LongCat-2.0 and GLM-5.2: Open-Weight MoE Flagships Compared
A source-based comparison of LongCat-2.0 (1.6T MoE, ~48B active, MIT License, $0.30/M input) and GLM-5.2 (744B MoE, ~40B active, MIT License, $1.40/M input). Covers architecture, pricing, licensing, coding benchmarks, and deployment.
Independent third-party resource. Not affiliated with or endorsed by LongCat, Meituan, DeepSeek, or any other publisher discussed on this page.
Published: 2026-08-06
This is an independent, source-based comparison of LongCat-2.0 (Meituan) and GLM-5.2 (Zhipu AI) — two open-weight, MIT-licensed Chinese MoE flagships optimized for domestic hardware. It compares documented architecture, pricing, licensing, and deployment options. It does not report performance as a verdict — for publisher-reported scores, see the individual model pages and benchmark index linked below.
| Dimension | LongCat-2.0 | GLM-5.2 |
|---|---|---|
| Developer | Meituan (LongCat team) | Zhipu AI (Z.ai) |
| Total parameters | 1.6T (MoE) | 744B (MoE) |
| Active parameters | ~48B per token (33B—56B dynamic) | ~40B per token |
| Architecture | ScMoE + LSA sparse attention + N-gram Embedding | MoE with IndexShare sparse attention + MTP |
| Context window | 1M tokens (native) | 1M tokens (lossless) |
| Max output | 128K tokens | 131K tokens |
| Modalities | Text | Text (vision in separate GLM-5V line) |
| API pricing (per 1M tokens) | $0.30 input / $1.20 output (OpenRouter) | $1.40 input / $4.40 output (Z.ai API) |
| Cache pricing | Free (cache hits not billed) | $0.26/M cached input |
| Cost ratio (relative) | 1× | ~4.7× input, ~3.7× output |
| License | MIT License (open-source weights) | MIT License (open weights) |
| Reasoning mode | Dynamic (MOPD distilled, adaptive) | Two effort levels: High / Max |
| Domestic hardware support | Trained end-to-end on 50,000 domestic AI ASICs | Day-0 inference adaptation on 8+ domestic chip platforms |
| Self-hosted deployment | Yes — SGLang, vLLM, Transformers, NPU path | Yes — open weights; GLM Coding Plan subscription |
Scale and Efficiency
LongCat-2.0 (1.6T total, ~48B active) is the larger of the two models. Its architecture — Shortcut-connected MoE, Zero- Computation Experts, LSA sparse attention, N-gram Embedding — was designed to make a trillion-parameter model trainable and inferable end-to-end on domestic AI ASICs.
GLM-5.2 (744B total, ~40B active) is Zhipu's flagship open-weight model. Its efficiency story centers on IndexShare, a technique that reuses a single indexer across sparse attention layers to cut per-token FLOPs roughly 2.9× at 1M context, plus an improved MTP layer for speculative decoding. The publisher reports the model achieved Day-0 inference adaptation on eight or more domestic chip platforms.
Pricing and Access
On metered API pricing, LongCat-2.0 is substantially cheaper: $0.30/M input and $1.20/M output via OpenRouter, versus GLM-5.2's $1.40/M and $4.40/M on the Z.ai API — roughly 4.7× and 3.7× gaps. LongCat-2.0 does not bill cache hits; GLM-5.2 charges $0.26/M for cached input.
Zhipu additionally offers the GLM Coding Plan, a flat monthly subscription starting around $18/month that includes GLM-5.2 access — a structure that suits frequent coding workloads but consumes quota at up to three times the normal rate during peak hours. Both models can be self-hosted, which changes the cost calculus for organizations with infrastructure.
Licensing: Both MIT, Different Postures
Both models are released under the MIT License. One nuance: GLM-5.2 is open-weight — the weights are MIT-licensed and downloadable, but the publisher has not released training code or data recipes. LongCat-2.0's license is also MIT, and its publisher documents the model more fully (architecture, training details, hardware). Neither license restricts commercial use, modification, or redistribution.
Benchmark Positioning (Publisher-Reported)
Zhipu reports GLM-5.2 as the first open model to top the Artificial Analysis Intelligence Index (51) and the first open model to beat GPT-5.5 on SWE-bench Pro (62.1% vs 58.6%). LongCat-2.0's publisher reports its own scores, including strong agentic-coding positions and top-tier OpenRouter adoption. These claims come from each vendor's documentation; this site has not independently verified either set.
Deployment and Ecosystem
Both models are designed for domestic-hardware deployment — LongCat-2.0 with a documented NPU inference path and SGLang/ vLLM support, GLM-5.2 with Day-0 adaptation across Ascend, T-Head, Moore Threads, Cambricon, Kunlunxin, Muxi, Hygon, Biren, and other domestic platforms. Both expose OpenAI- compatible APIs and integrate with mainstream agent tools. LongCat-2.0 additionally offers an Anthropic-compatible endpoint; GLM-5.2 is documented with Claude Code, Cline, OpenClaw, Goose, and Roo.
This comparison is based on publicly available publisher documentation accessed on 2026-08-06. LongCat-2.0 specifications are from the publisher blog and GitHub repository. GLM-5.2 specifications are from Zhipu's launch announcement, the HuggingFace model card, and the Z.ai API documentation.
This is a feature-level comparison, not a performance evaluation. This site has not independently tested either model. Benchmark scores published by either vendor have not been independently verified.
Pricing is current as of the access date and may change. GLM-5.2 is open-weight (MIT weights) but training code and data are not released. Verify current terms on each vendor's official documentation before deployment.
Related pages
- LongCat-2.0 model profile
Full technical brief covering architecture, training, benchmarks, and deployment options.
- LongCat-2.0 vs Kimi K3 comparison
The other major open-weight Chinese flagship comparison on this site.
- LongCat-2.0 vs DeepSeek V4-Flash comparison
The cost-efficiency comparison in the same Chinese open-weight tier.
- LongCat-2.0 Publisher-Reported Benchmark Scores
The benchmark data cited in this comparison with source attribution.
Sources
- LongCat-2.0 Publisher Blog Post
Publisher documentationAccessed 2026-08-06
Publisher announcement detailing LongCat-2.0 architecture (ScMoE, LSA, N-gram Embedding, MOPD), 1.6T parameters, domestic-ASIC training, and MIT License.
- LongCat-2.0 GitHub Repository
Primary sourceAccessed 2026-08-06
Model code, weights, MIT License terms, and deployment instructions.
- GLM-5.2 Launch and Open-Source Announcement
Publisher documentationPublished 2026-06-17Accessed 2026-08-06
Zhipu announcement: GLM-5.2 open-sourced under MIT, 1M context, IndexShare sparse attention, Day-0 adaptation to eight domestic AI chip platforms.
- GLM-5.2 Model Card (HuggingFace)
Primary sourceAccessed 2026-08-06
Model card with architecture details, 744B-A40B parameter profile, benchmark table, and license file.
- GLM-5.2 API Pricing (Z.ai)
Publisher documentationAccessed 2026-08-06
Official API pricing: $1.40/M input, $4.40/M output, $0.26/M cached input, plus the GLM Coding Plan subscription option.
Independent third-party disclosure
This page is published by an independent third-party site. It is not affiliated with, endorsed by, sponsored by, or operated by Meituan, LongCat, or any of their affiliates. The content summarizes publicly-available primary documentation and does not represent the views of any referenced organization.
Last reviewed: 2026-08-06