Is swift-1.5-9b good for coding?

In our benchmarks, swift-1.5-9b scores 43.3/100 for coding. It runs at about 41.1 tok/s, so if you want more speed, ornith-1.5-35b-a3b-apex-i-quality is faster (~73.2 tok/s) and still scores well for coding (83.1/100).

Is swift-1.5-9b good for agentic (tool-using) tasks?

In our benchmarks, swift-1.5-9b scores 73.6/100 for agentic workflows. It runs at about 41.1 tok/s, so if you want more speed, Nex-N2.5-mini-APEX-Mini is faster (~88.2 tok/s) and still scores well for agentic workflows (91.3/100).

How fast is swift-1.5-9b for local inference?

Across 14 community benchmark runs, swift-1.5-9b reaches up to 48.6 tok/s and averages 41.1 tok/s, with the fastest results on AMD Radeon RX 9050 / 9060 XT.

How much memory does swift-1.5-9b need?

The leanest observed configuration used about 9.4 GB of memory.

Which tools have been used to run swift-1.5-9b?

Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.

Model benchmarks

swift-1.5-9b local LLM performance

As of November 2026, swift-1.5-9b runs at up to 48.6 tok/s for local inference (best of 14 community benchmark runs across 1 GPU).

llama.cpp

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Model size

Peak speed

48.6 tok/s

Average speed

41.1 tok/s

Avg PP

1407.5 tok/s

Min memory

9.4 GB

Max context

200,000 tokens

Avg output / run

33,387 tokens

Avg runtime / run

14m 3s

Avg quality

71.5

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 14 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	56.7	71.5	78.0
Agent Workflow	35.2	73.6	89.0
Code Generation	21.5	43.3	62.2
Role Play & Narrative	78.1	85.5	92.4
Research & Analysis	77.3	83.7	89.7

Performance by hardware and tool

Every hardware/tool/quantization combination swift-1.5-9b has been benchmarked on, ranked by peak token generation speed. Last updated November 2026.

Hardware	Tool	Quant	Best tok/s	Avg tok/s	Memory	Context	Quality	Runs
AMD Radeon RX 9050 / 9060 XT	llama.cpp	—	48.6 tok/s	41.1 tok/s	9.4 GB	200,000 tokens	71.5	14

Benchmark runs

All 14 swift-1.5-9b runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.

Open run page →

Frequently asked questions

Is swift-1.5-9b good for coding?: In our benchmarks, swift-1.5-9b scores 43.3/100 for coding. It runs at about 41.1 tok/s, so if you want more speed, ornith-1.5-35b-a3b-apex-i-quality is faster (~73.2 tok/s) and still scores well for coding (83.1/100).
Is swift-1.5-9b good for agentic (tool-using) tasks?: In our benchmarks, swift-1.5-9b scores 73.6/100 for agentic workflows. It runs at about 41.1 tok/s, so if you want more speed, Nex-N2.5-mini-APEX-Mini is faster (~88.2 tok/s) and still scores well for agentic workflows (91.3/100).
How fast is swift-1.5-9b for local inference?: Across 14 community benchmark runs, swift-1.5-9b reaches up to 48.6 tok/s and averages 41.1 tok/s, with the fastest results on AMD Radeon RX 9050 / 9060 XT.
How much memory does swift-1.5-9b need?: The leanest observed configuration used about 9.4 GB of memory.
Which tools have been used to run swift-1.5-9b?: Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.