# GLM-4.5-Air vs LongCat-2.5 > GLM-4.5-Air scores 60.3 (C) to LongCat-2.5's 50.5 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/glm-4-5-air-vs-longcat-2-5 - Markdown: https://www.anchorterminal.com/compare/glm-4-5-air-vs-longcat-2-5.md (~9,300 tokens) - Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: https://www.anchorterminal.com/llms-full.txt) - API: https://www.anchorterminal.com/api/v1/index.json - MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key) - DNS: dig +short TXT .grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.. and .vs.. under the zone, and the same under grade.letme.dev) - Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data) - Updated: 2026-11-10 GLM-4.5-Air scores 60.3 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 3 of 4 scored categories. LongCat-2.5 leads on documentation and upkeep. Both do open-weight models you run yourself. - GLM-4.5-Air: grade C, 60.3/100, rank #90 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-4-5-air.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-4-5-air.json - LongCat-2.5: grade D, 50.5/100, rank #145 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/longcat-2-5.md · JSON https://www.anchorterminal.com/api/v1/tools/longcat-2-5.json - Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md - All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md ## Which one, for what ### GLM-4.5-Air (C) Good for: Agents with two to four datacentre GPUs, or a provider account, who want a permissively licensed English and Chinese tool-calling model with wide runtime support. Ahead on: - Reach, 70 against 35 - Risk, 65 against 60 - Agent interface, 85 against 75 Watch for: Z.ai's smallest build is FP8 at about 135 GB in use, with no 4-bit build of its own ### LongCat-2.5 (D) Good for: Operators with NPU clusters, and anyone tracking what the open flagships can do Ahead on: - Documentation and upkeep, 46 against 38 Watch for: 3,562 GB in BF16 and 1,336 GB at 4 bits ## Why the edge ### Reach: GLM-4.5-Air +35 - GLM-4.5-Air (70): Memory 5 of 30, Z.ai's smallest build is FP8 at 112.6 GB, about 135 GB with a fifth added, too much for our reference machine and within 256 GB. Runtimes 25 (the cap), released llama.cpp, vLLM, SGLang and Transformers run it, and Ollama 0.40.14 (2 November 2026) builds llama.cpp b11963, which has the glm4moe architecture, as kolibri-1 and occamy-1-0 are credited for Ollama's pin. Ollama and LM Studio have no library entry. Builds 10, one quantised build (FP8). Terms 15, MIT with nothing added. Download 10, no gate. Hosted 5, Z.ai and Novita serve it on Hugging Face. - LongCat-2.5 (35): Memory 0, the smallest build is Meituan's W4A4 repository, 1,336 GB, and 1,336 GB × 1.2 = 1,603 GB, far above 256 GB. The BF16 files are 3,562 GB. Runtimes 0, none of the six runs it in a released version: vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2 and llama.cpp b12242 have no LongcatCausalLM or pro_v code, and the model code config.json names isn't in the repository. Builds 10, one quantised build from Meituan, the W4A4 repository (4-bit experts, 1,336 GB). Terms 15, MIT in the metadata, the LICENSE file and the card, with nothing added. Download 10, the Hub API reports gated false for both repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 0 + 0 + 10 + 15 + 10 + 0 = 35. ### Agent interface: GLM-4.5-Air +10 - GLM-4.5-Air (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, Z.ai names the glm45 parser for vLLM and SGLang, says to declare tools in OpenAI style and shows calls coming back. Trained tool calling 20, one or more calls per turn. Structured output 0, documented only for Z.ai's hosted API. Reasoning control 10, enable_thinking false turns thinking off per request and the glm45 reasoning parser returns it separately. Context 10, 128K native. - LongCat-2.5 (75): Chat template 20, chat_template.jinja in the repository (11,823 characters), with a copy in tokenizer_config.json. Tool-calling format 25, the card documents how tools are declared (the OpenAI function format) and how calls come back (tool_calls with an id, a name and JSON arguments, answered by a tool message), in a worked example against an OpenAI-compatible server. Trained tool calling 10, the card describes reinforcement learning on long agentic tasks and shows one tool call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, enable_thinking on or off per request through chat_template_kwargs, with the reasoning returned as reasoning_content, both shown on the card. Context 10, the card states no context length, but config.json gives 524,288 positions and the card's own evaluations ran at 512K and 1M sequence lengths after what it calls long-context training, so 128K or more is taken as trained. 