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 | 80B |
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. | — | $0.30 |
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. | — | $1.50 |
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 | $0.12Parasail |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $2.00Mistral | $0.80Parasail |
| $180Mistral | $64.00Parasail · bf16 | |
| 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% | 70.6% |
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% | 62.8% |
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% | 36.2% |
SWE-Bench ProAgentic coding — Can the AI fix real bugs in real software? It's handed actual problems from open-source projects and has to write code that genuinely solves them. Higher is better. | — | 44.3% |
| Overview | ||
| Company | Mistral | Qwen |
| Release date | Dec 9 2025 | Feb 3 2026 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
Devstral 2 (123B)vsClaude Haiku 5.5Qwen3-Coder-NextvsClaude Haiku 5.5Devstral 2 (123B)vsGPT-6.1 SolQwen3-Coder-NextvsGPT-6.1 SolDevstral 2 (123B)vsGemini 4 ArgonQwen3-Coder-NextvsGemini 4 ArgonDevstral 2 (123B)vsMuse Spark OpenQwen3-Coder-NextvsMuse Spark OpenDevstral 2 (123B)vsGrok 4.7Qwen3-Coder-NextvsGrok 4.7Devstral 2 (123B)vsDeepSeek-V4.1-Flash-1017Qwen3-Coder-NextvsDeepSeek-V4.1-Flash-1017Frequently asked questions
Qwen3-Coder-Next leads Devstral 2 (123B) on 2 of the 3 benchmarks they both report (SWE-Bench Verified, SWE-Bench Multilingual, Terminal-Bench 2.0). Only Qwen3-Coder-Next has a verified first-party API price: $0.30 per million input tokens and $1.50 per million output tokens. No pay-as-you-go API rate is tracked for Devstral 2 (123B). Devstral 2 (123B) shipped 56 days before Qwen3-Coder-Next, so benchmark comparisons should account for the intervening progress.
Devstral 2 (123B) has 123B parameters, while Qwen3-Coder-Next has 80B. Context windows are 256k (Devstral 2 (123B)) vs 256k (Qwen3-Coder-Next).
On SWE-Bench Verified, Devstral 2 (123B) leads at 72.2% vs Qwen3-Coder-Next at 70.6%. On SWE-Bench Multilingual, Qwen3-Coder-Next leads at 62.8% vs Devstral 2 (123B) at 61.3%. On Terminal-Bench 2.0, Qwen3-Coder-Next leads at 36.2% vs Devstral 2 (123B) at 32.6%.