AI Model Release Tracker

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.
123B0.5B/1.8B/4B/7B/14B/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.
128k32k
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
CompanyMistralQwen
Release dateJul 24 2024Feb 4 2024
AccessOpen WeightOpen Weight
Model detailsView modelView model

Other comparisons

Mistral Large 2vsClaude Haiku 5.5Qwen1.5vsClaude Haiku 5.5Mistral Large 2vsGPT-6.1 SolQwen1.5vsGPT-6.1 SolMistral Large 2vsGemini 4 ArgonQwen1.5vsGemini 4 ArgonMistral Large 2vsMuse Spark OpenQwen1.5vsMuse Spark OpenMistral Large 2vsGrok 4.7Qwen1.5vsGrok 4.7Mistral Large 2vsDeepSeek-V4.1-Flash-1017Qwen1.5vsDeepSeek-V4.1-Flash-1017

Frequently asked questions

Mistral Large 2 and Qwen1.5 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Qwen1.5 shipped 171 days before Mistral Large 2, so benchmark comparisons should account for the intervening progress.

Mistral Large 2 has 123B parameters, while Qwen1.5 has 0.5B/1.8B/4B/7B/14B/72B. Context windows are 128k (Mistral Large 2) vs 32k (Qwen1.5).

Direct benchmark comparisons are unavailable — Mistral Large 2 and Qwen1.5 don't publish scores on any of the same benchmarks.