# North Mini Code 1.0 vs Qwen3.5-2B

> North Mini Code 1.0 scores 61 (C) to Qwen3.5-2B'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/north-mini-code-1-0-vs-qwen3-5-2b
- Markdown: https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-5-2b.md (~9,150 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

North Mini Code 1.0 scores 61 (C) on agent readiness against Qwen3.5-2B's 50.5 (D), and leads in 3 of 4 scored categories. 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
- Qwen3.5-2B: grade D, 50.5/100, rank #144 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-5-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-5-2b.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 60

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

### Qwen3.5-2B (D)

Good for: Builders who want a cheap local helper or fine-tuning base for narrow tasks and image reading on a laptop, small GPU or CPU

Watch for: No quantised build from Alibaba, and only one inference provider


## Why the edge

### Agent interface: North Mini Code 1.0 +25

- 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.
- Qwen3.5-2B (60): 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 format to the template. Reasoning control scores 0, since the card shows how to turn thinking on but not where the reasoning comes back. 262,144 tokens is native (10). Structured output scores 0, as nothing documents JSON output for the model. 20+10+20+0+0+10 = 60.

### 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.
- Qwen3.5-2B (80): Memory earns the full 30, since LM Studio's Q4_K_M GGUF with its vision projector is 1.94 GB, about 2.3 GB with the fifth added (Ollama's Q4_K_M build was 1.9 GB on 21 October). Alibaba publishes no quantised 2B, so builds scores 0. Runtimes reach the 25 cap, with llama.cpp b11963 and Ollama through the build it pins re-read today. Apache 2.0 with nothing added gives terms 15 and the ungated download 10. Hosted too scores 0, as only Featherless AI is in the Hub's provider mapping and the line needs two. 30+25+0+15+10+0 = 80.

### 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.
- Qwen3.5-2B (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 agent cautions and no reporting route was found. The Qwen3.5 blog and Alibaba's security response centre couldn't be read, so those are scored as absent and left open. 25+15+0+0+15+0 = 55.

## Score by category

| Category | Weight | North Mini Code 1.0 | Qwen3.5-2B | 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 | 60 | North Mini Code 1.0 +25 |
| Documentation and upkeep | 10% (10 this run) | 36 | 37 | Qwen3.5-2B +1 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **61 · C** | **50.5 · D** | |

## Facts side by side

| Fact | North Mini Code 1.0 | Qwen3.5-2B |
| --- | --- | --- |
| 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.

**Qwen3.5-2B.** A small vision-language model for prototypes, fine-tuning and narrow local steps such as tool calls, tagging or reading an image. It runs in about 2 GB at 4 bits in six runtimes under plain Apache 2.0. Keep thinking off, since the card warns it loops. The card has no safety results, and Qwen has answered none of its discussions.

## 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

### Qwen3.5-2B

1. Smallest start is `ollama run qwen3.5:2b-q4_K_M` (1.9 GB); plain `qwen3.5:2b` pulls the 2.7 GB Q8_0 build
2. Full precision `vllm serve Qwen/Qwen3.5-2B --port 8000 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder`
3. Thinking is off by default; if you turn it on with `"chat_template_kwargs": {"enable_thinking": true}`, stream and cut off runaway output, since the card warns of thinking loops, and add `--reasoning-parser qwen3` in vLLM or SGLang to split the reasoning out
4. There's no generation_config.json, so pass the card's sampling yourself, for example `temperature=1.0, top_p=1.0, top_k=20, presence_penalty=2.0` for text without thinking
5. Use it for narrow steps or as a fine-tuning base, not as the main agent, as the card itself suggests
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 Qwen3.5-2B?

