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A head-to-head benchmark comparison of LongCat-Flash across 13 evaluations.
We introduce LongCat-Flash, a 560-billion-parameter Mixture-of-Experts (MoE) language model designed for both computational efficiency and advanced agentic capa.
Full LongCat-Flash specs →| Benchmark | LongCat-Flash |
|---|---|
| agentic | |
| Terminal-Bench 2.0 | 39.51% |
| code | |
| HumanEval+ | 67.8% |
| LiveCodeBench | 48.02% |
| MBPP+ | 81.27% |
| SWE-bench Verified | 60.4% |
| general | |
| IFEval | 89.65% |
| MMLU | 89.71% |
| MMLU-Pro | 82.68% |
| math | |
| AIME 2024 | 70.42% |
| AIME 2025 | 61.25% |
| MATH-500 | 96.4% |
| reasoning | |
| GPQA Diamond | 73.23% |
| ZebraLogic | 89.3% |
Best result per row highlighted in cyan. Each benchmark links to its definition and sources; each model links to its full scorecard.
LongCat-Flash has the strongest result on HumanEval+ among the models compared here. See the code-category rows in the table for the full picture.
This comparison covers 13 benchmarks on which at least one of the selected models has a published score.
Scores are aggregated from official model cards, technical reports and standard public evaluations, and link back to each benchmark's source. They are updated as new results are published.
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