Datasets:
File size: 5,606 Bytes
e61957f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 | # Benchmark Report: north-mini-code (unknown)
**Date:** 2026-06-15
**Author:** WITCHEER
**Platform:** NVIDIA GeForce RTX 5090 Benchmark Rig (capsule)
---
## Findings
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](https://github.com/ggml-org/llama.cpp/pull/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](https://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](https://huggingface.co/datasets/witcheer/rtx-5090-benchmarks/blob/main/reports/north-mini-code.md).*
|