
Microsoft / Copilot
- BNY Mellon: 80%+ of developers use GitHub Copilot daily — “part of our DNA”
- DNV (shipping/maritime): Azure OpenAI reduced compliance analysis effort by 90%
- DoozyTemps: Copilot customer service bot reduced call volume by 60%
- New Zealand power utility: Copilot planning system halved required project staff
DeepSeek
- Global startups: Hundreds switched after January 2025 announcement, cutting API costs 80–95%
- Academic research: R1’s pure RL training approach widely studied and reproduced
Finance / Legal
- BloombergGPT: 30%+ error rate reduction on financial tasks vs. general LLMs; integrated into investment platforms
- JPMorgan COIN: Domain-trained LLM reviews commercial loan agreements
- AmLaw 200 firms: 45%+ exploring or deploying legal AI tools in 2025
- Global bank: 27% AML compliance cost reduction using SLM trained on transaction patterns
Trends and What’s Coming in 2026–2027
1. The Inference Efficiency Race Is Now the Main Event
The frontier capability gap between top labs is narrowing — but the cost-per-useful-output gap is widening as a competitive axis. DeepSeek V4’s MIT license and the steady drumbeat of open-weight models reaching frontier-adjacent benchmarks are all moves in the same direction: commoditizing intelligence at the token level. The next 12 months will be defined less by “which model scores highest” and more by “which model delivers the best quality per dollar at scale.” Labs that can’t win the inference efficiency arms race will cede enterprise deals to those that can.
2. Memory Is the Next Frontier — Working, Episodic, and Persistent
The dominant architecture of 2025 was long context. The dominant architecture of 2026 is structured memory. Ongoing work at OpenAI on persistent memory in ChatGPT and Anthropic’s investments in long-horizon agentic tasks all point to the same conclusion: raw context window size is no longer the differentiator — what the model remembers across sessions and tasks is. Expect every major lab to ship explicit memory management APIs by Q3 2026. RAG is not dead, but it is being supplemented by model-native episodic memory systems that update dynamically rather than requiring retrieval.
3. Open-Weight Models Are Reaching Undeniable Frontier Parity
With Llama 4 Behemoth scoring 96.2% AIME 2025 in its technical report, DeepSeek V4 potentially clearing similar bars, and Qwen3-Next already at 92.3%, the argument that open-weight models are “one generation behind” proprietary models is no longer defensible on reasoning benchmarks. The remaining gaps — computer use, deep multimodal integration, long-horizon agentic reliability — are narrowing quickly. Organizations that locked themselves into proprietary-only pipelines for capability reasons should re-evaluate; the cost, auditability, and data-privacy advantages of self-hosted open models now come with far fewer performance tradeoffs.
4. Video Understanding Becomes a Tier-1 Capability
Gemini 3.1 Pro’s native long-video context is the clearest signal yet that video is graduating from a demo feature to a production-grade capability. In 2025, video understanding meant “describe this 30-second clip.” In late 2026, it will mean “analyze this earnings call,” “review this surgical procedure,” and “audit this security footage.” DeepSeek V4’s multimodal scope includes video. Expect every major frontier lab to ship or expand video understanding capabilities before Q4 2026.
5. Agentic Reliability — Not Capability — Is the Bottleneck
Every major lab now ships computer use. Every major lab has a coding agent. The problem is not “can the model do agentic tasks” — it’s “can the model do agentic tasks without failing at step 17 of 23.” Claude Opus 4.6’s 14.5-hour task horizon is the current best-in-class reliability number. Real enterprise deployments are gating on error recovery, graceful degradation, and audit trails — not on whether the model can click a button. The next competitive moat will be built by the lab that ships the most reliable agentic runtime, not the most capable one.
6. Specialized Legal and Financial Models Are Being Displaced by Generalists
Twelve months ago, the assumption was that specialized vertical models (Harvey for legal, BloombergGPT for finance) would maintain durable advantages over generalists. GPT-5.4’s 91% BigLaw Bench score and Finance Agent #1 ranking for Claude Opus 4.6 are eroding that assumption at the frontier. The pattern is: specialized models lead, generalists close the gap, specialized models differentiate on workflow integration and fine-tuning rather than base model capability. Watch for Harvey, Bloomberg, and others to lean harder into RAG-over-proprietary-data and workflow embedding rather than competing on raw model quality.
7. China’s AI Export Tension Is Creating a Bifurcated Market
DeepSeek V4’s MIT license is simultaneously its greatest strength (global developer adoption) and its greatest geopolitical vulnerability. U.S. Commerce Department discussions about licensing requirements for large model weights from Chinese developers are ongoing. Italy, Denmark, and Czech Republic have already restricted government use of DeepSeek. If export-control logic is applied to model weights the way it has been applied to chips, MIT licensing won’t protect deployment in regulated Western enterprises. The result will be a bifurcated market: Chinese open-weight models dominant in Asia, Southeast Asia, and the Global South; U.S./EU models dominant in regulated Western sectors. This creates a durable opportunity for Mistral (EU-based, Apache 2.0) as a “trusted open” alternative.
