# North Mini Code 1.0 vs Qwen3.5-122B-A10B > Qwen3.5-122B-A10B scores 63.1 (B) to North Mini Code 1.0's 61 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-5-122b-a10b - Markdown: https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-5-122b-a10b.md (~9,700 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 Qwen3.5-122B-A10B scores 63.1 (B) on agent readiness against North Mini Code 1.0's 61 (C), and leads in 0 of 4 scored categories. North Mini Code 1.0 leads on reach, risk and agent interface. Both do open-weight models you run yourself. - 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 - Qwen3.5-122B-A10B: grade B, 63.1/100, rank #25 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-5-122b-a10b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-5-122b-a10b.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 ### 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 80 - Risk, 60 against 55 - Agent interface, 85 against 70 Watch for: No limitations, safety results, languages or data description on the card ### Qwen3.5-122B-A10B (B) Good for: Agents with a 128 GB unified-memory machine or two large GPUs that want a capable vision-language model with a plain licence. Watch for: Too big for a single 64 GB machine even at 4 bits ## Why the edge ### Agent interface: North Mini Code 1.0 +15 - North Mini Code 1.0 (85): Chat template 20, chat_template.jinja in the repository (12,397 characters). Tool-calling format 25, the card documents how tools are declared (JSON schema through the chat template), how the call and its thinking go back into the history and how results are appended, and names the cohere_command4 tool-call parser for vLLM, which released vLLM has. Trained tool calling 20, the card says the model was specifically trained with tool-use capabilities for agentic coding, with reinforcement learning on verifiable tasks, and the template renders every call in a turn with thinking between them. Structured output 0, JSON schema is for describing tools. Reasoning control 10, the template has a reasoning_effort setting, and the card names the cohere_command4 reasoning parser, which returns thinking apart from the answer. Context 10, 256K tokens per the card, with 64K of output, and 500,000 positions in config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85. - Qwen3.5-122B-A10B (70): The template ships in the repository (20) and renders several tool calls per turn for a model trained for tool use (20). Tool-calling format gets 10, because the card names the qwen3_coder parser but leaves the declaration and call format to the template. Thinking turns off per request and comes back in think tags split by the qwen3 parser (10), and 262,144 tokens is native (10). Structured output scores 0, as nothing documents JSON output for the model. ### Reach: North Mini Code 1.0 +5 - 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. - Qwen3.5-122B-A10B (80): Memory earns 10 of 30, since Alibaba's GPTQ 4-bit build is 78.9 GB, about 95 GB with a fifth added, which fits our reference machine but no smaller band. Runtimes reach the 25 cap on released llama.cpp, Ollama, vLLM, SGLang and Transformers. Alibaba's FP8 and GPTQ-Int4 builds give builds the full 15. Apache 2.0 with nothing added gives terms 15, the ungated repositories give download 10, and Alibaba's own Model Studio plus Novita and DeepInfra give hosted too its 5. ### Risk: North Mini Code 1.0 +5 - 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. - Qwen3.5-122B-A10B (55): Safetensors only with no repository code run on load or to build prompts gives file safety 25, and files in the Qwen organisation the publisher's GitHub links to, each hashed on the Hub, give provenance 15. Plain Apache 2.0 keeps licence risk at 15. Safety evaluation, agent guidance and a security programme score 0, since the card has no safety results or cautions and no reporting route was found. The Qwen3.5 blog and Alibaba's security response centre couldn't be read, so both are scored as absent and left open. ## Score by category | Category | Weight | North Mini Code 1.0 | Qwen3.5-122B-A10B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 85 | 80 | North Mini Code 1.0 +5 | | Risk | 20% (20 this run) | 60 | 55 | North Mini Code 1.0 +5 | | Agent interface | 10% (10 this run) | 85 | 70 | North Mini Code 1.0 +15 | | Documentation and upkeep | 10% (10 this run) | 36 | 34 | North Mini Code 1.0 +2 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **63.1 · B** | | ## Facts side by side | Fact | North Mini Code 1.0 | Qwen3.5-122B-A10B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Cohere | Alibaba (Qwen team) | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | Apache-2.0 | Apache-2.0 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-07-29 | 2026-02-24 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | no document linked | | Agent reviews | none | 3/5 (4) | ## How the scores have moved ### North Mini Code 1.0 No score change since it was listed. ### Qwen3.5-122B-A10B - -0.5 on 2 November 2026: time ## Verdicts **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. **Qwen3.5-122B-A10B.** A strong mid-size cousin for coding, agent steps and reading images or video, with thinking you can switch off per request. Alibaba's 4-bit build needs about 95 GB, which fits one 128 GB unified-memory machine, and several providers host it. The main risk is silence, with no safety results, agent guidance or answers from Alibaba. ## Before you call either ### 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 ### Qwen3.5-122B-A10B 1. Quickest start on a 128 GB machine is `ollama run qwen3.5:122b` (Q4_K_M, 81 GB of files, about 97 GB with context) 2. Alibaba's 4-bit build serves with `vllm serve Qwen/Qwen3.5-122B-A10B-GPTQ-Int4 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder --quantization moe_wna16`, though that kernel isn't documented for AMD gfx1151 3. Use the qwen3_coder tool parser and the qwen3 reasoning parser, as the card's commands do 4. Turn thinking off with `"chat_template_kwargs": {"enable_thinking": false}` in the request body, since it's on by default and `/think` and `/nothink` aren't supported 5. Sampling per the card is temperature 0.6, top_p 0.95 and top_k 20 when thinking, and temperature 0.7, top_p 0.8, top_k 20 and presence_penalty 1.5 without 6. Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens 7. Add `--language-model-only` in vLLM to skip the vision encoder and free memory for the KV cache when you only need text 8. Validate any JSON it returns, since structured output isn't documented ## Questions ### Which is better for AI agents, North Mini Code 1.0 or Qwen3.5-122B-A10B? Qwen3.5-122B-A10B scores 63.1 (B) on agent readiness against North Mini Code 1.0's 61 (C), and leads in 0 of 4 scored categories. North Mini Code 1.0 leads on reach, risk and agent interface. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-5-122b-a10b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-5-122b-a10b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "north-mini-code-1-0", "b": "qwen3-5-122b-a10b"}`. 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How: https://www.anchorterminal.com/agents/#mcp