Ideal für: Long-Context Workloads
Ranked on context window size, needle-in-a-haystack accuracy, and input price — long-context is input-token-heavy.
Aktualisiert April 2026. Top 3 diesen Monat: Qwen3.5 Plus 2026-02-15, Qwen3.5 397B A17B, MiniMax-01.
Podium
So werten wir
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.
Our full methodology is published on the Methodik-Seite.
Säulen und Gewichte:
Full ranking
| Rang | Modell | Anbieter | Input $/1M | Output $/1M | Kontext |
|---|---|---|---|---|---|
| 1 | Qwen3.5 Plus 2026-02-15 | Qwen | $0.26 | $1.56 | 1,000,000 |
| 2 | Qwen3.5 397B A17B | Qwen | $0.39 | $2.34 | 262,144 |
| 3 | MiniMax-01 | MiniMax | $0.20 | $1.10 | 1,000,192 |
| 4 | Claude Sonnet 4.5 | Anthropic | $3.00 | $15.00 | 1,000,000 |
| 5 | MiMo-V2-Flash | Xiaomi | $0.09 | $0.29 | 262,144 |
| 6 | Qwen3.5-122B-A10B | Qwen | $0.26 | $2.08 | 262,144 |
| 7 | Qwen3.5-27B | Qwen | $0.20 | $1.56 | 262,144 |
| 8 | Llama 4 Maverick | Meta | $0.15 | $0.60 | 1,048,576 |
| 9 | Gemma 4 31B | $0.00 | $0.00 | 262,144 | |
| 10 | Gemma 4 31B | $0.13 | $0.38 | 262,144 |
Field notes
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.
FAQ
The questions teams ask before picking a model for long-context workloads.
Get instant answers from our AI agent