8. The GUI Layer Is the New Interface Competition
With computer use now shipping from OpenAI (ChatGPT desktop), Anthropic (Claude Computer Use), and Google (Project Mariner), the competition has shifted from “which model has the best API” to “which model has the best agentic desktop experience.” OpenAI’s macOS ChatGPT computer use launch this week is a direct shot at Anthropic’s first-mover advantage in this space. By Q4 2026, computer use will be table stakes — the differentiator will be how well the agent handles ambiguous instructions, recovers from errors, and explains its actions to non-technical users. UI/UX of agentic systems is now a first-class product problem, not an infrastructure one.
9. Multimodal Input Is Consolidating; Multimodal Output Is the Next Gap
Every frontier model now accepts text + images. Most accept PDFs, audio, and short video. The input side of multimodality is largely solved at the flagship tier. The output side — native generation of images, audio, video, and code in a unified model without stitching together separate APIs — remains fragmented. GPT-5.4’s image generation still routes through DALL-E infrastructure. Gemini’s video generation is Veo, a separate model. Grok’s image generation is Grok Imagine, a separate pipeline. The lab that ships genuinely unified multimodal output — text, code, image, and audio from a single forward pass — will have a defensible product advantage for at least two quarters.
10. Enterprise AI Governance Is Now a Procurement Blocker
The EU AI Act is in force. UK ICO and Ireland DPC investigations into xAI are proceeding. Several large European enterprises have begun requiring ISO 42001 compliance or equivalent AI governance certifications as a condition of LLM procurement. Anthropic’s public refusal of certain U.S. DoD requests and OpenAI’s enterprise data residency options are both responses to this pressure. By end of 2026, expect AI governance certifications (audit trails, explainability documentation, data residency guarantees) to be as standard in enterprise RFPs as SOC 2 Type II is today. Labs that have invested in compliance infrastructure early — Anthropic and Google lead here — will have a structural advantage in regulated sectors.
Quick Reference: Who Makes What (March 28, 2026)
| Organization | Latest Models | Notes |
|---|---|---|
| OpenAI | GPT-5.4, GPT-5.4 Pro, GPT-5.3 Codex, GPT-5.2, GPT-5 Mini/Nano, o3, GPT-OSS 20B/120B | GPT-5.4 computer use now in ChatGPT desktop (macOS) |
| Anthropic | Claude Opus 4.6, Claude Sonnet 4.6, Claude Haiku 4.5 | Sonnet 4.6 is current recommended tier |
| Google DeepMind | Gemini 3.1 Pro, 3.1 Flash-Lite, 3 Flash, 2.5 Pro/Flash, Gemma 3, Gemini Embedding 2 | |
| xAI | Grok 4.20, Grok 4.1, Grok Code Fast 1, Grok Voice, Grok Imagine | |
| Meta AI | Llama 4 Scout, Llama 4 Maverick, Llama 4 Behemoth (tech report only), Llama 3.3, CodeLlama | Behemoth weights unreleased; 96.2% AIME 2025 in paper |
| Mistral AI | Mistral Large 3, Devstral 2, Mixtral 8x22B, Ministral 3B/8B, Pixtral Large, Mistral Nemo | |
| DeepSeek | DeepSeek V4 (limited beta), DeepSeek V3.2, R1, Coder V2, Prover-V2, R1-Distill series | V4 formally announced; MIT license; public release ~mid-April |
| Alibaba Cloud | Qwen3-Next, Qwen3 (4B–235B), Qwen2.5-Max, Qwen-VL, Qwen-Audio | Qwen3-Next remains strongest public open-weight on AIME |
| Microsoft Research | Phi-4, Phi-4 Mini | Phi-5 rumored for H1 2026 |
| IBM Research | Granite 4.0, Granite 4.0 1B Speech, Granite 3.3 | Apache 2.0; free on watsonx |
| NVIDIA | Nemotron 3 Super 120B | 439 tokens/sec; inference-optimized |
| TII (UAE) | Falcon 2 (11B, VLM) | |
| BigCode | StarCoder2 | |
| Allen Institute | OLMo | |
| Baidu | ERNIE 4.5 | |
| Zhipu AI | GLM-5, GLM-5 Turbo, ChatGLM | Highest open-weight Intelligence Index score |
| Moonshot AI | Kimi family, Kimi K2.5 Thinking | |
| ByteDance | Seed 2.0 Lite / Pro | |
| MiniMax | M2.5 | |
| 01.AI | Yi series | |
| Tencent | Hunyuan | |
| Shanghai AI Lab | InternLM 2.5 | |
| Cohere | Command R+ | |
| Amazon | Nova Micro / Lite / Pro (via Bedrock) | |
| Perplexity | Sonar, Sonar Pro, Sonar Reasoning | |
| Harvey | Harvey (legal specialist) | |
| GitHub / Microsoft | GitHub Copilot (multi-model: GPT-5.4, Claude, Gemini) | |
| Bloomberg | BloombergGPT | Finance-domain specialist |
| BigScience | BLOOM |
Models listed reflect announced and available releases as of March 28, 2026. Beta / limited-access models noted where applicable. “Latest Models” lists current-generation releases; legacy models omitted for brevity.
