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 | 397B |
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 | 1M |
| 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. | — | $0.40 |
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. | — | $2.40 |
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 | $0.26Alibaba |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $2.00Mistral | $1.56Alibaba |
| $180Mistral | $130Alibaba | |
| 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 | Feb 16 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Devstral 2 (123B)vsClaude Haiku 5.5Qwen3.5-PlusvsClaude Haiku 5.5Devstral 2 (123B)vsGPT-6.1 SolQwen3.5-PlusvsGPT-6.1 SolDevstral 2 (123B)vsGemini 4 ArgonQwen3.5-PlusvsGemini 4 ArgonDevstral 2 (123B)vsMuse Spark OpenQwen3.5-PlusvsMuse Spark OpenDevstral 2 (123B)vsGrok 4.7Qwen3.5-PlusvsGrok 4.7Devstral 2 (123B)vsDeepSeek-V4.1-Flash-1017Qwen3.5-PlusvsDeepSeek-V4.1-Flash-1017Frequently asked questions
Devstral 2 (123B) and Qwen3.5-Plus don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Qwen3.5-Plus has a verified first-party API price: $0.40 per million input tokens and $2.40 per million output tokens. No pay-as-you-go API rate is tracked for Devstral 2 (123B). Devstral 2 (123B) shipped 69 days before Qwen3.5-Plus, so benchmark comparisons should account for the intervening progress.
Devstral 2 (123B) has 123B parameters, while Qwen3.5-Plus has 397B. Context windows are 256k (Devstral 2 (123B)) vs 1M (Qwen3.5-Plus). Devstral 2 (123B) is open weight, while Qwen3.5-Plus is closed.
Direct benchmark comparisons are unavailable — Devstral 2 (123B) and Qwen3.5-Plus don't publish scores on any of the same benchmarks.