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 | — |
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. | — | $0.50 |
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. | — | $3.00 |
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 | $0.325Alibaba |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $2.00Mistral | $1.95Alibaba |
| $180Mistral | $163Alibaba | |
| Benchmarks | ||
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% | 78.8% |
BullshitBench v2Nonsense detection — Given a confidently-worded but nonsensical prompt, does the AI spot that it makes no sense and push back — instead of playing along and inventing an answer? The score is how often it clearly called out the nonsense. Higher is better. | — | 72% |
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% | — |
Humanity's Last Exam · with toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better. | — | 28.8% |
| Overview | ||
| Company | Mistral | Qwen |
| Release date | Dec 9 2025 | Apr 2 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Devstral 2 (123B)vsClaude Haiku 5.5Qwen3.6-PlusvsClaude Haiku 5.5Devstral 2 (123B)vsGPT-6.1 SolQwen3.6-PlusvsGPT-6.1 SolDevstral 2 (123B)vsGemini 4 ArgonQwen3.6-PlusvsGemini 4 ArgonDevstral 2 (123B)vsMuse Spark OpenQwen3.6-PlusvsMuse Spark OpenDevstral 2 (123B)vsGrok 4.7Qwen3.6-PlusvsGrok 4.7Devstral 2 (123B)vsDeepSeek-V4.1-Flash-1017Qwen3.6-PlusvsDeepSeek-V4.1-Flash-1017Frequently asked questions
Qwen3.6-Plus leads Devstral 2 (123B) on 1 of the 1 benchmark they both report (SWE-Bench Verified). Only Qwen3.6-Plus has a verified first-party API price: $0.50 per million input tokens and $3.00 per million output tokens. No pay-as-you-go API rate is tracked for Devstral 2 (123B). Devstral 2 (123B) shipped 114 days before Qwen3.6-Plus, so benchmark comparisons should account for the intervening progress.
Context windows are 256k (Devstral 2 (123B)) vs 1M (Qwen3.6-Plus). Devstral 2 (123B) is open weight, while Qwen3.6-Plus is closed.
On SWE-Bench Verified, Qwen3.6-Plus leads at 78.8% vs Devstral 2 (123B) at 72.2%.