# North Mini Code 1.0 vs SmolLM3-3B

> North Mini Code 1.0 and SmolLM3-3B score within a point of each other for open-weight models you run yourself, 61 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-smollm3-3b
- Markdown: https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-smollm3-3b.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 <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 and SmolLM3-3B score within a point of each other on agent readiness, 61 (C) and 61 (C). SmolLM3-3B leads on risk and documentation and upkeep. 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
- SmolLM3-3B: grade C, 61/100, rank #55 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/smollm3-3b.md · JSON https://www.anchorterminal.com/api/v1/tools/smollm3-3b.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:
- Agent interface, 85 against 80

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

### SmolLM3-3B (C)

Good for: Agents that want a small, fully transparent tool-calling cousin on a laptop, CPU or edge device

Ahead on:
- Risk, 65 against 60
- Documentation and upkeep, 74 against 36

Watch for: 65,536 tokens trained; 131,072 needs a YaRN edit to config.json


## Why the edge

### Documentation and upkeep: SmolLM3-3B +38

- North Mini Code 1.0 (36): Model card 11, Intended use, the architecture, the parameters and the context are on the card. Training data 0, the card gives the training method and nothing about the data. Last change 10, the main repository last changed on 15 June 2026 (outdated chat templates removed), 148 days before this check, and the draft model's repository on 29 July, 104 days. Responsiveness 0, none of the 11 most recent discussions that aren't pull requests (7 June to 6 November 2026) has a reply from a CohereLabs member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release, with each build in its own repository. File health 10, Config, template and card agree on the architecture, and the June commit that removed broken templates says so. 11 + 0 + 10 + 0 + 5 + 10 = 36.
- SmolLM3-3B (74): Model card 14, it covers limitations, architecture, parameters, context and languages but has no intended-use section and leaves the June 2025 cut-off to the chat template. Training data 20, the datasets, mixture weights and post-training set are published with sources and sizes. Last change 20, Hugging Face's ONNX build changed its tokenizer config on 28 October 2026, though the main repository hasn't changed since 10 September 2025. Responsiveness 10, staff answered 14 of 36 discussions, none since November 2025. Version policy 5, a first release never replaced, with no dated notes. File health 5, template and tokenizer reports wait with no reply.

### Risk: SmolLM3-3B +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.
- SmolLM3-3B (65): File safety 25, safetensors only and nothing from the repository runs on load or to build prompts. Provenance 15, the HuggingFaceTB organisation linked from Hugging Face's launch post, with a SHA-256 for each shard. Safety evaluation 0, none published. Agent guidance 5, only the general cautions in the card's Limitations section. Licence risk 15, Apache 2.0 with nothing added and a working licence link. Security programme 5, huggingface.co has a valid security.txt and Transformers has a security policy; whether advisories are handled in public couldn't be checked from our session, so that half is scored as absent.

### Agent interface: North Mini Code 1.0 +5

- 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.
- SmolLM3-3B (80): Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card says how tools are declared, how calls come back and which vLLM parser to use. Trained tool calling 20, the template invites one or more calls per turn, each in its own block. Structured output 0, nothing documents asking the weights for JSON. Reasoning control 10, thinking turns off per request through the kwarg or a system-prompt flag, and the reasoning comes back in think tags. Context 5, trained at 65,536 tokens, with 131,072 only through YaRN.

## Score by category

| Category | Weight | North Mini Code 1.0 | SmolLM3-3B | 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 | 65 | SmolLM3-3B +5 |
| Agent interface | 10% (10 this run) | 85 | 80 | North Mini Code 1.0 +5 |
| Documentation and upkeep | 10% (10 this run) | 36 | 74 | SmolLM3-3B +38 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **61 · C** | **61 · C** | |

## Facts side by side

| Fact | North Mini Code 1.0 | SmolLM3-3B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Cohere | Hugging Face |
| 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 | 2025-09-10 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 3.3/5 (4) |

## How the scores have moved

### North Mini Code 1.0

No score change since it was listed.

### SmolLM3-3B

- no change on 3 November 2026: product

## 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.

**SmolLM3-3B.** A tiny, fully open cousin with documented tool calling and switchable thinking, for local subtasks and routing on laptops and CPUs. It needs about 8 GB in BF16, or under 3 GB in Hugging Face's 4-bit ONNX build. The main risks are a 64K native context, no safety results and no staff reply in its discussions since November 2025.

