The official model card is the primary authoritative source for judging LongCat-2.0's suitable tasks: it scores 59.5 on SWE-bench Pro, ahead of GPT-5.5 (58.6) and Gemini 3.1 Pro (54.2), and reaches 70.8 on Terminal-Bench 2.1. However, it trails GPT-5.5 and Claude Opus 4.8 on several benchmarks including BrowseComp, GPQA, and IFEval—in short, it is strong at coding and agent tasks, but not a leader in retrieval and general reasoning.
| Benchmark | LongCat-2.0 | Gemini 3.1 Pro | GPT-5.5 | Claude Opus 4.6 | Claude Opus 4.7 | Claude Opus 4.8 |
|---|---|---|---|---|---|---|
| Terminal-Bench 2.1 | 70.8 | 70.7* | 73.8* | - | 71.7* | 78.9* |
| SWE-bench Pro | 59.5 | 54.2* | 58.6* | 57.3* | 64.3* | 69.2* |
| SWE-bench Multilingual | 77.3 | 76.9* | - | 77.8* | 80.5* | 84.8* |
| FORTE (General Agent) | 73.2 | 70.3 | 77.8 | 73.2 | 77.6 | 77.2 |
| BrowseComp | 79.9 | 85.9* | 84.4* | 84.0* | 79.3* | 84.3* |
| RWSearch | 78.8 | 76.3 | 85.3 | 81.3 | 79.3 | 77.3 |
| IFEval | 90.0 | 96.1 | 95.0 | 92.2 | 88.7 | 86.0 |
| Writing Bench | 83.8 | 83.7 | 84.7 | - | 85.3 | 85.2 |
| IMO-AnswerBench | 81.8 | 90.0 | 79.5 | 75.3* | 81.8 | 75.3 |
| GPQA-diamond | 88.9 | 94.3* | 93.6* | 91.3* | 94.2* | 92.4* |
Note: * = cited from the corresponding model's official report; - = no publicly comparable score.
Architecture: MoE, 1.6T total parameters, about 48B active per token (dynamic 33B–56B, according to the official X account); HF metadata lists a 1.8T model size (including 135B N-gram Embedding parameters, BF16).
Context: Native 1M tokens (trained on hundreds of billions of tokens of million-token-context data).
Key features: LongCat Sparse Attention (LSA, with SI/CLI/HI optimizations), 3-step MTP speculative decoding, and N-gram Embedding (n-gram size=5, 135B parameters).
Training: 35T+ tokens and more than 50,000 domestic compute chips (AI ASIC superpods), with no rollback throughout (according to the official blog).
Supported harnesses: Claude Code, OpenClaw, Hermes (officially described as “deeply integrated”).
License: MIT.
Official experience entry point: https://longcat.ai; contact longcat-team@meituan.com.
Methodology warning: All scores are Meituan self-tests (a self-built harness and in-house scoring; the model card notes “problematic tasks corrected”—AlphaSignal points out that this means problematic tasks were manually corrected before scoring). Scores marked * come from each provider's official report, so they are not directly comparable across laboratories; as of 2026-07-01, no independent third party (Artificial Analysis, Scale, etc.) had published a retest score for LongCat-2.0 (according to AlphaSignal's original text).
Relative strengths and weaknesses (consistent across multiple independent interpretations):
Areas ahead: SWE-bench Pro 59.5 > GPT-5.5 58.6 and > Gemini 3.1 Pro 54.2; Terminal-Bench 2.1 is tied with Gemini 3.1 Pro (70.8 vs. 70.7); RWSearch and FORTE exceed Gemini 3.1 Pro.
Areas behind: FORTE/RWSearch/BrowseComp all trail GPT-5.5 (77.8/85.3/84.4 vs. 73.2/78.8/79.9); GPQA-diamond and IFEval trail GPT-5.5 and Gemini 3.1 Pro; Claude Opus 4.8 leads LongCat by nearly 10 points on SWE-bench Pro (69.2).
Overall: In the official chart, the only model LongCat-2.0 consistently beats is Gemini 3.1 Pro (AlphaSignal's conclusion); against GPT-5.5 it narrowly wins only on SWE-bench Pro.
Task-fit judgment: Suitable for repository-level coding, terminal interaction, and long-context agent workflows; unsuitable for scenarios requiring top-tier retrieval (BrowseComp), scientific reasoning (GPQA), and strict instruction following (IFEval).
Related documents: Review 02 (official technical blog) and Review 03 (OpenRouter third-party methodology).
LongCat 2.0
