qwen3.8-27b-mlx

qwen3_5 · 27B · 4bit

MacBook Pro (Apple M1 Pro)

32 GB · macOS 15.6.1

Tested on October 20, 2026

Top 47% Compare

Global Score

78 /100

Good

Hardware Fit

56/100

Quality

87/100

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Hardware

Machine: MacBook Pro
CPU: Apple M1 Pro
Cores: 10 total (8 perf + 2 eff)
Frequency: 2.4 GHz
RAM: 32 GB LPDDR5
GPU: Apple M1 Pro
OS: macOS 15.6.1
Arch: arm64
Power Mode: balanced

Performance

Tokens/sec Tokens generated per second — higher is better: 10.8
Standard deviation Variation of speed across benchmark runs — lower means more consistent: ±0.1
First chunk latency Delay before the first streamed chunk arrives from runtime: 8 ms
Time to first token How fast the model starts responding — lower is better: 2.6 s
Load time Time to load the model into memory before inference: N/A
Memory usage RAM consumed during inference — percentage of total system memory: 21.0 GB (66%)
Total tokens Total number of tokens generated across all benchmark prompts: 1261

Score breakdown

Speed

23/50

Time to first token

16/20

Memory

17/30

Quality

Reasoning

18/20

Coding

18/20

Instruction following

15/20

Structured output

15/15

Math

11/15

Multilingual

10/10

Category levels

Reasoning: Strong Coding: Strong Instruction Following: Strong Structured Output: Strong Math: Adequate Multilingual: Strong

Metadata

Spec version: 0.2.1
Runtime: LM Studio
Model format: MLX
Hardware profile: BALANCED
Result hash: db13a9b54d2dba223d6870b85b3e0fddddb86b3d61e672e7b0ca746620dc46c6

Interpretation

Hardware fit: 56/100. Overall suitability: GOOD (Global 78/100). Category profile: Reasoning: Strong, Coding: Strong, Instruction Following: Strong, Structured Output: Strong, Math: Adequate, Multilingual: Strong.

Warnings

Model memory footprint is estimated via LM Studio CLI rather than measured from a fresh load.

Bench Environment

Power: AC CPU load: avg 13% (peak 15%)

Run yours now

$ npm install -g metrillm@latest

$ metrillm

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Or run without installing: npx metrillm@latest