Useful Resources
| Resource | URL |
|---|---|
| Live pricing (300+ models) | pricepertoken.com |
| Benchmark leaderboard | artificialanalysis.ai/leaderboards/models |
| Open model leaderboard | huggingface.co/spaces/open-llm-leaderboard |
| Real-time model releases | llm-stats.com |
| Wikipedia model list | en.wikipedia.org/wiki/List_of_large_language_models |
| OpenAI API pricing | platform.openai.com/docs/pricing |
| Anthropic API docs | platform.claude.com/docs/en/about-claude/models/overview |
| Google Gemini API | ai.google.dev/gemini-api/docs/models |
| Mistral API | mistral.ai/technology |
Last verified: March 28, 2026. The LLM landscape changes every 2–3 weeks — treat all version numbers and pricing as starting points, not gospel. Always verify against official provider documentation before production deployment.
16. Use Case Directory — Which Model for Which Software Task
This section maps real-world software development and product use cases to the best available models as of March 2026. Each use case includes a primary pick, budget alternative, open-weight alternative, and key reasoning for the recommendation.
🤖 Conversational Chatbots & Customer Support
What you’re building: Customer service bots, help desk automation, FAQ agents, onboarding assistants, internal IT support.
Requirements: Fast responses, multi-turn memory, graceful handling of off-topic queries, tone consistency, escalation awareness.
| Tier | Model | Why |
|---|---|---|
| Best overall | Claude Sonnet 4.6 | Best instruction following; least likely to go off-script; Constitutional AI keeps tone professional |
| Fastest/cheapest | Claude Haiku 4.5 or Gemini 3.1 Flash-Lite | Sub-second latency; handles routine queries; <$1.50/M output |
| Open-weight | Llama 4 Maverick or Mistral Large 3 | Self-hostable; fine-tuneable on your support KB |
| RAG-heavy support | Cohere Command R+ | Purpose-built for retrieving from support databases; multilingual |
Key decision point: If your support volume is high (millions of tickets), DeepSeek V3.2 at $0.42/M output with a smarter fallback model for complex tickets is the most cost-effective architecture.
Avoid: o3, Claude Opus, GPT-5.4 Pro for this use case — their reasoning depth is wasted on routine support and the cost-per-ticket becomes unjustifiable.
💻 Code Generation & Autocomplete
What you’re building: IDE plugins, code completion tools, inline code suggestions, boilerplate generation.
Requirements: Low latency (<200ms for feel-good UX), high acceptance rate, language breadth, context awareness across open files.
| Tier | Model | Why |
|---|---|---|
| Turnkey solution | GitHub Copilot (multi-model) | Handles infrastructure; multi-model; 20M devs already use it |
| Best raw model | Claude Sonnet 4.6 | Highest SWE-bench scores for instruction-following code generation |
| Fastest | Codestral (Mistral) | Optimized for low-latency completions; 80+ languages; 256K context |
| Open-weight | Qwen3-Coder or StarCoder2-15B | Free; strong on code; deployable locally |
| Budget API | DeepSeek Coder V2 | 338 languages; near-GPT-4 quality; $0.42/M output |
Key decision point: For IDE autocomplete where latency is everything, Codestral and StarCoder2 are purpose-built for fill-in-the-middle (FIM) tasks. For agentic multi-file generation, Claude Sonnet 4.6 or GPT-5.4 win on quality.
🧑💻 Agentic Coding / Software Engineering Agents
What you’re building: Autonomous coding agents that can read a codebase, implement features, fix bugs, open PRs, run tests, and iterate without human in the loop.
Requirements: Long context (entire codebase), multi-step reasoning, tool use (file read/write, shell exec, web search), recovery from failed steps, sustained context over long sessions.
| Tier | Model | Why |
|---|---|---|
| Best overall | Claude Opus 4.6 (via Claude Code) | 14.5hr task horizon; #1 SWE-bench; 1M context; considered best AI coding assistant Jan 2026 |
| Runner-up | GPT-5.4 | Native computer use; ~80% SWE-bench; 1M context in API; strong at tool-heavy workflows |
| Best open-weight | Devstral 2 | 72.2% SWE-bench; 123B MoE; 256K context; top open-weight coding model |
| Budget open-weight | Devstral Small 2 (24B) | Runs locally; Apache 2.0; solid SWE-bench for size |
Key decision point: If your agent needs to stay focused across a 6+ hour session without losing context, Claude Opus 4.6 is uniquely designed for this. For teams that want to self-host, Devstral 2 is the open-weight equivalent.
📄 Document Analysis & Summarization
What you’re building: Contract review, financial report analysis, research paper summarization, compliance document processing, meeting notes, legal brief analysis.
Requirements: Long context (full documents), accurate extraction without hallucination, structured output, citation support.
| Tier | Model | Why |
|---|---|---|
| Largest context | Gemini 3.1 Pro (1M) or Claude Opus 4.6 (1M) | Process entire document archives in one session |
| Best accuracy | Claude Sonnet 4.6 | Lowest hallucination rate; citation support via API |
| Google Workspace users | Gemini 3.1 Pro | Native in Docs/Sheets/Gmail; no integration work |
| Budget | Gemini 2.5 Flash ($0.30/M) or DeepSeek V3.2 | Solid summarization quality at 10–20x lower cost |
| Open-weight | Llama 4 Scout (10M context) | Unprecedented context window; free to self-host |
Key decision point: For documents under 200K tokens, Sonnet 4.6 is the best accuracy/cost trade-off. For entire legal contract databases or codebases in one prompt, Gemini 3.1 Pro or Llama 4 Scout are your only options.
