AI Model Context Window Cheat Sheet
Compare input context limits and max output tokens across frontier, balanced, fast, reasoning, and open-weight LLMs.
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| Provider ⇅ | Model ⇅ | Context ⇅ | Max output ⇅ | Category | Input / 1M ⇅ | Output / 1M ⇅ |
|---|---|---|---|---|---|---|
| OpenAI | OpenAI GPT-4.1 1M context, strong long-context retrieval. | 1.0M | 33K | Frontier | $2.00 | $8.00 |
| OpenAI | OpenAI GPT-4.1 mini Same context window as GPT-4.1 at much lower cost. | 1.0M | 33K | Balanced | $0.40 | $1.60 |
| OpenAI | OpenAI GPT-4.1 nano Smallest, cheapest, fastest; still 1M context. | 1.0M | 33K | Fast / cheap | $0.10 | $0.40 |
| OpenAI | OpenAI GPT-4o 128K context, natively multimodal. | 128K | 16K | Frontier | $2.50 | $10.00 |
| OpenAI | OpenAI o3-mini Reasoning model; high output budget, 200K context. | 200K | 100K | Reasoning | $1.10 | $4.40 |
| OpenAI | OpenAI o1 Deep reasoning; expensive but powerful for hard STEM. | 200K | 100K | Reasoning | $15.00 | $60.00 |
| Anthropic | Anthropic Claude 4 Sonnet 200K context; strong agentic coding and analysis. | 200K | 8K | Frontier | $3.00 | $15.00 |
| Anthropic | Anthropic Claude 4 Opus Highest-end Claude; 200K context. | 200K | 8K | Frontier | $15.00 | $75.00 |
| Anthropic | Anthropic Claude 4 Haiku Fast and cheap; same 200K window. | 200K | 8K | Fast / cheap | $0.25 | $1.25 |
| Google Gemini 2.5 Pro 1M token context, very large output budget. | 1.0M | 66K | Frontier | $1.25 | $10.00 | |
| Google Gemini 2.5 Flash Fast, cheap, 1M context. | 1.0M | 66K | Balanced | $0.15 | $0.60 | |
| Google Gemini 2.0 Flash 1M context at very low cost. | 1.0M | 8K | Fast / cheap | $0.10 | $0.40 | |
| DeepSeek | DeepSeek-V3 Low API cost; 64K context. | 64K | 8K | Balanced | $0.07 | $1.10 |
| DeepSeek | DeepSeek-R1 Open reasoning model; 64K context. | 64K | 8K | Reasoning | $0.14 | $2.19 |
| Meta (open weights) | Meta Llama 4 Scout Open weights; 128K context, can run locally. | 131K | 8K | Open weights | — | — |
| Meta (open weights) | Meta Llama 4 Maverick Open weights; larger variant, 128K context. | 131K | 8K | Open weights | — | — |
| Alibaba Cloud | Alibaba Qwen3-235B-A22B Mixture-of-experts; 128K context. | 128K | 8K | Frontier | $0.50 | $2.00 |
| Alibaba Cloud | Alibaba Qwen3-32B Strong smaller model; 128K context. | 128K | 8K | Balanced | $0.10 | $0.30 |
| Mistral AI | Mistral Large 2 128K context, strong multilingual and coding. | 128K | 8K | Frontier | $2.00 | $6.00 |
| Cohere | Cohere Command R+ 128K context; built for RAG and enterprise. | 128K | 8K | Balanced | $2.50 | $10.00 |
| xAI | xAI Grok 3 128K context; reasoning and coding focused. | 128K | 8K | Frontier | $5.00 | $25.00 |
Key rules of thumb
- System prompt + tools + RAG + history must fit inside the context window. Reserve ~10-20% as a safety buffer.
- Max output tokens limits how long the model's response can be. Reasoning models often have larger output budgets.
- 1M-token contexts (OpenAI GPT-4.1, Google Gemini 2.5) fit entire codebases, long documents, or multi-turn sessions.
- 200K contexts (Claude 4) still cover most enterprise use cases with strong retrieval quality.
- Open-weight models (Llama 4, Qwen 3) are often limited by local GPU VRAM rather than the model's theoretical context.
Token budget planner
Total required context
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Frequently asked questions
What is a context window in an LLM?▼
The context window is the maximum number of input tokens a model can process in one request. It includes the system prompt, user messages, few-shot examples, retrieved RAG chunks, tool outputs, and conversation history.
What is the difference between context tokens and max output tokens?▼
Context tokens are the total tokens the model can read. Max output tokens are the tokens it is allowed to generate in its response. Both consume inference time and cost, but providers usually price them separately.
Which model has the largest context window?▼
As of mid-2026, OpenAI GPT-4.1 series and Google Gemini 2.5 series support roughly 1 million context tokens. Anthropic Claude 4 series supports 200K tokens. Always check the provider's latest docs.
Do larger context windows always mean better retrieval?▼
Not necessarily. A model can read a long context, but retrieval quality, needle-in-haystack performance, and cost vary. Long-context models work best when the relevant information is clearly formatted and placed.
How do I choose a context window for my agent?▼
Estimate your system prompt + tool definitions + RAG chunks + user history + desired output reserve. Add a 20% safety buffer. If you plan to ingest full documents or long conversations, prioritize 128K+ models.
Last updated: 2026-07-28. See notes.
Context-window and output-token limits are based on public API documentation. Providers change limits, pricing, and model availability frequently; verify before building production agents.