# GLM-4.5-Air vs North Mini Code 1.0 > North Mini Code 1.0 and GLM-4.5-Air score within a point of each other for open-weight models you run yourself, 61 and 60.3 out of 100. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/glm-4-5-air-vs-north-mini-code-1-0 - Markdown: https://www.anchorterminal.com/compare/glm-4-5-air-vs-north-mini-code-1-0.md (~8,900 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 North Mini Code 1.0 and GLM-4.5-Air score within a point of each other on agent readiness, 61 (C) and 60.3 (C). GLM-4.5-Air leads on risk. 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 - North Mini Code 1.0: grade C, 61/100, rank #80 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/north-mini-code-1-0.md · JSON https://www.anchorterminal.com/api/v1/tools/north-mini-code-1-0.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: - Risk, 65 against 60 Watch for: Z.ai's smallest build is FP8 at about 135 GB in use, with no 4-bit build of its own ### North Mini Code 1.0 (C) Good for: Developers who want a coding agent on their own GPU with a permissive licence Ahead on: - Reach, 85 against 70 Watch for: No limitations, safety results, languages or data description on the card ## Why the edge ### Reach: North Mini Code 1.0 +15 - 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. - North Mini Code 1.0 (85): Memory 25, the smallest build from Cohere is the w4a16 repository, 19.38 GB, and 19.38 GB × 1.2 = 23.3 GB, within 24 GB. Unsloth's GGUF isn't read for this line. Runtimes 20, Released vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 have the cohere2_moe model, and llama.cpp b12242 has the cohere2moe architecture. Builds 15, Quantised builds from Cohere at two precisions, FP8 and 4-bit weights (w4a16), each in its own repository. Terms 15, Apache 2.0 in the metadata and on the card, an OSI licence with nothing added. Download 10, the Hub API reports gated false for the model and its builds 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. 25 + 20 + 15 + 15 + 10 + 0 = 85. ### 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. - North Mini Code 1.0 (60): File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in Cohere's verified CohereLabs organisation, which cohere.com links to per our other Cohere listings (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on prompt injection, instruction priority or what the model refuses. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 5, Cohere's trust centre holds a responsible disclosure policy with a bug bounty, as recorded on our Cohere listings (5). 25 + 15 + 0 + 0 + 15 + 5 = 60. ## Score by category | Category | Weight | GLM-4.5-Air | North Mini Code 1.0 | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 70 | 85 | North Mini Code 1.0 +15 | | Risk | 20% (20 this run) | 65 | 60 | GLM-4.5-Air +5 | | Agent interface | 10% (10 this run) | 85 | 85 | even | | Documentation and upkeep | 10% (10 this run) | 38 | 36 | GLM-4.5-Air +2 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **60.3 · C** | **61 · C** | | ## Facts side by side | Fact | GLM-4.5-Air | North Mini Code 1.0 | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Z.ai (Zhipu AI) | Cohere | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | MIT | Apache-2.0 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2025-08-11 | 2026-07-29 | | Terms last updated | no document linked | no document linked | | 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 ### North Mini Code 1.0 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. **North Mini Code 1.0.** A coding agent model that fits one 24 GB GPU at 4 bits, trained for tool use, with a 256K context, Apache 2.0 and four released runtimes. It's a research release and reads like one: nothing on limitations, safety or data, the card's run instructions are out of date, and Cohere hasn't answered a question on the repository. ## 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 ### North Mini Code 1.0 1. Serve it with vLLM 0.33.0 or later and `--tool-call-parser cohere_command4 --reasoning-parser cohere_command4 --enable-auto-tool-choice`, with `cohere_melody` installed 2. Pass the model's thinking back with its tool calls on every turn. The card says results drop without it 3. Describe tools in JSON schema and append tool results as a dictionary with the tool role 4. Use temperature 1.0 and top_p 0.95 5. For llama.cpp the GGUF files are Unsloth's or bartowski's conversions, not Cohere's 6. It's text only. Don't send images ## Questions ### Which is better for AI agents, GLM-4.5-Air or North Mini Code 1.0? North Mini Code 1.0 and GLM-4.5-Air score within a point of each other on agent readiness, 61 (C) and 60.3 (C). GLM-4.5-Air leads on risk. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/glm-4-5-air-vs-north-mini-code-1-0.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-4-5-air-vs-north-mini-code-1-0.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "glm-4-5-air", "b": "north-mini-code-1-0"}`. 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