🔍 RAG (Retrieval-Augmented Generation) Systems
What you’re building: Internal knowledge bases, enterprise search, document Q&A, product documentation assistants, customer-facing knowledge bots.
Requirements: Faithfulness to retrieved context (not making things up), citation of sources, multilingual support, structured output for downstream systems.
| Tier | Model | Why |
|---|---|---|
| Best for RAG | Cohere Command R+ | Purpose-built for RAG; trained to ground answers in retrieved docs; 128K context; 10+ languages |
| Best general | Claude Sonnet 4.6 | Citations API; strong at faithfully synthesizing retrieved chunks |
| Google ecosystem | Gemini 3.1 Pro | Native Google Search grounding; Vertex AI RAG pipelines |
| Open-weight | Mixtral 8x22B or Qwen3-32B | Strong at following system prompt instructions; free to self-host |
| Research/transparency | OLMo | Full training data transparency; important for auditable enterprise AI |
Key decision point: If multilingual RAG across 10+ languages is required, Command R+ is the clear winner. For a simpler English-only internal knowledge base, Claude Sonnet 4.6 with citation mode is the most reliable.
🧠 Complex Reasoning & Multi-Step Problem Solving
What you’re building: Automated analysis pipelines, scientific research assistants, financial modeling, algorithmic problem solving, proof generation, strategic planning tools.
Requirements: Deep reasoning, self-correction, structured logical output, tolerance for slow response times in exchange for accuracy.
| Tier | Model | Why |
|---|---|---|
| Best overall | GPT-5.4 Thinking or Gemini 3.1 Pro | State-of-the-art on AIME and reasoning benchmarks |
| Deepest reasoning | Claude Opus 4.6 (extended thinking) | Deliberate self-reflection loops; best for multi-step enterprise analysis |
| Best open-weight | DeepSeek R1 | 87.5% AIME; discovered chain-of-thought via pure RL; MIT licensed |
| Math/proofs | DeepSeek-Prover-V2 | Only major open-source model for formal theorem proving in Lean 4 |
| Multi-agent reasoning | Grok 4.20 | Four-agent parallel architecture; aggregates multiple independent reasoning paths |
| Budget | Qwen3-Next (92.3% AIME25) | Open-weight; frontier reasoning at zero API cost |
Key decision point: If latency doesn’t matter and accuracy is everything, use Claude Opus 4.6 with extended thinking or GPT-5.4 Thinking. If you need this at scale on a budget, DeepSeek R1 hosted via Groq or Together AI is the best cost/accuracy ratio.
🌐 Real-Time Web & Search Applications
What you’re building: News aggregators, competitive intelligence tools, financial data monitors, social listening platforms, research assistants with live data.
Requirements: Real-time web access, citation of sources, recency awareness, speed.
| Tier | Model | Why |
|---|---|---|
| Best for citations | Perplexity Sonar Pro | Every answer cites sources; purpose-built for grounded web answers |
| Best for social data | Grok 4.20 | Native X/Twitter real-time integration; best for social intelligence |
| Google ecosystem | Gemini 3.1 Pro with Search grounding | Grounding with Google Search is native; Workspace integration |
| General web | GPT-5 with web browsing | Strong at synthesis; large ecosystem |
Key decision point: For social media monitoring or anything involving X/Twitter data, Grok 4.20 is uniquely positioned. For academic or news research requiring citations, Perplexity Sonar Pro is purpose-built.
🌍 Multilingual Applications
What you’re building: Global customer support, multilingual content generation, cross-language search, localization pipelines, translation tools.
Requirements: High accuracy in target languages (not just English), cultural nuance beyond literal translation, support for less common languages.
| Tier | Model | Why |
|---|---|---|
| Best breadth | Qwen2.5-Max / Qwen3-Next | 119 languages; genuine cultural nuance; best non-English open model |
| Best for business languages | Cohere Command R+ | Optimized for 10 major business languages; strong multilingual RAG |
| Best coverage (46 languages) | BLOOM | Only model covering many low-resource and regional languages |
| Proprietary | Mistral Large 3 | Strong European language support (FR, DE, IT, ES, PT) |
| Chinese-first | Qwen3 or ERNIE 4.5 | Native Chinese cultural understanding; far outperforms Western models in Chinese |
Key decision point: For European business languages, Mistral Large 3 is optimized and cost-effective. For Asian and global markets at scale, Qwen3 is the dominant choice. For low-resource language research, BLOOM remains uniquely capable.
🔒 Privacy-Critical / Air-Gapped Deployments
What you’re building: Healthcare data processing, legal document handling, defense applications, financial systems with strict data sovereignty, government workloads.
