Datasets:

witcheer
/

rtx-5090-benchmarks

Cohere North-Mini-Code-1.0 — a 30B-total / 3B-active MoE (cohere2moe), Apache-2.0, marketed for agentic coding. The arch merged into llama.cpp on 2026-06-13 (PR #24260, validated on an RTX 5090 inside the PR), so these are the first consumer-Blackwell numbers. Run at Q6_K (23.8 GiB, peak 25.6/32 GB VRAM, no offload), think-OFF to match the board.

1. It lands LAST on the 9-model agentic board. Agentic Score 90.4 (task-success 83.3%, tool-eff 0.94), tied with Nex-N2-mini and below generalist A3B models (Qwen3.5-35B-A3B 97.5, Nemotron-Cascade-2-30B-A3B 96.9). Tool-efficiency is fine; task-success is the gap. The agentic-coding tuning doesn't translate to top tool-driving on this harness. (chart: north-mini-board.png)

2. ARC-Challenge is reasoning-gated — and the think-OFF board understates a reasoning model. ARC-Challenge 60.2% think-OFF → ~95% think-ON (+35pt; think-ON is a 40-question spot-check, 38/40). The Challenge subset is selected for multi-step reasoning; forcing a bare-letter answer (think-OFF) measures the wrong thing. GSM8K is unaffected (95.8% either way) because it's generative — the model writes its solution regardless. North-Mini is genuinely a reasoning model (<|START_THINKING|> tokens; its template gates on reasoning/reasoning_effort, not enable_thinking). (chart: north-mini-arc-gate.png)

3. The reality anchor can't adjudicate it (different protocol). Cohere self-reports 67.6% SWE-bench Verified (full set, their scaffold, 1×H100 FP8) — which would top this board's best measured resolve (63%). But the rig's anchor axis is our native loop on a 30-hardest-bug subset, graded officially. Different scaffold, subset, and difficulty: the two numbers don't share an axis. So North-Mini is last on synthetic, claims-best on real, and the gap is unresolved — either the synthetic agentic bench under-rates a model trained on real SWE harnesses, or the vendor number doesn't reproduce on a standardized loop (the Qwopus-Coder precedent: claimed 67%, scored 57% on our subset). A fresh same-protocol run would resolve it; out of scope here.

Coder strengths are real: HumanEval 86.6%, GSM8K 95.8%, decode 305 tok/s (Q6_K). The vendor "2.8x output throughput vs Devstral Small 2" is a 1×H100 FP8 number; on a 5090 you simply get fast A3B decode.

Methodology note: benchmarking this reasoning model think-OFF first produced a contaminated, ~26×-slower run — the harness's enable_thinking:False is a no-op for cohere2moe, which gates on reasoning. Fixed in the harness (send reasoning/reasoning_effort + serve with --jinja); verified 106→4 tokens/question.

Model

Field	Value
Model	north-mini-code
Parameters	30.48 B (dense)
Quantization	unknown
File size	23.76 GiB
Engine	llama.cpp (CUDA 12.8 (patched))

Hardware

Component	Spec
GPU	NVIDIA GeForce RTX 5090
CPU	AMD Ryzen 5 9600
RAM	64GB DDR5-5600
OS	Ubuntu 26.04 LTS
CUDA	12.8 (patched)

Quality Benchmarks

All benchmarks use generative evaluation via llama-server chat completions. Multiple-choice tasks (MMLU, ARC, HellaSwag) use letter extraction instead of loglikelihood scoring -- results are internally consistent for model comparison but absolute scores may differ from logprob-based evaluations by 5-15%.

Summary

Benchmark	Score	Metric
MMLU	73.32%	accuracy
ARC-Challenge	60.24%	accuracy
HellaSwag	70.82%	accuracy
HumanEval	86.59%	pass@1
GSM8K	95.83%	exact_match

MMLU Breakdown by Category

Category	Score	Correct / Total
Stem	66.49%	1,002 / 1,507
Humanities	72.25%	1,143 / 1,582
Social Sciences	85.12%	1,407 / 1,653
Other	70.02%	1,588 / 2,268

Sampled at 50% (seed 42)

Speed Benchmarks

Measured with llama-bench. All layers GPU-offloaded (-ngl 99).

Prompt Processing (tokens/s)

Context Length	Speed	+/-sigma
128	3,661	20.2
512	9,468	59.1
2048	9,302	48.0
4096	9,026	17.3
8192	8,937	71.5
16384	8,643	98.6

Generation (tokens/s)

Metric	Speed	+/-sigma
tg128	304.8	1.2

Methodology

Evaluation Framework

Custom generative evaluators built for this rig. All benchmarks run through llama-server's /v1/chat/completions endpoint.

Scoring: Generative evaluation (not loglikelihood)
Thinking: disabled
MCQ scoring: First valid letter extracted from response (A/B/C/D)
Sampling: 50% of dataset used
Temperature: 0 (deterministic)
Max tokens: 2,048
GPU offload: All layers (-ngl 99)

Benchmarked by WITCHEER on the RTX 5090 Benchmark Rig. Source: github.com/notwitcheer/llm-bench-rig/blob/main/reports/north-mini-code.md. Dataset: huggingface.co/datasets/witcheer/rtx-5090-benchmarks/blob/main/reports/north-mini-code.md.