Compare AI models
| Specifications | ||
ParametersThe learned values that shape how a model responds. B means billion and T means trillion. For mixture-of-experts models, the total count is larger than the number active for each token. Parameter count alone does not measure quality. | 123B | >1T |
Context windowHow much information the model can work with in one request, measured in tokens — pieces of words or other data. This includes your question, attachments, conversation and reply. Limits can vary by model variant and provider. | 256k | 256k |
| API pricingUSD per 1M tokens · lower wins · base tier | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | — | $1.20 |
Output priceWhat you pay for the text the model writes back. It is normally the dearer half: producing an answer costs more than reading one. | — | $6.00 |
Cheapest inputLowest input rate across every provider on OpenRouter, the lab included. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | $0.40Mistral | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $2.00Mistral | — |
| $180Mistral | $540Qwen list price | |
| These models have no shared benchmark scores. | ||
SWE-Bench VerifiedCoding — Real coding tasks pulled from open-source projects — the AI has to find and fix actual bugs. A human-checked version of the original SWE-Bench. Higher is better. | 72.2% | — |
SWE-Bench MultilingualMultilingual coding — Like SWE-Bench, but the coding problems span many programming languages, not just one. Tests how broadly the AI can code. Higher is better. | 61.3% | — |
Terminal-Bench 2.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? (Version 2.0 of the test.) Higher is better. | 32.6% | — |
| Overview | ||
| Company | Mistral | Qwen |
| Release date | Dec 9 2025 | Sep 24 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Devstral 2 (123B)vsClaude Haiku 5.5Qwen3-MaxvsClaude Haiku 5.5Devstral 2 (123B)vsGPT-6.1 SolQwen3-MaxvsGPT-6.1 SolDevstral 2 (123B)vsGemini 4 ArgonQwen3-MaxvsGemini 4 ArgonDevstral 2 (123B)vsMuse Spark OpenQwen3-MaxvsMuse Spark OpenDevstral 2 (123B)vsGrok 4.7Qwen3-MaxvsGrok 4.7Devstral 2 (123B)vsDeepSeek-V4.1-Flash-1017Qwen3-MaxvsDeepSeek-V4.1-Flash-1017Frequently asked questions
Devstral 2 (123B) and Qwen3-Max don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Qwen3-Max has a verified first-party API price: $1.20 per million input tokens and $6.00 per million output tokens. No pay-as-you-go API rate is tracked for Devstral 2 (123B). Qwen3-Max shipped 76 days before Devstral 2 (123B), so benchmark comparisons should account for the intervening progress.
Devstral 2 (123B) has 123B parameters, while Qwen3-Max has >1T. Context windows are 256k (Devstral 2 (123B)) vs 256k (Qwen3-Max). Devstral 2 (123B) is open weight, while Qwen3-Max is closed.
Direct benchmark comparisons are unavailable — Devstral 2 (123B) and Qwen3-Max don't publish scores on any of the same benchmarks.