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. | — | 9B |
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 | 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 | — |
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 | Z.ai |
| Release date | Feb 26 2024 | Jun 5 2024 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
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Mistral Large and GLM-4-9B don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Mistral Large shipped 100 days before GLM-4-9B, so benchmark comparisons should account for the intervening progress.
Context windows are 32k (Mistral Large) vs 128k (GLM-4-9B). Mistral Large is closed, while GLM-4-9B is open weight.
Direct benchmark comparisons are unavailable — Mistral Large and GLM-4-9B don't publish scores on any of the same benchmarks.