qwen3.5-4b-super-coder

qwen35 · 4B · Q4_0

Gigabyte Technology Co., Ltd. H310M S2 2.0 (Intel Core™ i3-9100F)

16 GB · Майкрософт Windows 10 Pro 10.0.19045

Tested on October 24, 2026

Top 30% Compare

Global Score

83 /100

Excellent

Hardware Fit

96/100

Quality

77/100

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Hardware

Machine: Gigabyte Technology Co., Ltd. H310M S2 2.0
CPU: Intel Core™ i3-9100F
Cores: 4 total (4 perf)
Frequency: 3.6 GHz
RAM: 16 GB DDR4
GPU: Virtual Desktop Monitor, NVIDIA GeForce GTX 1650
OS: Майкрософт Windows 10 Pro 10.0.19045
Arch: x64
Power Mode: balanced

Performance

Tokens/sec Tokens generated per second — higher is better: 15.3
Standard deviation Variation of speed across benchmark runs — lower means more consistent: ±2.0
First chunk latency Delay before the first streamed chunk arrives from runtime: 33 ms
Time to first token How fast the model starts responding — lower is better: 686 ms
Load time Time to load the model into memory before inference: N/A
Memory usage RAM consumed during inference — percentage of total system memory: 3.6 GB (22%)
Total tokens Total number of tokens generated across all benchmark prompts: 1302

Score breakdown

Speed

46/50

Time to first token

20/20

Memory

30/30

Quality

Reasoning

16/20

Coding

13/20

Instruction following

13/20

Structured output

15/15

Math

11/15

Multilingual

9/10

Category levels

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

Metadata

Spec version: 0.2.1
Runtime: LM Studio 0.4.11+1
Model format: GGUF
Hardware profile: ENTRY
Result hash: d12c3d524b7793a08c231cbd9edddc2671008ae8101c6a80e787c0e33dfa5d93

Interpretation

Hardware fit: 96/100. Overall suitability: EXCELLENT (Global 83/100). Category profile: Reasoning: Strong, Coding: Adequate, Instruction Following: Adequate, 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

Swap delta: +0.0 GB CPU load: avg 55% (peak 60%)

Run yours now

$ npm install -g metrillm@latest

$ metrillm

Requires Node 20+ and Ollama or LM Studio running

Or run without installing: npx metrillm@latest