## 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

### SmolLM3-3B

1. Start it with `vllm serve HuggingFaceTB/SmolLM3-3B --enable-auto-tool-choice --tool-call-parser=hermes`, or tool calls come back as text
2. Pass `chat_template_kwargs` with `enable_thinking` false for short tasks, since thinking is on by default; a `/think` or `/no_think` flag in the system prompt overrides the kwarg
3. Put earlier tool calls in the assistant message content, since the template ignores a `tool_calls` field when it renders history
4. Add the YaRN `rope_scaling` block (factor 2.0, original 65536) to config.json before sending more than 65,536 tokens
5. Use temperature 0.6 and top_p 0.95, the card's recommended sampling
6. Needs Transformers v4.53.0 or later; for llama.cpp use a GGUF from ggml-org or another converter, since Hugging Face publishes none
7. Validate JSON output yourself or use the runtime's guided decoding

## Questions

### Which is better for AI agents, North Mini Code 1.0 or SmolLM3-3B?

North Mini Code 1.0 and SmolLM3-3B score within a point of each other on agent readiness, 61 (C) and 61 (C). SmolLM3-3B leads on risk and documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-smollm3-3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-smollm3-3b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "north-mini-code-1-0", "b": "smollm3-3b"}`. From a terminal: `anchor compare north-mini-code-1-0 smollm3-3b`
- 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/smollm3-3b.json

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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)
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- [North Mini Code 1.0 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-6-27b.md)
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- [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-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 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 SmolLM3-3B](https://www.anchorterminal.com/compare/occamy-1-0-vs-smollm3-3b.md)
- [Olmo 3 7B Think vs SmolLM3-3B](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-smollm3-3b.md)
- [Ornith-1.5-122B vs SmolLM3-3B](https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-smollm3-3b.md)
- [Ornith-1.5-35B-A3B vs SmolLM3-3B](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-smollm3-3b.md)
- [Ornith-1.5-397B vs SmolLM3-3B](https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-smollm3-3b.md)
- [Ornith-1.5-9B vs SmolLM3-3B](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-smollm3-3b.md)
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- [Qwen3-30B-A3B-Instruct-2507 vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-smollm3-3b.md)
- [Qwen3-4B-Instruct-2507 vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-smollm3-3b.md)
- [Qwen3.5-0.8B vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-smollm3-3b.md)
- [Qwen3.5-122B-A10B vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-smollm3-3b.md)
- [Qwen3.5-2B vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-smollm3-3b.md)
- [Qwen3.5-397B-A17B vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-smollm3-3b.md)
- [Qwen3.5-4B vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-5-4b-vs-smollm3-3b.md)
- [Qwen3.5-9B vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-5-9b-vs-smollm3-3b.md)
- [Qwen3.6-27B vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-smollm3-3b.md)
- [Qwen3.6-35B-A3B vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-smollm3-3b.md)
- [Qwen3.8-27B vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-smollm3-3b.md)
- [Qwen3.8-Flash-Next vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-smollm3-3b.md)
- [Qwen3-Coder-30B-A3B-Instruct vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-smollm3-3b.md)
- [Qwen3-Coder-Next vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-coder-next-vs-smollm3-3b.md)
- [Qwen3-Omni-30B-A3B-Instruct vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-smollm3-3b.md)
- [Qwen3-VL-2B-Instruct vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-smollm3-3b.md)
- [Qwen3-VL-30B-A3B-Instruct vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-smollm3-3b.md)
- [Qwen3-VL-32B-Instruct vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-smollm3-3b.md)
- [Qwen3-VL-4B-Instruct vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-smollm3-3b.md)
- [Qwen3-VL-8B-Instruct vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-smollm3-3b.md)
- [Qwopus3.8-27B-Flash-V2 vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-smollm3-3b.md)
- [Recursive Flint vs SmolLM3-3B](https://www.anchorterminal.com/compare/recursive-flint-vs-smollm3-3b.md)
- [Ripple-65B Preview vs SmolLM3-3B](https://www.anchorterminal.com/compare/ripple-65b-preview-vs-smollm3-3b.md)
- [SenseNova-Loop-9B-Preview vs SmolLM3-3B](https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-smollm3-3b.md)
- [SmolLM3-3B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/smollm3-3b-vs-spark-x2-6-4b.md)
- [SmolLM3-3B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/smollm3-3b-vs-step-3-7-flash.md)
- [SmolLM3-3B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/smollm3-3b-vs-swift-1-5-qwen3-8-27b.md)
- [SmolLM3-3B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/smollm3-3b-vs-swift-1-5-qwen3-8-flash-next.md)
- [SmolLM3-3B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/smollm3-3b-vs-thinkingcap-qwen3-8-27b.md)
- [SmolLM3-3B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/smollm3-3b-vs-unlimited-ocr-2.md)
- [SmolLM3-3B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/smollm3-3b-vs-wevisone-vl-2b.md)
- [SmolLM3-3B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/smollm3-3b-vs-wevisone-vl-4b.md)
- [SmolLM3-3B vs ZAYA1-8B](https://www.anchorterminal.com/compare/smollm3-3b-vs-zaya1-8b.md)

---

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