qwen3.5-2b-mlx

qwen3_5 · 2B · 4bit

Mac mini (Apple M4 Pro)

64 GB · macOS 15.7.4

Tested on March 6, 2026

Top 55% Compare

Global Score

65 /100

Good

Hardware Fit

88/100

Quality

55/100

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MLX versions & conversions

Hardware

Machine: Mac mini
CPU: Apple M4 Pro
Cores: 14 total (10 perf + 4 eff)
Frequency: 2.4 GHz
RAM: 64 GB LPDDR5
GPU: Apple M4 Pro
OS: macOS 15.7.4
Arch: arm64
Power Mode: balanced

Performance

Tokens/sec Tokens generated per second — higher is better: 48.9
Standard deviation Variation of speed across benchmark runs — lower means more consistent: ±85.3
First chunk latency Delay before the first streamed chunk arrives from runtime: 4.2 s
Time to first token How fast the model starts responding — lower is better: 4.2 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: 1.6 GB (3%)
Total tokens Total number of tokens generated across all benchmark prompts: 461

Score breakdown

Speed

50/50

Time to first token

8/20

Memory

30/30

Quality

Reasoning

10/20

Coding

7/20

Instruction following

13/20

Structured output

14/15

Math

3/15

Multilingual

8/10

Category levels

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

Metadata

Spec version: 0.2.1
Runtime: LM Studio 0.4.6+1
Model format: MLX
Hardware profile: HIGH-END
Result hash: b500426de412cc0d6a0c21f529f16783f8983137d51f97b7d6fc4b284bad8784

Interpretation

Hardware fit: 88/100. Overall suitability: GOOD (Global 65/100). Category profile: Reasoning: Adequate, Coding: Weak, Instruction Following: Adequate, Structured Output: Strong, Math: Poor, Multilingual: Strong.

Warnings

Token speed is unstable (stddev 85.3 tok/s, mean 48.9 tok/s) — may indicate thermal throttling or memory pressure.

Bench Environment

Power: AC CPU load: avg 31% (peak 37%)

Run yours now

$ npm install -g metrillm@latest

$ metrillm

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Or run without installing: npx metrillm@latest