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. | — | 253B |
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. | 128k | 128k |
| 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 | Moonshot AI | NVIDIA |
| Release date | Jan 20 2025 | Apr 8 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
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Kimi K1.5 and Llama Nemotron Ultra 253B don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Kimi K1.5 shipped 78 days before Llama Nemotron Ultra 253B, so benchmark comparisons should account for the intervening progress.
Context windows are 128k (Kimi K1.5) vs 128k (Llama Nemotron Ultra 253B). Kimi K1.5 is closed, while Llama Nemotron Ultra 253B is open weight.
Direct benchmark comparisons are unavailable — Kimi K1.5 and Llama Nemotron Ultra 253B don't publish scores on any of the same benchmarks.