Requirements: Data never leaves your infrastructure, compliance certifications, ability to audit model behavior, fine-tuning on proprietary data.
| Tier | Model | Why |
|---|---|---|
| Best overall | Llama 4 Maverick (self-hosted) | Meta license permits commercial use; strong benchmarks; no API calls |
| Best for regulated industries | IBM Granite 4.0 (Apache 2.0) | Most permissive license; IBM enterprise support; Apache 2.0 = IP clarity |
| Best reasoning | GPT-OSS 120B (Apache 2.0) | OpenAI-quality reasoning; fully self-hostable |
| Best coding | Devstral Small 2 (24B, Apache 2.0) | Strong coding; single GPU deployment |
| Smallest footprint | Phi-4 Mini or Gemma 3 4B | Runs on laptop; MIT/Apache; HIPAA-friendly if deployed privately |
Key decision point: For maximum IP protection, Apache 2.0 licensed models (IBM Granite, Phi-4, GPT-OSS, Qwen3) remove all ambiguity. For maximum capability, Llama 4 or GPT-OSS 120B self-hosted on your own infrastructure.
📊 Data Analysis & Structured Output
What you’re building: Data extraction pipelines, schema-to-JSON converters, report generators, database query generators, ETL automation, spreadsheet AI.
Requirements: Reliable JSON/structured output, function calling, low hallucination on numbers and facts, ability to follow strict schemas.
| Tier | Model | Why |
|---|---|---|
| Best for structured output | Claude Sonnet 4.6 | Structured outputs GA with expanded schema support; strong schema adherence |
| Best for data + spreadsheets | GPT-5.4 (via ChatGPT for Excel add-in) | Native Excel operations; spreadsheet + presentation skills built in as of March 2026 |
| Best for SQL generation | DeepSeek Coder V2 | Outperforms IBM Watson on SQL (73.78% vs 45.6% HumanEval SQL) |
| Best open-weight | Qwen3 or Mistral 7B (fine-tuned) | Function calling native; easy to fine-tune on your schema |
| Budget | DeepSeek V3.2 | Unified chat + structured output; $0.42/M; strong JSON following |
Key decision point: If you need guaranteed JSON schema adherence in production, use structured outputs mode via Anthropic or OpenAI APIs — it uses constrained grammar to guarantee valid output, not just hope.
🖥️ Computer Use / GUI Automation Agents
What you’re building: Browser automation, desktop workflow agents, RPA (robotic process automation) replacements, autonomous research agents, form-filling bots, QA automation.
Requirements: Vision (screenshot understanding), ability to click/type/navigate, multi-step planning, error recovery.
| Tier | Model | Why |
|---|---|---|
| Best overall | GPT-5.4 (Computer Use API) | Native computer use; first mainline model with state-of-the-art GUI control; record on OSWorld-Verified |
| Best for enterprise workflows | Claude Opus 4.6 | 61.4% OSWorld; computer use built in; longest task horizon (14.5hrs) |
| Best for web automation | Gemini 3.1 Pro | Computer use tool native; deep Google ecosystem; auto browse in Chrome |
| Open-weight | (Limited options) | This capability is largely proprietary; GLM-4V and Qwen-VL have partial vision support |
Key decision point: GPT-5.4 is currently the strongest for computer use in the API, particularly for professional document workflows (Excel, PowerPoint, browser). Claude Opus 4.6 is stronger for long-running autonomous tasks where the agent must stay on-task for hours.
🎓 Education & Tutoring Platforms
What you’re building: Personalized tutoring, homework helpers, language learning apps, coding bootcamp assistants, exam prep tools.
Requirements: Age-appropriate responses, Socratic dialogue capability, explanation of reasoning, multiple difficulty levels, safe content generation.
| Tier | Model | Why |
|---|---|---|
| Best overall | GPT-5 or Claude Sonnet 4.6 | Excellent at Socratic dialogue; strong at adjusting complexity |
| Best math/science | Gemini 3.1 Pro (Deep Think) or DeepSeek R1 | Best STEM reasoning; can show step-by-step work |
| Best for young learners | Claude Haiku 4.5 | Constitutional AI = safest content; fast; affordable for per-user billing |
| On-device (offline) | Phi-4 Mini | MIT license; strong reasoning for size; runs on tablets |
| Budget at scale | Gemini 3 Flash or DeepSeek V3.2 | Sub-cent per interaction; viable for free-tier edtech products |
Real-World: Khan Academy uses GPT-4/5 for Khanmigo, Duolingo Max uses GPT for conversation practice. Both demonstrate that GPT-family models set the standard for educational dialogue.
🏥 Healthcare & Clinical Applications
What you’re building: Clinical documentation assistants, diagnostic support tools, patient communication bots, medical record analysis, drug information systems.
Requirements: Accuracy on medical terminology, HIPAA compliance, conservative/safe outputs, ability to cite clinical sources, no hallucinated diagnoses.
| Tier | Model | Why |
|---|---|---|
| Best overall | Google MedLM (Gemini-based) | Expert-level USMLE performance; HIPAA via Google Cloud BAA; deployed in production hospital systems |
| Best for research | BioMedLM (Stanford) | Trained on PubMed; open research weights; strong biomedical NLP |
| Best general model for medical RAG | Claude Sonnet 4.6 | Lowest hallucination rate; citation support; can be deployed on AWS/GCP with HIPAA BAA |
| Structured EHR tasks | ClinicalBERT | ICD coding, NER, adverse event detection in structured clinical notes |
| On-premise (sensitive data) | Llama 4 or IBM Granite (self-hosted) | Data never leaves hospital infrastructure |
Key decision point: For patient-facing applications, never use an unconstrained general model without medical-specific fine-tuning, RAG grounding on clinical guidelines, and a human-in-the-loop review step. Always pair with a HIPAA BAA from your cloud provider.
