Best LLM for Long-Context Workloads
Ranked on context window size, needle-in-a-haystack accuracy, and input price — long-context is input-token-heavy.
Updated April 2026. Top 3 this month: GPT-5, Gemini 2 Pro, Claude Opus 4.7.
How we rank
If you are summarizing books, reviewing legal discovery, or analyzing multi-turn transcripts, the context window is the cliff you fall off. But bigger is not always better: many long-context models degrade in accuracy past a certain depth. We weight context size moderately and weight long-context benchmark accuracy more.
Pillars and weights: context window (25%) · long-context accuracy (45%) · input price (30%). Our full methodology is published on the methodology page.
Top ranked models
| Rank | Model | Provider | Input $/1M | Output $/1M | Context |
|---|---|---|---|---|---|
| 1 | GPT-5 | OpenAI | $1.25 | $10.00 | 200,000 |
| 2 | Gemini 2 Pro | $3.50 | $10.50 | 2,000,000 | |
| 3 | Claude Opus 4.7 | Anthropic | $5.00 | $25.00 | 200,000 |
| 4 | Gemini 2.0 Flash-Lite | $0.07 | $0.30 | 1,000,000 | |
| 5 | GPT-5 nano | OpenAI | $0.05 | $0.40 | 400,000 |
| 6 | Gemini 2.0 Flash | $0.10 | $0.40 | 1,000,000 | |
| 7 | GPT-4.1 nano | OpenAI | $0.10 | $0.40 | 1,000,000 |
| 8 | MiniMax-Text-01 | MiniMax | $0.20 | $1.10 | 1,000,000 |
| 9 | MiniMax-01 | MiniMax | $0.20 | $1.10 | 1,000,000 |
| 10 | qwen3.5-9b | Alibaba (Qwen) | $0.40 | $1.50 | 262,000 |
Tips for long-context workloads
- Prefer cached-input pricing to avoid paying full price for re-submitted long prompts.
- Chunk intelligently — a 1M-token context with bad retrieval is worse than a 128k context with good retrieval.
- Measure latency: very long contexts add seconds per query.
Frequently asked questions
Which model has the longest context?
Some models advertise 1–2M tokens. As of April 2026, our weighted top 3 considering accuracy at depth are GPT-5, Gemini 2 Pro, Claude Opus 4.7.
Does big context replace RAG?
Sometimes. For repeating corpora, RAG is still cheaper. For a one-off long document review, paste it.
How fast do long contexts degrade?
Varies a lot. Some models are flat out to 200k; some drop sharply after 64k. Always test on your workload.
Related tasks
Want to model your own workload? Use the volume and switch-cost calculators on the main tool page. Sign in with Google to unlock compare-my-prompt with real tokenizer counts.
Data refreshed daily via our snapshot cron. See our public JSON API for programmatic access.