Compare AI models
| Specifications | ||
ParametersA rough measure of how big the model is. More parameters usually means more capable and more expensive to run, though it is a poor guide on its own — a smaller, newer model often beats a larger, older one. | 24B | 253B |
| API pricingUSD per 1M tokens · lower wins | ||
Cheapest inputLowest input rate across third-party providers, excluding the lab itself. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | $0.075DeepInfra | — |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $0.20DeepInfra | — |
These models have no shared benchmark scores. | ||
LiveCodeBenchCompetitive coding — Coding problems published so recently the AI can't have seen them in training — a contamination-free test of raw programming skill. Higher is better. | — | 66.3% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 76% |
| Overview | ||
| Company | Mistral | NVIDIA |
| Release date | Jun 20 2025 | Apr 8 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
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Mistral Small 3.2 and Llama Nemotron Ultra 253B don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Llama Nemotron Ultra 253B shipped 73 days before Mistral Small 3.2, so benchmark comparisons should account for the intervening progress.
Mistral Small 3.2 has 24B parameters, while Llama Nemotron Ultra 253B has 253B.
Direct benchmark comparisons are unavailable — Mistral Small 3.2 and Llama Nemotron Ultra 253B don't publish scores on any of the same benchmarks.