qwen36-distill-apex

THINKING MODEL

MacBook Pro (Apple M5 Max)

64 GB · macOS 27.0

Tested on July 17, 2026 · Submitted by itxjobe

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Global Score

36 /100

Not Rec.

Hardware Fit

100/100

Quality

8/100

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Ollama

ollama pull qwen36-distill-apex

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ollama.com/library/qwen36-distill-apex

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GGUF versions & quantizations

Hardware

Machine: MacBook Pro
CPU: Apple M5 Max
Cores: 18 threads (6 cores)
Frequency: 2.4 GHz
RAM: 64 GB LPDDR5
GPU: Apple M5 Max
OS: macOS 27.0
Arch: arm64
Power Mode: balanced

Performance

Tokens/sec Tokens generated per second — higher is better: 55.2
Standard deviation Variation of speed across benchmark runs — lower means more consistent: ±0.3
First chunk latency Delay before the first streamed chunk arrives from runtime: 210 ms
Time to first token How fast the model starts responding — lower is better: 210 ms
Load time Time to load the model into memory before inference: 4.8 s
Memory usage RAM consumed during inference — percentage of total system memory: 0.9 GB (2%)
Total tokens Total number of tokens generated across all benchmark prompts: 1379

Score breakdown

Speed

50/50

Time to first token

20/20

Memory

30/30

Quality

Reasoning

8/20

Coding

0/20

Instruction following

0/20

Structured output

0/15

Math

0/15

Multilingual

0/10

Category levels

Reasoning: Weak Coding: Poor Instruction Following: Poor Structured Output: Poor Math: Poor Multilingual: Poor

Metadata

Spec version: 0.2.1
Runtime: Ollama 0.20.7
Model format: GGUF
Hardware profile: HIGH-END
Result hash: 73e50e680badb50743cb5dfba85f19f3f18dc5e9be4d703a51eb1d38f790d92d

Interpretation

Hardware fit: 100/100. Overall suitability: NOT RECOMMENDED (Global 36/100). Category profile: Reasoning: Weak, Coding: Poor, Instruction Following: Poor, Structured Output: Poor, Math: Poor, Multilingual: Poor. Warning: model produced very low accuracy on quality tasks — results may be unusable despite good hardware performance.

Warnings

Model produced very low accuracy on quality tasks — results may be unusable despite good hardware performance.

Bench Environment

Power: AC CPU load: avg 11% (peak 13%)

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$ npm install -g metrillm@latest

$ metrillm

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Or run without installing: npx metrillm@latest