# North Mini Code 1.0 vs Qwen3.5-4B

> Qwen3.5-4B and North Mini Code 1.0 score within a point of each other for open-weight models you run yourself, 61.2 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-5-4b
- Markdown: https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-5-4b.md (~8,800 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 <slug>.grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.<category>. and <a>.vs.<b>. 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

Qwen3.5-4B and North Mini Code 1.0 score within a point of each other on agent readiness, 61.2 (C) and 61 (C). North Mini Code 1.0 leads on 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
- Qwen3.5-4B: grade C, 61.2/100, rank #38 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-5-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-5-4b.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:
- Risk, 60 against 55
- Agent interface, 85 against 70

Watch for: No limitations, safety results, languages or data description on the card

### Qwen3.5-4B (C)

Good for: Agents that want a cheap local helper for tool calls, short reasoning and reading images on a laptop or small GPU

Watch for: No quantised build from Alibaba for this size


## 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.
- Qwen3.5-4B (70): The template ships in the repository (20) and the model was trained for tool use with several calls per turn in the template (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.

### 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.
- Qwen3.5-4B (55): Safetensors only with no remote code gives file safety its 25, and files in the Qwen organisation the publisher's GitHub links to, each with a hash on the Hub, give provenance 15. The Apache 2.0 licence carries none of the deductions, so licence risk keeps 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 those two are scored as absent and left open.

## Score by category

| Category | Weight | North Mini Code 1.0 | Qwen3.5-4B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 85 | 85 | even |
| 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** | **61.2 · C** | |

## Facts side by side

| Fact | North Mini Code 1.0 | Qwen3.5-4B |
| --- | --- | --- |
| 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 | 2026-03-02 |
| 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) |

## 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.

**Qwen3.5-4B.** A small vision-language cousin for tool calls, light agent steps and image or video reading on almost any machine, with switchable thinking. Ollama's 3.3 GB build or the 9.3 GB BF16 files run in every major runtime under plain Apache 2.0. The main risks are no safety results, no structured-output guarantee and no answers from Alibaba since March.

## 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

### Qwen3.5-4B

1. Smallest start is `ollama run qwen3.5:4b` (Q4_K_M, about 4 GB with context)
2. Full precision `vllm serve Qwen/Qwen3.5-4B --port 8000 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`
3. 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
4. There's no generation_config.json, so pass the card's sampling yourself, for example `temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5` without thinking
5. Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens
6. Validate any JSON it returns, since structured output isn't documented

## Questions

### Which is better for AI agents, North Mini Code 1.0 or Qwen3.5-4B?

Qwen3.5-4B and North Mini Code 1.0 score within a point of each other on agent readiness, 61.2 (C) and 61 (C). North Mini Code 1.0 leads on risk and agent interface.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-5-4b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-5-4b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "north-mini-code-1-0", "b": "qwen3-5-4b"}`. From a terminal: `anchor compare north-mini-code-1-0 qwen3-5-4b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/north-mini-code-1-0.json and https://www.anchorterminal.com/api/v1/tools/qwen3-5-4b.json

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- [North Mini Code 1.0 vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-coder-next.md)
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- [North Mini Code 1.0 vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-vl-2b-instruct.md)
- [North Mini Code 1.0 vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-vl-30b-a3b-instruct.md)
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- [North Mini Code 1.0 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-vl-4b-instruct.md)
- [North Mini Code 1.0 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-vl-8b-instruct.md)
- [North Mini Code 1.0 vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwopus3-8-27b-flash-v2.md)
- [North Mini Code 1.0 vs Recursive Flint](https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-recursive-flint.md)
- [North Mini Code 1.0 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-ripple-65b-preview.md)
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- [North Mini Code 1.0 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-spark-x2-6-4b.md)
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- [North Mini Code 1.0 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-swift-1-5-qwen3-8-flash-next.md)
- [North Mini Code 1.0 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-thinkingcap-qwen3-8-27b.md)
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- [North Mini Code 1.0 vs ZAYA1-8B](https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-zaya1-8b.md)
- [occamy-1.0 vs Qwen3.5-4B](https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-4b.md)
- [Olmo 3 7B Think vs Qwen3.5-4B](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-4b.md)
- [Ornith-1.5-122B vs Qwen3.5-4B](https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-4b.md)
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- [Ornith-1.5-397B vs Qwen3.5-4B](https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-4b.md)
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- [Qwen3-30B-A3B-Instruct-2507 vs Qwen3.5-4B](https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-5-4b.md)
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- [Qwen3.5-122B-A10B vs Qwen3.5-4B](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-5-4b.md)
- [Qwen3.5-2B vs Qwen3.5-4B](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-5-4b.md)
- [Qwen3.5-397B-A17B vs Qwen3.5-4B](https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-5-4b.md)
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- [Qwen3.5-4B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-5-4b-vs-ripple-65b-preview.md)
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- [Qwen3.5-4B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-5-4b-vs-swift-1-5-qwen3-8-27b.md)
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- [Qwen3.5-4B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/qwen3-5-4b-vs-unlimited-ocr-2.md)
- [Qwen3.5-4B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-5-4b-vs-wevisone-vl-2b.md)
- [Qwen3.5-4B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/qwen3-5-4b-vs-wevisone-vl-4b.md)
- [Qwen3.5-4B vs ZAYA1-8B](https://www.anchorterminal.com/compare/qwen3-5-4b-vs-zaya1-8b.md)

---

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