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 | 0.5B/1.5B/3B/7B/14B/32B/72B |
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. | 128k | 128k |
| API pricingUSD per 1M tokens · lower wins | ||
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. | $2.00Mistral | $0.36DeepInfra |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $6.00Mistral | $0.40DeepInfra |
| $700Mistral | $92.00DeepInfra · fp8 | |
| Overview | ||
| Company | Mistral | Qwen |
| Release date | Jul 24 2024 | Sep 19 2024 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
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Mistral Large 2 and Qwen2.5 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Mistral Large 2 shipped 57 days before Qwen2.5, so benchmark comparisons should account for the intervening progress.
Mistral Large 2 has 123B parameters, while Qwen2.5 has 0.5B/1.5B/3B/7B/14B/32B/72B. Context windows are 128k (Mistral Large 2) vs 128k (Qwen2.5).
Direct benchmark comparisons are unavailable — Mistral Large 2 and Qwen2.5 don't publish scores on any of the same benchmarks.