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.
123B—
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.
256k—
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.
$0.40Mistral—
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input.
$2.00Mistral—
$180Mistral—
Benchmarks
SWE-Bench MultilingualMultilingual coding — Like SWE-Bench, but the coding problems span many programming languages, not just one. Tests how broadly the AI can code. Higher is better.
61.3%73.7%
Terminal-Bench 2.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? (Version 2.0 of the test.) Higher is better.
32.6%61.7%
Overview
CompanyMistralSpaceXSI
Release dateDec 9 2025Mar 19 2026
AccessOpen WeightClosed
Model detailsView modelView model

Frequently asked questions

Composer 2 leads Devstral 2 (123B) on 2 of the 2 benchmarks they both report (SWE-Bench Multilingual, Terminal-Bench 2.0). Devstral 2 (123B) shipped 100 days before Composer 2, so benchmark comparisons should account for the intervening progress.

Devstral 2 (123B) is open weight, while Composer 2 is closed.

On SWE-Bench Multilingual, Composer 2 leads at 73.7% vs Devstral 2 (123B) at 61.3%. On Terminal-Bench 2.0, Composer 2 leads at 61.7% vs Devstral 2 (123B) at 32.6%.