North Mini Code 1.0 scores 61 (C) on agent readiness against Qwen3.5-2B's 50.5 (D), and leads in 3 of 4 scored categories.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-5-2b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-5-2b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "north-mini-code-1-0", "b": "qwen3-5-2b"}`. From a terminal: `anchor compare north-mini-code-1-0 qwen3-5-2b`
- 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/qwen3-5-2b.json

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- [MiMo-V2.6-Flash-RL vs North Mini Code 1.0](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-north-mini-code-1-0.md)
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- [North Mini Code 1.0 vs Qwen3.5-4B](https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-5-4b.md)
- [North Mini Code 1.0 vs Qwen3.5-9B](https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-5-9b.md)
- [North Mini Code 1.0 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-6-27b.md)
- [North Mini Code 1.0 vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-6-35b-a3b.md)
- [North Mini Code 1.0 vs Qwen3.8-27B](https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-8-27b.md)
- [North Mini Code 1.0 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-8-flash-next.md)
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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)
- [North Mini Code 1.0 vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-omni-30b-a3b-instruct.md)
- [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)
- [North Mini Code 1.0 vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-vl-32b-instruct.md)
- [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)
- [North Mini Code 1.0 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-sensenova-loop-9b-preview.md)
- [North Mini Code 1.0 vs SmolLM3-3B](https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-smollm3-3b.md)
- [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)
- [North Mini Code 1.0 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-step-3-7-flash.md)
- [North Mini Code 1.0 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-swift-1-5-qwen3-8-27b.md)
- [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)
- [North Mini Code 1.0 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-unlimited-ocr-2.md)
- [North Mini Code 1.0 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-wevisone-vl-2b.md)
- [North Mini Code 1.0 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-wevisone-vl-4b.md)
- [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 Qwen3.5-2B](https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-2b.md)
- [Olmo 3 7B Think vs Qwen3.5-2B](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-2b.md)
- [Ornith-1.5-122B vs Qwen3.5-2B](https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-2b.md)
- [Ornith-1.5-35B-A3B vs Qwen3.5-2B](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-2b.md)
- [Ornith-1.5-397B vs Qwen3.5-2B](https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-2b.md)
- [Ornith-1.5-9B vs Qwen3.5-2B](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-2b.md)
- [OTel-2.0-LLM-31B-IT vs Qwen3.5-2B](https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-5-2b.md)
- [Qwen-AgentWorld-35B-A3B vs Qwen3.5-2B](https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-2b.md)
- [Qwen3-30B-A3B-Instruct-2507 vs Qwen3.5-2B](https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-5-2b.md)
- [Qwen3-4B-Instruct-2507 vs Qwen3.5-2B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-5-2b.md)
- [Qwen3.5-0.8B vs Qwen3.5-2B](https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-5-2b.md)
- [Qwen3.5-122B-A10B vs Qwen3.5-2B](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-5-2b.md)
- [Qwen3.5-2B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-5-397b-a17b.md)
- [Qwen3.5-2B vs Qwen3.5-4B](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-5-4b.md)
- [Qwen3.5-2B vs Qwen3.5-9B](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-5-9b.md)
- [Qwen3.5-2B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-6-27b.md)
- [Qwen3.5-2B vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-6-35b-a3b.md)
- [Qwen3.5-2B vs Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-8-27b.md)
- [Qwen3.5-2B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-8-flash-next.md)
- [Qwen3.5-2B vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-coder-30b-a3b-instruct.md)
- [Qwen3.5-2B vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-coder-next.md)
- [Qwen3.5-2B vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-omni-30b-a3b-instruct.md)
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- [Qwen3.5-2B vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-vl-30b-a3b-instruct.md)
- [Qwen3.5-2B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-vl-32b-instruct.md)
- [Qwen3.5-2B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-vl-4b-instruct.md)
- [Qwen3.5-2B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-vl-8b-instruct.md)
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- [Qwen3.5-2B vs Recursive Flint](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-recursive-flint.md)
- [Qwen3.5-2B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-ripple-65b-preview.md)
- [Qwen3.5-2B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-sensenova-loop-9b-preview.md)
- [Qwen3.5-2B vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-smollm3-3b.md)
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- [Qwen3.5-2B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-swift-1-5-qwen3-8-27b.md)
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- [Qwen3.5-2B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-thinkingcap-qwen3-8-27b.md)
- [Qwen3.5-2B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-unlimited-ocr-2.md)
- [Qwen3.5-2B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-wevisone-vl-2b.md)
- [Qwen3.5-2B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-wevisone-vl-4b.md)
- [Qwen3.5-2B vs ZAYA1-8B](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-zaya1-8b.md)

---

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