⚖️ Legal Tech Applications
What you’re building: Contract analysis tools, case law research assistants, due diligence automation, compliance monitoring, legal document drafting aids.
Requirements: Precision on legal terminology, citation of case law and statutes, low hallucination on facts and dates, confidentiality (data residency), audit trail.
| Tier | Model | Why |
|---|---|---|
| Best turnkey | Harvey AI | Purpose-built for BigLaw; BigLaw Bench score 91% with GPT-5.4; Westlaw/LexisNexis integration |
| Best platform | CoCounsel (Thomson Reuters) | Native Westlaw; case law grounding; proven in AmLaw 200 firms |
| Best underlying model | GPT-5.4 | 91% BigLaw Bench; praised specifically for transactional contract analysis |
| Best for long contracts | Claude Opus 4.6 (1M context) | Entire contract portfolio in one session; strong instruction following |
| Open-weight | ChatLAW or Claude/Llama with legal RAG | Research-grade; requires your own legal corpus and citation pipeline |
Key decision point: For large law firms, Harvey or CoCounsel wrap the hard integration work. For legal tech startups building custom products, use GPT-5.4 or Claude Sonnet 4.6 with a Westlaw/LexisNexis RAG pipeline and careful output validation.
💰 Financial Services Applications
What you’re building: Investment research tools, earnings analysis, portfolio risk screening, compliance monitoring, AML (anti-money laundering) systems, financial report generation.
Requirements: Accuracy on numbers, SEC/FINRA/GAAP terminology, no hallucinated financial data, audit trail, data residency compliance.
| Tier | Model | Why |
|---|---|---|
| Best purpose-built | BloombergGPT | Trained on 363B Bloomberg tokens; 30%+ error reduction vs. general LLMs on financial tasks |
| Best general model | Claude Opus 4.6 | #1 on Finance Agent benchmark; strong at financial report synthesis |
| Best for research synthesis | Perplexity Sonar Pro | Cited, real-time financial news synthesis |
| Best open-weight | FinGPT (AI4Finance) | Apache 2.0; fine-tuneable on proprietary financial data |
| For volume/screening | DeepSeek V3.2 or GPT-5 Mini | ESG screening, portfolio flagging at scale — Norway SWF uses Claude for this |
Real-World: Norway’s $2.2T sovereign wealth fund uses Claude to screen its portfolio for ESG risks. JPMorgan COIN uses domain-trained LLMs for loan agreement review. 60%+ of major North American banks have LLM pilots or production deployments.
🔐 Cybersecurity Applications
What you’re building: Threat detection assistants, vulnerability scanning automation, security report generation, SIEM log analysis, penetration testing tools, phishing detection.
Requirements: Understanding of CVEs, MITRE ATT&CK, network protocols; structured output for SIEM integration; low false-positive rate; no generating exploit code.
| Tier | Model | Why |
|---|---|---|
| Best platform | Microsoft Security Copilot | GPT-5.2 + Microsoft Sentinel; enterprise-grade; SIEM integration native |
| Best general model | GPT-5.4 or Claude Sonnet 4.6 | Strong at log analysis, threat narrative generation, policy drafting |
| Best open-weight | Llama 4 or Mixtral (fine-tuned on security data) | Self-hosted; no sensitive log data leaving infrastructure |
| Code security specifically | GitHub Copilot (Enterprise) + Snyk AI | Security scanning built into IDE workflow; real-time vulnerability detection |
Key decision point: For security-sensitive workloads, self-hosted open-weight models are often the only acceptable option — sending network logs or CVE data to a third-party API creates its own attack surface. GPT-5.4 noted its cyber safety systems carefully in its safety evaluation during the March 2026 launch.
🛒 E-Commerce & Personalization
What you’re building: Product description generation, personalized recommendation copy, review summarization, search ranking assistance, customer Q&A bots, visual product search.
Requirements: Fast, cheap per-item processing; multimodal (product images + text); SEO-aware output; brand voice consistency.
| Tier | Model | Why |
|---|---|---|
| Best for volume | Gemini 3.1 Flash-Lite | Demonstrated UI generation; fast; $0.25/M; can generate product listings at scale |
| Best for quality | Claude Sonnet 4.6 | Brand voice consistency; strong instruction following for style guides |
| Best multimodal | Gemini 3.1 Pro or GPT-5.4 | Image + text product understanding; can analyze product photos |
| Best open-weight | Qwen3 or Llama 4 (fine-tuned) | Fine-tune on your product catalog and brand guidelines |
| Cheapest viable | DeepSeek V3.2 | Excellent value for high-volume description generation |
Key decision point: For bulk product description generation (thousands/day), DeepSeek V3.2 or Gemini Flash-Lite at sub-cent per item is the right answer. For homepage/hero copy requiring brand voice precision, invest in Sonnet 4.6.
📱 Mobile & On-Device AI Features
What you’re building: Offline AI assistants, on-device text prediction, local document summarization, privacy-first AI features that run without internet.
