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. | 70B | 7B |
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 | 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. | $0.10DeepInfra | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.32DeepInfra | — |
| $36.00DeepInfra · turbo · fp8 | — | |
| Overview | ||
| Company | Meta | Mistral |
| Release date | Dec 6 2024 | Jul 16 2024 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
LLaMA 3.3vsClaude Haiku 5.5Mathstral 7BvsClaude Haiku 5.5LLaMA 3.3vsGPT-6.1 SolMathstral 7BvsGPT-6.1 SolLLaMA 3.3vsGemini 4 ArgonMathstral 7BvsGemini 4 ArgonLLaMA 3.3vsGrok 4.7Mathstral 7BvsGrok 4.7LLaMA 3.3vsDeepSeek-V4.1-Flash-1017Mathstral 7BvsDeepSeek-V4.1-Flash-1017LLaMA 3.3vsKimi K3Mathstral 7BvsKimi K3Frequently asked questions
LLaMA 3.3 and Mathstral 7B don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Mathstral 7B shipped 143 days before LLaMA 3.3, so benchmark comparisons should account for the intervening progress.
LLaMA 3.3 has 70B parameters, while Mathstral 7B has 7B. Context windows are 128k (LLaMA 3.3) vs 32k (Mathstral 7B).
Direct benchmark comparisons are unavailable — LLaMA 3.3 and Mathstral 7B don't publish scores on any of the same benchmarks.