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. | 1.3B/5.7B/6.7B/33B | 141B |
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. | 16k | 64k |
| 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 · zdr | |
| Overview | ||
| Company | DeepSeek | Mistral |
| Release date | Nov 2 2023 | Apr 10 2024 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
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DeepSeek Coder and Mixtral 8×22B don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. DeepSeek Coder shipped 160 days before Mixtral 8×22B, so benchmark comparisons should account for the intervening progress.
DeepSeek Coder has 1.3B/5.7B/6.7B/33B parameters, while Mixtral 8×22B has 141B. Context windows are 16k (DeepSeek Coder) vs 64k (Mixtral 8×22B).
Direct benchmark comparisons are unavailable — DeepSeek Coder and Mixtral 8×22B don't publish scores on any of the same benchmarks.