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. | — | 0.5B/1.8B/4B/7B/14B/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. | 32k | 32k |
| 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 | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $6.00Mistral | — |
| $700Mistral | — | |
| Overview | ||
| Company | Mistral | Qwen |
| Release date | Feb 26 2024 | Feb 4 2024 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
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Mistral Large and Qwen1.5 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Qwen1.5 shipped 22 days before Mistral Large, so benchmark comparisons should account for the intervening progress.
Context windows are 32k (Mistral Large) vs 32k (Qwen1.5). Mistral Large is closed, while Qwen1.5 is open weight.
Direct benchmark comparisons are unavailable — Mistral Large and Qwen1.5 don't publish scores on any of the same benchmarks.