LongCat-2.0 vs Chinese Open-Source LLMs
Side-by-side parameters, benchmarks, and links to detailed comparisons with DeepSeek V4-Pro, Qwen3.6 Plus, Kimi K2.6, and GLM-5.1.
Comparison overview
Each model in China's open-source camp targets a different sweet spot. Use this table to match your workload — agentic coding, terminal tasks, long sessions, or frontend/web agents — before diving into head-to-head pages.
| Model | Params / Active | Context | Strength | Positioning | Key Scores |
|---|---|---|---|---|---|
| LongCat-2.0 | 1.6T / 33–56B (avg ~48B) | 1M | Agentic coding + full domestic-chip training & deployment | Trillion-scale MoE built for software agents | SWE-bench Pro 59.5 |
| DeepSeek V4-Pro | 1.6T total / 4.9B active | 1M | Long context + heavy reasoning | Ultra-sparse MoE for reasoning-heavy coding | SWE-Bench Verified 80.6; LiveCodeBench 93.5; Codeforces 3206 |
| Qwen3.6 Plus | Undisclosed | 1M | Terminal tasks + cost efficiency | Balanced agent for terminal & value | Terminal-Bench 2.0 61.6% (vs Claude Opus 4.6 59.3%); SWE-Bench Verified 78.8% |
| Kimi K2.6 | 1T / 32B active, 384 experts | 256K | Ultra-long session stability | Marathon agent sessions with sustained tool use | Terminal-Bench 2.0 66.7%; 13h session, 4,000+ tool calls |
| GLM-5.1 | 754B (MoE routing) | Undisclosed | Frontend / Web Agent | Web & UI agent specialist | SWE-Bench Pro 58.4%; Code Arena Elo 1530 (#3 global web-agent leaderboard) |
Scores sourced from public reports and independent evaluators (Fello AI, Atlas Cloud, AceCloud). Benchmarks differ in task design — compare like-for-like suites when making decisions.
Four Differences Worth Knowing
Domestic Chip Training
LongCat-2.0 is the first trillion-parameter model with end-to-end training and deployment on domestic chips. DeepSeek V4-Pro uses Huawei chips for inference only — pretraining still runs on NVIDIA hardware.
Read analysis →API Pricing
LongCat-2.0 standard API: $0.75 / $2.95 per million input/output tokens; promo $0.30 / $1.20 with free cached-context reads. Undercuts GLM-5.1 ($1.40/M+) and Kimi K2.6 ($0.95/M+) on many workloads.
Cost table →The Owl Alpha Story
Before its reveal, LongCat-2.0 operated anonymously as Owl Alpha on OpenRouter — ~10.1T tokens in one month, #1 on Hermes Agent, #2 in Claude Code deployments.
Full timeline →Filling Benchmark Gaps
Third-party tools like BenchLM still lack side-by-side benchmark data for many Chinese models. We track reproducible eval gaps and plan to publish open comparisons.
Gap analysis →Head-to-Head Comparisons
- LongCat-2.0 vs DeepSeek V4-Pro — agentic coding vs reasoning-first sparse MoE
- LongCat-2.0 vs Qwen3.6 Plus — 1M context agents, terminal & SWE benchmarks
- LongCat-2.0 vs Kimi K2.6 — 1M vs 256K context, session stability
- LongCat-2.0 vs GLM-5.1 — general agentic coding vs frontend/web agents