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. | 22B | 70B |
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. | 32k | 128k |
| Overview | ||
| Company | Mistral | NVIDIA |
| Release date | May 29 2024 | Oct 15 2024 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Codestral 22BvsClaude Haiku 5.5Llama 3.1 Nemotron 70BvsClaude Haiku 5.5Codestral 22BvsGPT-6.1 SolLlama 3.1 Nemotron 70BvsGPT-6.1 SolCodestral 22BvsGemini 4 ArgonLlama 3.1 Nemotron 70BvsGemini 4 ArgonCodestral 22BvsMuse Spark OpenLlama 3.1 Nemotron 70BvsMuse Spark OpenCodestral 22BvsGrok 4.7Llama 3.1 Nemotron 70BvsGrok 4.7Codestral 22BvsDeepSeek-V4.1-Flash-1017Llama 3.1 Nemotron 70BvsDeepSeek-V4.1-Flash-1017Frequently asked questions
Codestral 22B and Llama 3.1 Nemotron 70B don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Codestral 22B shipped 139 days before Llama 3.1 Nemotron 70B, so benchmark comparisons should account for the intervening progress.
Codestral 22B has 22B parameters, while Llama 3.1 Nemotron 70B has 70B. Context windows are 32k (Codestral 22B) vs 128k (Llama 3.1 Nemotron 70B). Codestral 22B is closed, while Llama 3.1 Nemotron 70B is open weight.
Direct benchmark comparisons are unavailable — Codestral 22B and Llama 3.1 Nemotron 70B don't publish scores on any of the same benchmarks.