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 | 30B |
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 | 1M |
| 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 | $0.00Nvidia |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $2.00Mistral | $0.00Nvidia |
| $180Mistral | $11.00Darkbloom · int4 | |
| Benchmarks | ||
SWE-Bench VerifiedCoding — Real coding tasks pulled from open-source projects — the AI has to find and fix actual bugs. A human-checked version of the original SWE-Bench. Higher is better. | 72.2% | 51.6% |
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% | — |
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% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 75.4% |
| Overview | ||
| Company | Mistral | NVIDIA |
| Release date | Dec 9 2025 | Aug 11 2026 |
| Access | Open Weight | Open Source |
| Model details | View model | View model |
Other comparisons
Devstral 2 (123B)vsClaude Haiku 5.5Nemotron 3.5 LightningvsClaude Haiku 5.5Devstral 2 (123B)vsGPT-6.1 SolNemotron 3.5 LightningvsGPT-6.1 SolDevstral 2 (123B)vsGemini 4 ArgonNemotron 3.5 LightningvsGemini 4 ArgonDevstral 2 (123B)vsMuse Spark OpenNemotron 3.5 LightningvsMuse Spark OpenDevstral 2 (123B)vsGrok 4.7Nemotron 3.5 LightningvsGrok 4.7Devstral 2 (123B)vsDeepSeek-V4.1-Flash-1017Nemotron 3.5 LightningvsDeepSeek-V4.1-Flash-1017Frequently asked questions
Devstral 2 (123B) leads Nemotron 3.5 Lightning on 1 of the 1 benchmark they both report (SWE-Bench Verified). Devstral 2 (123B) shipped 245 days before Nemotron 3.5 Lightning, so benchmark comparisons should account for the intervening progress.
Devstral 2 (123B) has 123B parameters, while Nemotron 3.5 Lightning has 30B. Context windows are 256k (Devstral 2 (123B)) vs 1M (Nemotron 3.5 Lightning). Devstral 2 (123B) is open weight, while Nemotron 3.5 Lightning is open source.
On SWE-Bench Verified, Devstral 2 (123B) leads at 72.2% vs Nemotron 3.5 Lightning at 51.6%.