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. | — | 8B/49B |
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 | 128k |
| Overview | ||
| Company | Mistral | NVIDIA |
| Release date | Jan 13 2025 | Mar 18 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Codestral 25.01vsClaude Haiku 5.5Llama Nemotron (Nano/Super)vsClaude Haiku 5.5Codestral 25.01vsGPT-6.1 SolLlama Nemotron (Nano/Super)vsGPT-6.1 SolCodestral 25.01vsGemini 4 ArgonLlama Nemotron (Nano/Super)vsGemini 4 ArgonCodestral 25.01vsMuse Spark OpenLlama Nemotron (Nano/Super)vsMuse Spark OpenCodestral 25.01vsGrok 4.7Llama Nemotron (Nano/Super)vsGrok 4.7Codestral 25.01vsDeepSeek-V4.1-Flash-1017Llama Nemotron (Nano/Super)vsDeepSeek-V4.1-Flash-1017Frequently asked questions
Codestral 25.01 and Llama Nemotron (Nano/Super) don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Codestral 25.01 shipped 64 days before Llama Nemotron (Nano/Super), so benchmark comparisons should account for the intervening progress.
Context windows are 256k (Codestral 25.01) vs 128k (Llama Nemotron (Nano/Super)). Codestral 25.01 is closed, while Llama Nemotron (Nano/Super) is open weight.
Direct benchmark comparisons are unavailable — Codestral 25.01 and Llama Nemotron (Nano/Super) don't publish scores on any of the same benchmarks.