AI Model Release Tracker

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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.
7B0.5B/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.
32k128k
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.
—$0.36DeepInfra
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input.
—$0.40DeepInfra
—$92.00DeepInfra · fp8
Overview
CompanyMistralQwen
Release dateJul 16 2024Sep 19 2024
AccessOpen WeightOpen Weight
Model detailsView modelView model

Other comparisons

Mathstral 7BvsClaude Haiku 5.5Qwen2.5vsClaude Haiku 5.5Mathstral 7BvsGPT-6.1 SolQwen2.5vsGPT-6.1 SolMathstral 7BvsGemini 4 ArgonQwen2.5vsGemini 4 ArgonMathstral 7BvsMuse Spark OpenQwen2.5vsMuse Spark OpenMathstral 7BvsGrok 4.7Qwen2.5vsGrok 4.7Mathstral 7BvsDeepSeek-V4.1-Flash-1017Qwen2.5vsDeepSeek-V4.1-Flash-1017

Frequently asked questions

Mathstral 7B and Qwen2.5 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Mathstral 7B shipped 65 days before Qwen2.5, so benchmark comparisons should account for the intervening progress.

Mathstral 7B has 7B parameters, while Qwen2.5 has 0.5B/1.5B/3B/7B/14B/32B/72B. Context windows are 32k (Mathstral 7B) vs 128k (Qwen2.5).

Direct benchmark comparisons are unavailable — Mathstral 7B and Qwen2.5 don't publish scores on any of the same benchmarks.