Model benchmarks

qwen3.5-9b-sushi-coder-rl local LLM performance

As of May 2026, qwen3.5-9b-sushi-coder-rl runs at up to 35.7 tok/s for local inference (best of 2 community benchmark runs across 1 GPU).

LM Studio
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Model size

9B

Peak speed

35.7 tok/s

Average speed

34.4 tok/s

Min memory

3.4 GB

Max context

8,192 tokens

Avg quality

54.8

Benchmark runs

2

GPUs tested

1

Quality by task

Average LLM-judged quality (0–100) with the run-to-run spread shown as a P5–P95 band, overall and for each benchmark task, across all 2 runs. The low and high columns show how much the judge’s score varies between runs, and need at least two runs to display.

TaskP5 (low)AvgP95 (high)
Overall48.054.861.6
Agent Workflow67.271.676.0
Code Generation47.150.754.3
Role Play & Narrative7.039.070.9
Research & Analysis54.158.061.9

Performance by hardware and tool

Every hardware/tool/quantization combination qwen3.5-9b-sushi-coder-rl has been benchmarked on, ranked by peak token generation speed. Last updated May 2026.

HardwareToolQuantBest tok/sAvg tok/sMemoryContextQualityRuns
NVIDIA GeForce RTX 3060 TiLM Studio35.7 tok/s34.4 tok/s3.4 GB8,192 tokens54.82

Frequently asked questions

Is qwen3.5-9b-sushi-coder-rl good for coding?
In our benchmarks, qwen3.5-9b-sushi-coder-rl scores 50.7/100 for coding. It runs at about 34.4 tok/s, so if you want more speed, Gemma4:E2B/QAT-MTP@131K is faster (~303.9 tok/s) and still scores well for coding (66.9/100).
Is qwen3.5-9b-sushi-coder-rl good for agentic (tool-using) tasks?
In our benchmarks, qwen3.5-9b-sushi-coder-rl scores 71.6/100 for agentic workflows. It runs at about 34.4 tok/s, so if you want more speed, Gemma4:E2B/QAT-MTP@131K is faster (~303.9 tok/s) and still scores well for agentic workflows (71.7/100).
How fast is qwen3.5-9b-sushi-coder-rl for local inference?
Across 2 community benchmark runs, qwen3.5-9b-sushi-coder-rl reaches up to 35.7 tok/s and averages 34.4 tok/s, with the fastest results on NVIDIA GeForce RTX 3060 Ti.
How much memory does qwen3.5-9b-sushi-coder-rl need?
The leanest observed configuration used about 3.4 GB of memory.
Which tools have been used to run qwen3.5-9b-sushi-coder-rl?
Benchmarks were submitted using LM Studio. Results are community-contributed and updated as new runs arrive.