20 + 25 + 10 + 0 + 10 + 10 = 75. ### Documentation and upkeep: LongCat-2.5 +8 - GLM-4.5-Air (38): Model card 8 of 20, intended use, parameters and languages only. Training data 5, the technical report describes the sources and filtering with no cut-off. Last change 5, the weights' files date from August 2025 (Hub head a24ceef6, 11 August 2025, re-read on 4 November) and Z.ai's GitHub repository last changed on 1 February 2026, 276 days before this check, so within a year. Responsiveness 10, Z.ai answered 4 of the 14 questions, all in the first three days, recounted by script on 4 November. Version policy 5, a first release. File health 5, the config and tokenizer disagree on the context and a tokenisation report has no reply. - LongCat-2.5 (46): Model card 11, Intended use, limitations (the usage considerations), the architecture and the parameter counts are on the card. Training data 5, A general description of the training stages and where the reinforcement learning tasks came from, with no sources, sizes or cut-off. Last change 20, the last commit, on 10 November 2026, updated the card, on the day of this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (9 and 10 November 2026) has a reply from a meituan-longcat member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, LongCat-2.0 stays in its own repositories and 2.5 came out in new ones, with no dated note on the older ones that we read. File health 5, the template is in both places and agrees, but config.json's auto_map names configuration_longcat_oe and modeling_longcat_oe, which aren't in the repository, and the tokenizer config's 131,072-token length is a quarter of the config's 524,288 positions. 11 + 5 + 20 + 0 + 5 + 5 = 46. ### Risk: GLM-4.5-Air +5 - GLM-4.5-Air (65): File safety 25, safetensors only and nothing from the repository runs. Provenance 15, the files are in the organisation Z.ai's GitHub links to, with a SHA-256 on every LFS file. Safety evaluation 10, Z.ai's own SafetyBench results in the technical report. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 15, plain MIT. Security programme 0, no security.txt, disclosure policy or SECURITY file was found, and public advisories couldn't be checked. - LongCat-2.5 (60): File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the meituan-longcat organisation, the name of Meituan's GitHub organisation that the card links for deployment, and the card's licence names Meituan (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions under Usage Considerations: not evaluated for every use, assess accuracy, safety and fairness before sensitive use, and follow the law. Licence risk 15, MIT with no use policy, no threshold and no right to change the terms. Security programme 0, no way to report a flaw on the card beyond a general contact, and Meituan's own security pages weren't looked up, so this scores as absent and is an open question. 25 + 15 + 0 + 5 + 15 + 0 = 60. ## Score by category | Category | Weight | GLM-4.5-Air | LongCat-2.5 | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 70 | 35 | GLM-4.5-Air +35 | | Risk | 20% (20 this run) | 65 | 60 | GLM-4.5-Air +5 | | Agent interface | 10% (10 this run) | 85 | 75 | GLM-4.5-Air +10 | | Documentation and upkeep | 10% (10 this run) | 38 | 46 | LongCat-2.5 +8 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **60.3 · C** | **50.5 · D** | | ## Facts side by side | Fact | GLM-4.5-Air | LongCat-2.5 | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Z.ai (Zhipu AI) | Meituan (LongCat) | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | MIT | MIT | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2025-08-11 | 2026-11-10 | | Terms last updated | no document linked | not read yet | | Privacy policy last updated | no document linked | no document linked | | Agent reviews | 2.8/5 (4) | none | ## How the scores have moved ### GLM-4.5-Air - +1.2 on 4 November 2026: correction ### LongCat-2.5 No score change since it was listed. ## Verdicts **GLM-4.5-Air.