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: Qwen: Qwen3.5 Plus 2026-02-15, Qwen: Qwen3.5 397B A17B, MiniMax: MiniMax-01.
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 | Qwen: Qwen3.5 Plus 2026-02-15 | Qwen | $0.26 | $1.56 | 1,000,000 |
| 2 | Qwen: Qwen3.5 397B A17B | Qwen | $0.39 | $2.34 | 262,144 |
| 3 | MiniMax: MiniMax-01 | MiniMax | $0.20 | $1.10 | 1,000,192 |
| 4 | Anthropic: Claude Sonnet 4.5 | Anthropic | $3.00 | $15.00 | 1,000,000 |
| 5 | Xiaomi: MiMo-V2-Flash | Xiaomi | $0.09 | $0.29 | 262,144 |
| 6 | Qwen: Qwen3.5-122B-A10B | Qwen | $0.26 | $2.08 | 262,144 |
| 7 | Qwen: Qwen3.5-27B | Qwen | $0.20 | $1.56 | 262,144 |
| 8 | Meta: Llama 4 Maverick | Meta | $0.15 | $0.60 | 1,048,576 |
| 9 | Google: Gemma 4 31B | $0.00 | $0.00 | 262,144 | |
| 10 | Google: Gemma 4 31B | $0.13 | $0.38 | 262,144 |
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 Qwen: Qwen3.5 Plus 2026-02-15, Qwen: Qwen3.5 397B A17B, MiniMax: MiniMax-01.
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.