Requirements: Runs on device CPU or NPU, <4GB RAM footprint, sub-second inference, no network dependency, private by default.
| Tier | Model | Why |
|---|---|---|
| Best iOS/macOS | Apple on-device models (FastVLM) | Apple silicon optimized; privacy-first; native OS integration |
| Best cross-platform (3.8B) | Phi-4 Mini | MIT license; 128K context; strong reasoning for size; runs on CPU |
| Best for Android/general | Gemma 3 4B | Google-quality; multimodal; runs efficiently on consumer hardware |
| Smallest viable | Gemma 3 1B or Llama 3.2 1B | Smartphone-class hardware; limited but functional |
| Best for coding features | Qwen3 4B | Strong code understanding for IDE plugins on local hardware |
Key decision point: For Apple platforms, Apple’s own on-device models are best-in-class — but weights aren’t public. For cross-platform apps needing strong reasoning in a small package, Phi-4 Mini is the current leader.
🔬 Scientific Research Assistants
What you’re building: Literature review tools, hypothesis generation aids, experimental data analysis, protein structure annotation, genomics pipeline assistants, citation managers.
Requirements: Deep domain accuracy, citation grounding, ability to follow long complex instructions, math and statistics capability.
| Tier | Model | Why |
|---|---|---|
| Best for biomedical | BioMedLM + Claude Sonnet 4.6 | BioMedLM for biomedical NLP; Sonnet for synthesis and writing |
| Best for math/physics | DeepSeek R1 or Gemini 3.1 Pro (Deep Think) | Gold-level math competition performance; strong formal reasoning |
| Best for literature review | Perplexity Sonar Pro | Real-time citation-grounded research synthesis |
| Best for formal proofs | DeepSeek-Prover-V2 | Only major open-source model for Lean 4 theorem proving |
| Best for SciGLM | SciGLM | Cross-domain (chemistry, biology, physics); Chinese academic institutions |
| Best general | Claude Opus 4.6 (1M context) | Read entire papers, datasets, and related work in one session |
🏗️ DevOps, Infrastructure & Cloud Automation
What you’re building: IaC (Terraform, CDK) generation, CI/CD script automation, cloud cost optimization tools, runbook generation, incident response assistants.
Requirements: Understanding of cloud-specific APIs and services, structured output for YAML/JSON/HCL, low hallucination on resource names and API signatures.
| Tier | Model | Why |
|---|---|---|
| AWS-native | Amazon Q Developer | Deep AWS service knowledge; understands Lambda, CloudFormation, CDK natively |
| Best general | GPT-5.4 or Claude Sonnet 4.6 | Strong at generating accurate IaC; good at multi-file Terraform plans |
| Best open-weight | Llama 4 or Qwen3-Coder (fine-tuned on Terraform) | Self-hosted; fine-tuneable on your specific infra patterns |
| IDE integration | GitHub Copilot Enterprise | Native VS Code/JetBrains; understands repo context; multi-model |
🎨 Creative Content Generation
What you’re building: Marketing copy, social media content, blog post drafts, email campaigns, product narratives, game dialogue, story generation.
Requirements: Creative flexibility, brand voice adherence, variety in output, low repetition, ability to match tone and style.
| Tier | Model | Why |
|---|---|---|
| Best overall | GPT-5 | OpenAI highlights GPT-5 as “best model yet for writing”; literary depth and rhythm; less sycophantic |
| Best for long-form | Claude Sonnet 4.6 | 200K context for maintaining narrative consistency; strong instruction following on style |
| Most “unfiltered” | Grok 4.20 (Spicy mode) | Less restricted creative outputs for mature content platforms (Premium+) |
| Budget at scale | DeepSeek V3.2 or Gemini 3 Flash | Marketing copy at pennies per piece; quality sufficient for most commercial uses |
| Open-weight | Mistral Large 3 or Llama 4 | Fine-tuneable on your brand corpus; no API costs at volume |
🌐 Translation & Localization Pipelines
What you’re building: Automated translation, multilingual content management, localization QA, subtitle generation, cross-language customer support.
Requirements: High translation quality across target languages, cultural adaptation (not just literal translation), fast throughput, cost efficiency for volume.
| Tier | Model | Why |
|---|---|---|
| Best coverage | Qwen3-Next | 119 languages; cultural nuance; strong on Asian languages |
| Best European | Mistral Large 3 | Optimized for FR, DE, IT, ES, PT; strong European cultural context |
| Best for business | Cohere Command R+ | 10 major business languages; grounding in enterprise context |
| Fastest/cheapest | Gemini 3.1 Flash-Lite | Explicitly listed as a top use case by Google; 45% faster than 2.5 Flash; $0.25/M |
| Low-resource languages | BLOOM | 46 languages including many underrepresented ones; open-source |
🧩 Embeddings & Semantic Search
What you’re building: Vector database population, semantic search engines, recommendation systems, document similarity, duplicate detection, clustering pipelines.
