Context windows, pricing, and capabilities for every major large language model — sortable, filterable.
Updated August 2026 · Prices in USD per 1M tokens
| Model | Context Window ↕ | Input $/1M ↕ | Output $/1M ↕ | Strengths |
|---|---|---|---|---|
As of August 2026, Gemini 2.5 Pro, Gemini 2.5 Flash, Gemini 2.0 Flash, and GPT-4.1 all support 1 million token contexts (approximately 750,000 words or several books' worth of text). This makes them ideal for processing entire codebases, lengthy legal documents, or extensive research archives in a single API call. Claude Opus 4.7, Claude Sonnet 4.6, Claude Haiku 4.5, o3, and o4-mini support 200,000 tokens — still capable of handling book-length documents. GPT-4o supports 128,000 tokens, sufficient for the vast majority of real-world applications.
Gemini 2.0 Flash is currently the lowest-cost option at $0.10/1M input and $0.40/1M output tokens. This makes it extraordinarily economical for high-volume applications — processing 10 million input tokens costs just $1.00. GPT-4o mini ($0.15/$0.60) is also extremely affordable and widely used for consumer-facing applications. Claude Haiku 4.5 ($0.80/$4.00) is the budget option from Anthropic, offering strong instruction-following at a fraction of the cost of Claude Sonnet. Note that the cheapest model for your use case depends on quality requirements — always benchmark on your specific task before optimizing purely for cost.
Both are excellent mid-tier models at similar price points ($2.00 vs. $2.00 per million input tokens). GPT-5.6 Terra tends to excel on structured coding tasks, tool use, and complex multi-step instruction following within its 270K context window. Claude Sonnet 5 typically shines at nuanced long-document analysis, creative writing, and use cases requiring predictable structured output — Claude's XML-formatted response capabilities remain a popular choice for applications needing parseable outputs. For most teams, the best approach is to evaluate both on your actual prompt patterns, since real-world performance often differs significantly from published benchmark scores.
The context window is the maximum number of tokens a model can process in a single API call — this includes your system prompt, all conversation history, any documents you attach, and the model's response. Exceeding the context window causes the API to reject the request with an error. In practical terms, a 200K token context (like Claude) can hold roughly 150–160 pages of text. A 270K context (like GPT-5.6) holds about 200+ pages. A 1M token context (like all current Gemini models) can hold entire codebases or several full-length books simultaneously. Context window size is one of the most important practical constraints when designing AI applications, especially for RAG (Retrieval-Augmented Generation) systems or long-running agentic sessions.
Flagship models use significantly more compute to deliver higher-quality outputs on complex tasks — multi-step reasoning, nuanced creative writing, difficult code generation, and agentic workflows with many tool calls. They can be 5–50× more expensive than budget models. For straightforward tasks (summarization, classification, simple Q&A), the premium is rarely justified. They shine on genuinely hard problems where mid-tier models fail or produce inconsistent output. The practical approach: prototype with a flagship, then step down to a mid-tier model (GPT-5.6 Terra, Claude Sonnet 5, Gemini 3.5 Flash) and benchmark whether the quality holds for your specific task. Many production systems use a mix — flagship for complex reasoning steps, budget models for high-volume simple tasks.
Provider choice often comes down to use case and ecosystem fit. OpenAI has the largest developer ecosystem, most mature tooling, and widest compatibility across libraries and frameworks — it's often the safe default choice. Anthropic Claude models are particularly strong for safety-critical applications, nuanced instruction-following, and use cases requiring predictable, well-structured outputs. Google Gemini models offer the longest context windows at the most competitive prices, making them excellent for document-heavy applications and cost-sensitive high-volume workloads. Many organizations run multiple providers: a premium model for user-facing responses, a budget model for background processing, and a reasoning model for complex decision-making.