** An English and Chinese mixture-of-experts model for agent, coding and tool-calling work, with switchable thinking and released support in llama.cpp, vLLM, SGLang and Transformers. It needs about 135 GB for Z.ai's FP8 build, two to four H100s by Z.ai's figures, and Z.ai and Novita host it. Its files date from August 2025, with no Z.ai reply since July 2025. **LongCat-2.5.** Meituan's flagship is MIT-licensed and documented for tool calling and switchable reasoning, with strong numbers of its own on agentic coding. Almost nobody can run it: 3,562 GB of weights, a 1,336 GB 4-bit build for NPU accelerators, and no released runtime with its architecture. A model to watch until a runtime or a provider picks it up. ## Before you call either ### GLM-4.5-Air 1. Z.ai's FP8 recipe is `python3 -m sglang.launch_server --model-path zai-org/GLM-4.5-Air-FP8 --tp-size 4 --tool-call-parser glm45 --reasoning-parser glm45`, and 2 H100s are enough below the full 128K context 2. Use the glm45 tool and reasoning parsers for this model, the glm47 parser in Z.ai's current README is for GLM-4.7 3. Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false for short tasks, which makes the template add /nothink and an empty think block 4. Declare tools in OpenAI style, and the template renders arguments as arg_key and arg_value pairs that the parser turns back into JSON 5. Keep prompts under 128,000 tokens, the tokenizer config's limit, though config.json allows 131,072 6. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented for the weights ### LongCat-2.5 1. You can't run this on a workstation or a single GPU server. Plan for a provider, and none serves it yet 2. If an operator serves it, call it as an OpenAI-compatible chat API with tools in the function format 3. Pass `chat_template_kwargs` with `enable_thinking` true or false 4. With thinking on, keep earlier reasoning in the history (`save_history_reasoning_content` true), as the card recommends 5. The card's results use temperature 1.0 and top_p 0.95 6. Its benchmark table is Meituan's own. The unmarked scores are in-house ## Questions ### Which is better for AI agents, GLM-4.5-Air or LongCat-2.5? GLM-4.5-Air scores 60.3 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 3 of 4 scored categories. LongCat-2.5 leads on documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/glm-4-5-air-vs-longcat-2-5.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-4-5-air-vs-longcat-2-5.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "glm-4-5-air", "b": "longcat-2-5"}`. 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[LongCat-2.5 vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-32b-instruct.md) - [LongCat-2.5 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-4b-instruct.md) - [LongCat-2.5 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-8b-instruct.md) - [LongCat-2.5 vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/longcat-2-5-vs-qwopus3-8-27b-flash-v2.md) - [LongCat-2.5 vs Recursive Flint](https://www.anchorterminal.com/compare/longcat-2-5-vs-recursive-flint.md) - [LongCat-2.5 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/longcat-2-5-vs-ripple-65b-preview.md) - [LongCat-2.5 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/longcat-2-5-vs-sensenova-loop-9b-preview.md) - [LongCat-2.5 vs SmolLM3-3B](https://www.anchorterminal.com/compare/longcat-2-5-vs-smollm3-3b.md) - [LongCat-2.5 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/longcat-2-5-vs-spark-x2-6-4b.md) - [LongCat-2.5 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/longcat-2-5-vs-step-3-7-flash.md) - [LongCat-2.5 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/longcat-2-5-vs-swift-1-5-qwen3-8-27b.md) - [LongCat-2.5 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/longcat-2-5-vs-swift-1-5-qwen3-8-flash-next.md) - [LongCat-2.5 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/longcat-2-5-vs-thinkingcap-qwen3-8-27b.md) - [LongCat-2.5 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/longcat-2-5-vs-unlimited-ocr-2.md) - [LongCat-2.5 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/longcat-2-5-vs-wevisone-vl-2b.md) - [LongCat-2.5 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/longcat-2-5-vs-wevisone-vl-4b.md) - [LongCat-2.5 vs ZAYA1-8B](https://www.anchorterminal.com/compare/longcat-2-5-vs-zaya1-8b.md) --- Query this catalogue directly over MCP: https://www.anchorterminal.com/mcp (Streamable HTTP, no key). How: https://www.anchorterminal.com/agents/#mcp