Cohere coding model for practical software engineering and agentic edits
- Context
- 256K
- Released
- 4mo ago
- Parameters
- —
Sources:models.dev
Benchmarks
DeepSWE is the primary coding benchmark where an exact model evaluation exists. Other sourced evaluations appear below; missing scores are not zero.
Not evaluated in DeepSWE 1.1 as of 2026-09-22. Results are only shown for exact model versions in the official dataset. Official leaderboard.
No benchmark results have been matched to this model yet.
Specifications
- Developer
- Cohere
- Model family
- —
- Type
- Language model
- Predecessor
- —
- Successor
- —
- Released
- June 9, 2026
- Model ID
cohere/north-mini-code-1-0- Openness
- Open weights
- License
- —
- Parameters
- —
- Input modalities
- text
- Output modalities
- text
- Modality
- text->text
- Max output tokens
- 64K
- Knowledge cutoff
- 2025-09-23
- Hugging Face
- —
- Source freshness
- Metadata synced Oct 8, 2026, 10:17 PM UTC
Additional source evidence
- Source model
- cohere/north-mini-code-1-0
- Source family
- north
- Last updated
- 2026-06-09
- Knowledge cutoff
- 2025-09-23
- Model context
- 256K
- Model output limit
- 64K
- Open weights
- Yes
- File attachments
- No
- Reasoning
- Yes
- Tool calling
- Yes
- Structured output
- Yes
- Temperature control
- Yes
Published benchmarks
| Benchmark | Score | Evaluation details | Evidence |
|---|---|---|---|
| SWE-Bench Verified | 67.6 · resolved | SWE-agent · 2026-06-09 | Source |
| SWE-Bench Pro | 40.2 · resolve rate | SWE-agent · 2026-06-09 | Source |
| Artificial Analysis Intelligence Index | 27.6 · index score | 2026-06-09 | Source |
| Artificial Analysis Coding Index | 33.4 · index score | 2026-06-09 | Source |
| GDPval-AA | 14 · win rate | 2026-06-09 | Source |
| τ²-Bench Telecom | 37 · success rate | 2026-06-09 | Source |
Complete source record (JSON)
{
"id": "cohere/north-mini-code-1-0",
"name": "North Mini Code",
"description": "Cohere coding model for practical software engineering and agentic edits",
"family": "north",
"last_updated": "2026-06-09",
"knowledge": "2025-09-23",
"attachment": false,
"reasoning": true,
"tool_call": true,
"structured_output": true,
"temperature": true,
"modalities": {
"input": [
"text"
],
"output": [
"text"
]
},
"limit": {
"context": 256000,
"output": 64000
},
"benchmarks": [
{
"name": "SWE-Bench Verified",
"score": 67.6,
"source": "https://huggingface.co/CohereLabs/North-Mini-Code-1.0",
"metric": "resolved",
"harness": "SWE-agent",
"date": "2026-06-09"
},
{
"name": "SWE-Bench Pro",
"score": 40.2,
"source": "https://huggingface.co/CohereLabs/North-Mini-Code-1.0",
"metric": "resolve rate",
"harness": "SWE-agent",
"date": "2026-06-09"
},
{
"name": "Artificial Analysis Intelligence Index",
"score": 27.6,
"source": "https://artificialanalysis.ai/articles/north-mini-code-cohere-s-small-coding-focused-moe-model",
"metric": "index score",
"date": "2026-06-09"
},
{
"name": "Artificial Analysis Coding Index",
"score": 33.4,
"source": "https://artificialanalysis.ai/articles/north-mini-code-cohere-s-small-coding-focused-moe-model",
"metric": "index score",
"date": "2026-06-09"
},
{
"name": "GDPval-AA",
"score": 14,
"source": "https://artificialanalysis.ai/articles/north-mini-code-cohere-s-small-coding-focused-moe-model",
"metric": "win rate",
"date": "2026-06-09"
},
{
"name": "τ²-Bench Telecom",
"score": 37,
"source": "https://artificialanalysis.ai/articles/north-mini-code-cohere-s-small-coding-focused-moe-model",
"metric": "success rate",
"date": "2026-06-09"
}
],
"release_date": "2026-06-09",
"open_weights": true
}Infrastructure
Provider availability
Provider endpoint data will appear after this model's first OpenRouter endpoint sync.
Deployment graph
Can I run this?
Vertical Tabs only shows compatibility when it has structured evidence tying this exact model to hardware, runtime, quantization, memory use, and observed performance.