# North Mini Code 1.0 vs Spark-X2.6-4B

> North Mini Code 1.0 and Spark-X2.6-4B 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-spark-x2-6-4b
- Markdown: https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-spark-x2-6-4b.md (~9,450 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 Spark-X2.6-4B score within a point of each other on agent readiness, 61 (C) and 61 (C). Spark-X2.6-4B leads on 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
- Spark-X2.6-4B: grade C, 61/100, rank #56 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/spark-x2-6-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/spark-x2-6-4b.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 50
- Agent interface, 85 against 70

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

### Spark-X2.6-4B (C)

Good for: Agents that want a tiny local model for tool loops and long documents on a laptop, CPU or small GPU.

Ahead on:
- Documentation and upkeep, 51 against 36

Watch for: No safety results and no agent or prompt-injection guidance


## 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.
- Spark-X2.6-4B (70): Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, the format lives only in the template and the spark25 parser name only in the SGLang command. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented. Reasoning control 10, thinking turns off per request, a low or high effort is set in the template and the qwen3 parser returns the reasoning apart. Context 10, 1,048,576 tokens with no scaling.

### Documentation and upkeep: Spark-X2.6-4B +15

- 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.
- Spark-X2.6-4B (51): Model card 11 of 20, intended use, parameters, context and languages, with no limitations, cut-off or architecture description of its own. Training data 0, the X2.6 card describes none. Last change 20, the card changed on 1 November 2026. Responsiveness 10, SparkLLM members answered 11 of 40 discussions. Version policy 5, X2.5 stays in place with no note. File health 5, the tokenizer config's 131,072 disagrees with the 1M context.

### Risk: North Mini Code 1.0 +10

- 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.
- Spark-X2.6-4B (50): File safety 15 of 25, safetensors and GGUF only, but the card's Transformers and vLLM routes run the repository's Python. Provenance 15, the files are in SparkLLM's own Hub organisation, which its GitHub docs name, and every file has a checksum, though neither organisation is verified and no company site was found. Safety evaluation 0 and agent guidance 0, none published. Licence risk 15, plain Apache 2.0 that hasn't changed. Security programme 5 of 10, a private reporting route and email in SparkLLM's community repository, with no public advisories.

## Score by category

| Category | Weight | North Mini Code 1.0 | Spark-X2.6-4B | 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 | 50 | North Mini Code 1.0 +10 |
| Agent interface | 10% (10 this run) | 85 | 70 | North Mini Code 1.0 +15 |
| Documentation and upkeep | 10% (10 this run) | 36 | 51 | Spark-X2.6-4B +15 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **61 · C** | **61 · C** | |

## Facts side by side

| Fact | North Mini Code 1.0 | Spark-X2.6-4B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Cohere | SparkLLM (XHToken) |
| 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 | 2026-10-20 |
| 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.

### Spark-X2.6-4B

- no change on 3 November 2026: new evidence

## 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.

**Spark-X2.6-4B.** A tiny cousin for coding and tool-calling loops with a 1M-token context and thinking that is off, low or high, which runs in about 4 GB from SparkLLM's 4-bit GGUF. Use llama.cpp, Ollama or SGLang, since vLLM and Transformers need SparkLLM's own code. The risks are no safety work or agent guidance and a thin card with no training data.

## 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

### Spark-X2.6-4B

1. Quickest start is `llama-server -hf XHToken/Spark-X2.6-4B-GGUF:Q4_K_M --jinja`, which needs llama.cpp b10828 or later, or `ollama run SparkLLM/Spark-X2.6-4B` (8.2 GB, Ollama 0.34.1 or later)
2. For parsed tool calls serve it with SGLang 0.5.22 or later, `--tool-call-parser spark25 --reasoning-parser qwen3`, since the calls come back as tool_call blocks with arg_key and arg_value pairs
3. Thinking is on by default. Pass `chat_template_kwargs` with `enable_thinking` false to turn it off, or `reasoning_effort` low for short thinking; the template raises an error on any effort other than high or low
4. SparkLLM's recommended sampling is temperature 1.0, top_p 0.95 and top_k -1
5. The template always opens with its own default system line, with the effort instruction, and puts your system message after the tool list
6. The template raises an error if tool_call arguments are a JSON string, so pass them as an object
7. Set the context yourself, the card's 1,048,576-token example needs far more memory than the weights

## Questions

### Which is better for AI agents, North Mini Code 1.0 or Spark-X2.6-4B?

North Mini Code 1.0 and Spark-X2.6-4B score within a point of each other on agent readiness, 61 (C) and 61 (C). Spark-X2.6-4B leads on documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-spark-x2-6-4b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-spark-x2-6-4b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "north-mini-code-1-0", "b": "spark-x2-6-4b"}`. From a terminal: `anchor compare north-mini-code-1-0 spark-x2-6-4b`
- 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/spark-x2-6-4b.json

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- [Spark-X2.6-4B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-wevisone-vl-4b.md)
- [Spark-X2.6-4B vs ZAYA1-8B](https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-zaya1-8b.md)

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