Requirements: High-quality embeddings that capture semantic meaning, multilingual support, efficient inference, flexible output dimensions.
| Tier | Model | Why |
|---|---|---|
| Best multimodal | Gemini Embedding 2 (March 28, 2026) | Text + image + video + audio + docs in one unified embedding space; SOTA benchmarks |
| Best text | OpenAI text-embedding-3-large | High quality; well-supported; widely adopted |
| Best open-weight | nomic-embed or BGE (from HuggingFace) | Strong text embeddings; self-hostable; Apache 2.0 |
| Best for code | Voyage Code (via Anthropic) | Optimized for code semantic search; used by Claude Code internally |
🤝 Multi-Agent Orchestration Frameworks
What you’re building: Pipelines where multiple AI agents collaborate — one researches, one writes, one reviews; or parallel agents tackling subtasks simultaneously.
Requirements: Reliable tool use, consistent output format across agents, long context for passing state, low cost for high call volume, predictable behavior.
| Tier | Model | Why |
|---|---|---|
| Best overall orchestrator | Claude Sonnet 4.6 | Best instruction following; most predictable output format; structured outputs GA |
| Best parallel reasoning | Grok 4.20 | Native four-agent architecture; purpose-built for multi-agent workflows |
| Best open-weight | Qwen3 or Mistral Large 3 | Function calling native; Apache 2.0; self-hostable multi-agent pipelines |
| Budget worker agents | DeepSeek V3.2 or Gemini Flash | Use a cheap, fast model for the “worker” agents; expensive model only for final synthesis |
| For computer-use agents | GPT-5.4 or Claude Opus 4.6 | Native computer use; can operate real software as part of an agent pipeline |
Key pattern: Use a flagship model (Claude Sonnet, GPT-5) as the orchestrator that plans, delegates, and synthesizes. Use cheaper models (Haiku, Gemini Flash, DeepSeek V3.2) as worker agents for individual subtasks. This architecture can reduce cost by 70–90% vs. using a frontier model for everything.
🧪 Model Evaluation & Red-Teaming Tools
What you’re building: LLM evaluation frameworks, automated test suites for AI outputs, safety testing tools, benchmark harnesses, hallucination detectors.
Requirements: Reliable judge behavior, ability to score outputs on rubrics, calibrated confidence, low meta-hallucination (the judge hallucinating about the student model’s output).
| Tier | Model | Why |
|---|---|---|
| Best judge model | Claude Opus 4.6 or GPT-5.4 | Highest reasoning reliability; least likely to give sycophantic evaluations |
| Specialized eval model | Atla Selene Mini (8B) | Purpose-built evaluation model; Apache 2.0; strong for automated scoring |
| For safety red-teaming | Claude Sonnet 4.6 | Constitutional AI makes it well-calibrated for harm detection |
| For open eval pipelines | OLMo + OpenAI evals framework | Full transparency; reproducible; good for academic research |
| Cheapest at scale | GPT-5 Mini or Gemini 3 Flash | Run thousands of evals cheaply; use flagship model only for borderline cases |
Summary Decision Table
| Use Case | Primary Pick | Open-Weight | Budget |
|---|---|---|---|
| Customer support chatbot | Claude Sonnet 4.6 | Llama 4 Maverick | Claude Haiku / Gemini Flash-Lite |
| Code completion (IDE) | GitHub Copilot | StarCoder2 / Qwen3-Coder | Codestral |
| Agentic coding | Claude Opus 4.6 | Devstral 2 | Devstral Small 2 |
| Document analysis | Claude Sonnet 4.6 | Llama 4 Scout | Gemini 2.5 Flash |
| RAG / knowledge base | Cohere Command R+ | Mixtral 8x22B | DeepSeek V3.2 |
| Complex reasoning | GPT-5.4 Thinking | DeepSeek R1 | Qwen3-Next |
| Real-time web search | Perplexity Sonar Pro | — | Grok 4.1 Fast |
| Multilingual | Qwen3-Next | Qwen3 / BLOOM | Gemini Flash-Lite |
| Air-gapped / private | Llama 4 (self-hosted) | IBM Granite 4.0 | Phi-4 Mini |
| Structured data extraction | Claude Sonnet 4.6 | Qwen3 (fine-tuned) | DeepSeek V3.2 |
| Computer use / GUI | GPT-5.4 | — (limited) | — |
| Education / tutoring | GPT-5 / Claude Sonnet | Phi-4 Mini | Gemini 3 Flash |
| Healthcare | MedLM (Google Cloud) | Llama 4 (self-hosted) | BioMedLM |
| Legal | Harvey / CoCounsel | ChatLAW + RAG | Claude Sonnet 4.6 |
| Finance | BloombergGPT / Claude Opus | FinGPT | DeepSeek V3.2 |
| Cybersecurity | MS Security Copilot | Llama 4 (self-hosted) | Mixtral fine-tuned |
| Mobile / on-device | Apple on-device / Phi-4 Mini | Gemma 3 4B | Gemma 3 1B |
| Creative writing | GPT-5 | Mistral Large 3 | DeepSeek V3.2 |
| Translation | Qwen3-Next | Mistral Large 3 | Gemini Flash-Lite |
| Embeddings / search | Gemini Embedding 2 | nomic-embed / BGE | text-embedding-3-small |
| Multi-agent orchestration | Claude Sonnet 4.6 | Qwen3 / Mistral | DeepSeek V3.2 (worker) |
| Model evaluation | Claude Opus 4.6 | Atla Selene Mini | GPT-5 Mini |