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  4. North Mini Code
Cohere
Released June 9, 2026

North Mini Code

North Mini Code by Cohere is a Mixture-of-Experts model trained for agentic software engineering, terminal tasks, and code generation. Apache 2.0 licensed.

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Inputs
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Outputs
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Model Overview

Capabilities, design details, and architectural traits

North Mini Code - Cohere's first agentic coding model for sovereign developers

North Mini Code is a Mixture-of-Experts model designed specifically for agentic software engineering. It is the inaugural member of Cohere's North family of code agent models, released under the Apache 2.0 license for open-source, sovereign deployment.

TraitDetail
Sparse MoE architecture128 experts with 8 activated per token; sigmoid router activation before top-k selection; single dense layer before sparse layers
Interleaved attentionSliding-window attention with RoPE and global attention with no positional embeddings, in a 3:1 ratio
Multi-harness trainingTrained against multiple agent scaffolds rather than tuned to a single one, so performance generalizes across harnesses like SWE-Agent and OpenCode
Cascaded post-trainingTwo-stage supervised fine-tuning followed by reinforcement learning with verifiable rewards (RLVR), targeting software engineering and terminal tasks
Agentic coding focusBuilt for repo-level code changes, terminal-based agents driving shell tools end-to-end, and orchestrating sub-agents across multi-turn tasks

Sovereign open-source positioning

Cohere frames North Mini Code as a step toward sovereign AI for developers who need control over their agentic coding infrastructure. Weights are freely available in bf16, fp8, and w4a16 formats.

Speed and throughput

In internal testing, North Mini Code achieved up to 2.8x higher output throughput than Devstral Small 2 under identical concurrency, along with a 30% advantage in inter-token latency. It scores 33.4 on the Artificial Analysis Coding Index, outperforming several larger open-source models in its size class.

Benchmark Performance

Independent evaluations · Artificial Analysis

20.2%
Intelligence
36.5%
Coding Index
3.1%
Agentic Index

Accuracy & Capability Details

GPQA - Graduate Science75.7%
Humanity's Last Exam11.1%
SciCode - Scientific Coding38.2%
Instruction Following57.6%
Long Context Reasoning36.0%
τ²-Bench - Agentic Tasks37.4%
TerminalBench - System Control31.1%
Specs
Context window
256Ktokens

Prices in USD.

Compare Models Side-by-Side

Evaluate specifications, pricing, and independent benchmark indices

Model Details
General Info
ProviderCohereAnthropicAnthropic
Release DateJune 9, 2026July 24, 2026June 9, 2026
Knowledge Cutoff-May 2026-
Context & Limits
Context Window256K
1M
Best Context Window
1M
Best Context Window
Pricing (per 1M tokens)
Input Pricing
Free
Best Input Pricing
$5$10
Output Pricing
Free
Best Output Pricing
$25$50
Modalities
Inputs
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Outputs
text
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Benchmarks (0-100)
Intelligence Index20.2
63.1
Best Intelligence Index
62.1
Coding Index36.5
78.0
Best Coding Index
76.5
Agentic Index3.1
59.2
Best Agentic Index
56.6
North Mini Code
Claude Opus 5
Claude Fable 5

GPQA Benchmark

Graduate-level reasoning and expert Q&A evaluation.

76%
North Mini Code
GPQA Benchmark
Score: 76%
North Mini Code
93%
Claude Opus 5
GPQA Benchmark
Score: 93%
Claude Opus 5
93%
Claude Fable 5
GPQA Benchmark
Score: 93%
Claude Fable 5

Humanity's Last Exam

Extremely difficult logical reasoning and knowledge.

11%
North Mini Code
Humanity's Last Exam
Score: 11%
North Mini Code
55%
Claude Opus 5
Humanity's Last Exam
Score: 55%
Claude Opus 5
56%
Claude Fable 5
Humanity's Last Exam
Score: 56%
Claude Fable 5

Long Context Reasoning

Logical reasoning over long context windows.

36%
North Mini Code
Long Context Reasoning
Score: 36%
North Mini Code
76%
Claude Opus 5
Long Context Reasoning
Score: 76%
Claude Opus 5
77%
Claude Fable 5
Long Context Reasoning
Score: 77%
Claude Fable 5

Independent evaluation data provided by Artificial Analysis. To view the latest benchmarks and full details, visit their official site.

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