81Fit score
Claude Haiku 5.5 is Anthropic's small, fast model for high-volume, cost-sensitive work such as summarization, subagents, and browser use. It succeeds Claude Haiku 4.5 with stronger coding, computer use, and...
Model details →Best long-context AI models (128K+)
Sorted by maximum context window. Long-context models charge more per token but eliminate the need for chunking and summarization in many workflows.
81Fit score
DeepSeek V4 Flash is an efficiency-optimized Mixture-of-Experts model from DeepSeek with 284B total parameters and 13B activated parameters, supporting a 1M-token context window. It is designed for fast inference and...
Model details →81Fit score
DeepSeek V4 Flash 0731 is a sparse mixture-of-experts model from DeepSeek, with 13B active parameters out of 284B total. This re-post-trained revision is suited for coding, reasoning, and agent workflows....
Model details →Top 25 models for large documents
| # | Model | Provider | Context | Input price / 1M | Tier |
|---|---|---|---|---|---|
| 1 | Anthropic: Claude Haiku 5.5 (batch) | Anthropic | 1M Ultra context (1M+) | $0.05 | Budget |
| 2 | DeepSeek: DeepSeek V4 Flash 0423 | DeepSeek | 1.0M Ultra context (1M+) | $0.09 | Budget |
| 3 | DeepSeek: DeepSeek V4 Flash 0731 | DeepSeek | 1.0M Ultra context (1M+) | $0.02 | Budget |
| 4 | DeepSeek: DeepSeek V4 Flash 0731 (free) | DeepSeek | 1.0M Ultra context (1M+) | $0.00 | Budget |
| 5 | DeepSeek: DeepSeek V4.1 Flash | DeepSeek | 1.0M Ultra context (1M+) | $0.04 | Budget |
| 6 | Google: Gemini 2.0 Flash Lite | 1.0M Ultra context (1M+) | $0.07 | Budget | |
| 7 | Google: Gemini 2.5 Flash Lite (batch) | 1.0M Ultra context (1M+) | $0.05 | Budget | |
| 8 | Google: Lyria 3 Clip Preview | 1.0M Ultra context (1M+) | $0.00 | Budget | |
| 9 | Google: Lyria 3 Pro Preview | 1.0M Ultra context (1M+) | $0.00 | Budget | |
| 10 | MiniMax: MiniMax M3 (free) | Minimax | 1.0M Ultra context (1M+) | $0.00 | Budget |
| 11 | NVIDIA: Nemotron 3 Ultra (free) | Nvidia | 1M Ultra context (1M+) | $0.00 | Budget |
| 12 | NVIDIA: Nemotron 3.5 Lightning (free) | Nvidia | 1M Ultra context (1M+) | $0.00 | Budget |
| 13 | NVIDIA: Switchyard | Nvidia | 1M Ultra context (1M+) | $-1000000.00 | Budget |
| 14 | OpenAI: GPT-4.1 Nano (batch) | OpenAI | 1.0M Ultra context (1M+) | $0.05 | Budget |
| 15 | OpenAI: GPT-6 Luna (batch) | OpenAI | 1.1M Ultra context (1M+) | $0.05 | Budget |
| 16 | OpenAI: GPT-6 Luna Pro (batch) | OpenAI | 1.1M Ultra context (1M+) | $0.05 | Budget |
| 17 | Auto Router | Openrouter | 2M Ultra context (1M+) | $-1000000.00 | Budget |
| 18 | Auto Router (Beta) | Openrouter | 2M Ultra context (1M+) | $-1000000.00 | Budget |
| 19 | OpenRouter: Fusion | Openrouter | 1M Ultra context (1M+) | $-1000000.00 | Budget |
| 20 | Pareto Code Router | Openrouter | 2M Ultra context (1M+) | $-1000000.00 | Budget |
| 21 | Poolside: Laguna S 2.1 | Poolside | 1.0M Ultra context (1M+) | $0.09 | Budget |
| 22 | Qwen: Qwen3.5-Flash | Qwen | 1M Ultra context (1M+) | $0.07 | Budget |
| 23 | Qwen: Qwen3.6 Plus Preview (free) | Qwen | 1M Ultra context (1M+) | $0.00 | Budget |
| 24 | Qwen: Qwen3.7 Flash | Qwen | 1M Ultra context (1M+) | $0.03 | Budget |
| 25 | Qwen: Qwen3.8 27B | Qwen | 1M Ultra context (1M+) | $0.04 | Budget |
Frequently asked questions
What can I do with a 1M-context model?
Drop entire codebases, books, or hours of meeting transcripts in one prompt. Useful for research synthesis, codebase Q&A, and long-running agents that keep state in-context instead of in a vector DB.
Does longer context mean better answers?
Not always — accuracy on 'needle-in-haystack' tasks varies a lot across models. Check published long-context benchmarks for the specific model.
Related use cases
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