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.
7B80B
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.
32k256k
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.
—$0.072DeepInfra
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input.
—$0.88DeepInfra
—$73.00DeepInfra · fp8
Overview
CompanyMistralQwen
Release dateJul 16 2024Sep 11 2025
AccessOpen WeightOpen Weight
Model detailsView modelView model

Other comparisons

Mathstral 7BvsClaude Haiku 5.5Qwen3-NextvsClaude Haiku 5.5Mathstral 7BvsGPT-6.1 SolQwen3-NextvsGPT-6.1 SolMathstral 7BvsGemini 4 ArgonQwen3-NextvsGemini 4 ArgonMathstral 7BvsMuse Spark OpenQwen3-NextvsMuse Spark OpenMathstral 7BvsGrok 4.7Qwen3-NextvsGrok 4.7Mathstral 7BvsDeepSeek-V4.1-Flash-1017Qwen3-NextvsDeepSeek-V4.1-Flash-1017

Frequently asked questions

Mathstral 7B and Qwen3-Next don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Mathstral 7B shipped 422 days before Qwen3-Next, so benchmark comparisons should account for the intervening progress.

Mathstral 7B has 7B parameters, while Qwen3-Next has 80B. Context windows are 32k (Mathstral 7B) vs 256k (Qwen3-Next).

Direct benchmark comparisons are unavailable — Mathstral 7B and Qwen3-Next don't publish scores on any of the same benchmarks.