Datasets:

witcheer
/

rtx-5090-benchmarks

	# 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).

	# 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).