Is Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp good for coding?

In our benchmarks, Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp scores 79.2/100 for coding. It runs at about 39.1 tok/s, so if you want more speed, nvidia/Qwen3.8-27B-NVFP4 is faster (~63.3 tok/s) and still scores well for coding (82.7/100).

Is Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp good for agentic (tool-using) tasks?

In our benchmarks, Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp scores 82.1/100 for agentic workflows. It runs at about 39.1 tok/s, so if you want more speed, swift-iq2_xs is faster (~91.5 tok/s) and still scores well for agentic workflows (91.4/100).

How fast is Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp for local inference?

Across 7 community benchmark runs, Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp reaches up to 48.9 tok/s and averages 39.1 tok/s, with the fastest results on AMD Radeon RX 7800 XT.

How much memory does Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp need?

The leanest observed configuration used about 15.7 GB of memory.

Which tools have been used to run Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp?

Benchmarks were submitted using llama.cpp, llama.cpp k5 v5 16384 budget. Results are community-contributed and updated as new runs arrive.

Model benchmarks

Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp local LLM performance

As of October 2026, Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp runs at up to 48.9 tok/s for local inference (best of 7 community benchmark runs across 2 GPUs).

llama.cppllama.cpp k5 v5 16384 budget

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Model size

27B

Peak speed

48.9 tok/s

Average speed

39.1 tok/s

Avg PP

302.1 tok/s

Min memory

15.7 GB

Max context

131,072 tokens

Avg output / run

47,766 tokens

Avg runtime / run

20m 36s

Avg quality

83.1

Benchmark runs

GPUs tested

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 7 runs. The low and high columns show how much the judge’s score varies between runs, and need at least two runs to display.

Task	P5 (low)	Avg	P95 (high)
Overall	78.2	83.1	87.9
Agent Workflow	72.6	82.1	89.6
Code Generation	69.6	79.2	86.0
Role Play & Narrative	73.4	85.4	93.8
Research & Analysis	84.2	85.8	87.8

Performance by hardware and tool

Every hardware/tool/quantization combination Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp has been benchmarked on, ranked by peak token generation speed. Last updated October 2026.

Hardware	Tool	Quant	Best tok/s	Avg tok/s	Memory	Context	Quality	Runs
AMD Radeon RX 7800 XT	llama.cpp	—	48.9 tok/s	39.6 tok/s	15.7 GB	131,072 tokens	82.4	4
AMD Radeon RX 7800 XT	llama.cpp k5 v5 16384 budget	—	39.6 tok/s	39.6 tok/s	15.7 GB	65,536 tokens	87.4	1
NVIDIA GeForce RTX 5060	llama.cpp	—	38.3 tok/s	37.9 tok/s	n/a	98,304 tokens	82.4	2

Benchmark runs

All 7 Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.

Open run page →

Frequently asked questions

Is Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp good for coding?: In our benchmarks, Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp scores 79.2/100 for coding. It runs at about 39.1 tok/s, so if you want more speed, nvidia/Qwen3.8-27B-NVFP4 is faster (~63.3 tok/s) and still scores well for coding (82.7/100).
Is Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp good for agentic (tool-using) tasks?: In our benchmarks, Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp scores 82.1/100 for agentic workflows. It runs at about 39.1 tok/s, so if you want more speed, swift-iq2_xs is faster (~91.5 tok/s) and still scores well for agentic workflows (91.4/100).
How fast is Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp for local inference?: Across 7 community benchmark runs, Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp reaches up to 48.9 tok/s and averages 39.1 tok/s, with the fastest results on AMD Radeon RX 7800 XT.
How much memory does Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp need?: The leanest observed configuration used about 15.7 GB of memory.
Which tools have been used to run Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp?: Benchmarks were submitted using llama.cpp, llama.cpp k5 v5 16384 budget. Results are community-contributed and updated as new runs arrive.