LongCat-2.0 (Meituan)
B Tier · 7.9/10
Meituan's open-source 1.6T-parameter MoE (~48B active) with native 1M-token context, MIT license -- trained entirely on domestic Chinese AI ASICs and revealed as the stealth 'Owl Alpha' model that had been topping OpenRouter
Score Breakdown
Benchmark Scores
Benchmarks for LongCat-2.0 (self-reported, third-party verification pending)
| Benchmark | Description | Score | |
|---|---|---|---|
| SWE-bench Pro | 59.5% | ||
| Terminal-Bench 2.1 | 70.8% | ||
| SWE-bench Multilingual | 77.3% |
Last updated: 2026-07-05
The Good and the Bad
What we like
- +Genuinely open frontier scale: 1.6T total / ~48B active per token under MIT -- the largest permissively-licensed model released to date
- +Native long context: LongCat Sparse Attention trained on hundreds of billions of tokens of 1M-context data, not a post-hoc context extension
- +Self-reported coding results in frontier territory: SWE-bench Pro 59.5 (above GPT-5.5's 58.6 and Gemini 3.1 Pro's 54.2 on the same table), Terminal-Bench 2.1 70.8
- +Proof that the domestic-Chinese-silicon stack works at frontier scale: full 35T+-token pretraining run and deployment on AI ASIC superpods with, per Meituan, no rollbacks or irrecoverable loss spikes
What could be better
- −1.6T total parameters puts self-hosting out of reach for everyone but datacenter operators -- 'open weights' here means auditable and API-competitive, not local
- −Benchmark numbers are self-published by Meituan; independent verification (Artificial Analysis etc.) still pending, and its own table shows Claude Opus 4.8 well ahead (SWE-bench Pro 69.2, Terminal-Bench 78.9)
- −Meituan is a food-delivery giant, not a dedicated AI lab -- long-term model-line commitment and Western enterprise support are unproven
- −US/EU enterprises may face procurement friction on a Chinese-trained model regardless of the MIT license
Pricing
Self-hosted (open weights)
- ✓MIT license -- commercial use permitted
- ✓Weights on Hugging Face (meituan-longcat/LongCat-2.0)
- ✓1.6T total params -- datacenter-scale deployment only
API
- ✓Pricing as reported by VentureBeat at launch -- verify on vendor platform before building
- ✓Also available via OpenRouter (previously as stealth 'Owl Alpha')
System Requirements
Hardware needed to self-host. Min = smallest viable setup (usually heavy quantization). Max = full-precision / production-grade.
| Model variant | Min | Max |
|---|---|---|
| LongCat-2.0 (1.6T MoE, ~48B active)Not practical to self-host below datacenter scale -- consume via API or OpenRouter instead. | Datacenter multi-node GPU/ASIC cluster | ASIC superpod (Meituan's own deployment) |
Known Issues
- LAUNCH (2026-06-30): Meituan open-sourced LongCat-2.0 -- 1.6T-parameter MoE (~48B activated per token), MIT license, native 1M-token context via LongCat Sparse Attention. Trained on 35T+ tokens entirely on domestic Chinese AI ASIC 'superpods' (no Nvidia), which is the geopolitical headline. Revealed as the stealth 'Owl Alpha' model that had been leading OpenRouter rankings. Self-reported benchmarks: SWE-bench Pro 59.5, Terminal-Bench 2.1 70.8, SWE-bench Multilingual 77.3. API pricing per VentureBeat: $0.75/$2.95 per 1M tokens in/out. Weights verified live on Hugging Face (safetensors present) as of 2026-07-05Source: Hugging Face model card (huggingface.co/meituan-longcat/LongCat-2.0), VentureBeat, SiliconANGLE (2026-06-30) · 2026-06-30
Best for
Teams that want frontier-class open weights for audit, fine-tuning research, or API use at aggressive pricing -- especially agentic-coding workloads where its SWE-bench Pro showing and 1M context matter.
Not for
Anyone hoping to run it locally (1.6T params is datacenter-only), or enterprises that need vendor-grade support and independently verified benchmarks before adopting.
Our Verdict
LongCat-2.0 is the most consequential open-weights release since GLM-5.2 -- not because it beats the closed frontier (Meituan's own table shows Opus 4.8 comfortably ahead) but because of what it represents: a 1.6T-parameter MIT-licensed model trained end-to-end on Chinese ASICs, from a company best known for food delivery, quietly topping OpenRouter under a pseudonym before launch. If you consume models by API, the reported $0.75/$2.95 pricing makes it worth benchmarking against your current stack. If you're tracking the open-vs-closed race, this is the data point that says frontier-scale training no longer requires Nvidia -- or a US lab.
Sources
- Hugging Face: meituan-longcat/LongCat-2.0 model card (accessed 2026-07-05)
- VentureBeat: Meituan open-sources LongCat-2.0 (accessed 2026-07-05)
- SiliconANGLE: Meituan open-sources massive LongCat-2.0 (accessed 2026-07-05)
Explore more LongCat-2.0 (Meituan) rankings
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