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 | 0.6B/1.7B/4B/8B/14B/30B/32B/235B |
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 | 128k |
| 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. | — | $0.70 |
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. | — | $2.80 |
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 | $0.08DeepInfra |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $2.00Mistral | $0.22NextBit |
| $180Mistral | $30.00DeepInfra · fp8 | |
| These models have no shared benchmark scores. | ||
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. | — | 6% |
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 | Apr 29 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
Devstral 2 (123B)vsClaude Haiku 5.5Qwen3vsClaude Haiku 5.5Devstral 2 (123B)vsGPT-6.1 SolQwen3vsGPT-6.1 SolDevstral 2 (123B)vsGemini 4 ArgonQwen3vsGemini 4 ArgonDevstral 2 (123B)vsMuse Spark OpenQwen3vsMuse Spark OpenDevstral 2 (123B)vsGrok 4.7Qwen3vsGrok 4.7Devstral 2 (123B)vsDeepSeek-V4.1-Flash-1017Qwen3vsDeepSeek-V4.1-Flash-1017Frequently asked questions
Devstral 2 (123B) and Qwen3 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Qwen3 has a verified first-party API price: $0.70 per million input tokens and $2.80 per million output tokens. No pay-as-you-go API rate is tracked for Devstral 2 (123B). Qwen3 shipped 224 days before Devstral 2 (123B), so benchmark comparisons should account for the intervening progress.
Devstral 2 (123B) has 123B parameters, while Qwen3 has 0.6B/1.7B/4B/8B/14B/30B/32B/235B. Context windows are 256k (Devstral 2 (123B)) vs 128k (Qwen3).
Direct benchmark comparisons are unavailable — Devstral 2 (123B) and Qwen3 don't publish scores on any of the same benchmarks.