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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallUkisAI’s published comparison shows Swift-Qwen3.8-27B using fewer tokens than Qwen3.8-27B on all nine tested benchmark suites, but it does not establish a universal 50% improvement in response speed. Accuracy is close on some tasks, higher for Swift on LiveCodeBench v6, and lower on the listed AIME 2026 and HMMT math results. The practical choice depends on the workload—and the benchmark figures are UkisAI’s own, not an independent replication.
What are Qwen3.8-27B and Swift?
Qwen3.8-27B is the base model in this comparison. Swift-Qwen3.8-27B is a separate model made by UkisAI from that base. UkisAI says it penalized tokens associated with overthinking during training, aiming to produce shorter reasoning. The publisher says Swift retains text, image, and video support. (UkisAI’s model page; model card)
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“Fewer tokens” describes the amount of text generated in the reported tests. It is not, by itself, a measurement of how long a user waits for a response.
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What do UkisAI’s benchmark results show?
The table summarizes UkisAI’s BF16 comparison. Scores are average benchmark accuracy; token reductions are reported separately. Most rows measure thinking tokens, LiveCodeBench v6 measures completion tokens, and Terminal-Bench 2.1 counts tokens per complete trial, so the reductions are not perfectly like-for-like measures of a single speed metric.
| Benchmark | Qwen3.8-27B accuracy | Swift accuracy | Mean token reduction | Median token reduction |
|---|---|---|---|---|
| GPQA-Diamond | 88.38% | 88.28% | 41.0% | 58.3% |
| MMLU-Pro | 85.47% | 84.95% | 46.2% | 28.3% |
| C-Eval | 90.00% | 90.62% | 46.1% | 19.3% |
| IFBench | 73.53% | 71.80% | 42.2% | 50.5% |
| AIME 2026 | 98.67% | 94.00% | 26.7% | 50.2% |
| HMMT (Nov 2025) | 99.33% | 96.00% | 31.1% | 45.9% |
| ERQA | 67.45% | 66.30% | 50.6% | 54.6% |
| Terminal-Bench 2.1 | 66.74% | 65.84% | 26.5% | 38.7% |
| LiveCodeBench v6 | 76.76% | 81.55% | 24.3% | 45.8% |
Source for all figures: UkisAI’s BF16 comparison; the model card reproduces the table. UkisAI reports five request seeds per model and average scores. Figures are publisher-reported.
Where Swift is close, higher, or lower
- Close scores: GPQA-Diamond differs by 0.10 percentage points and MMLU-Pro by 0.52 points, with Swift lower in both comparisons.
- Higher for Swift: C-Eval is 0.62 percentage points higher, and LiveCodeBench v6 is 4.79 points higher. These are results on those particular published benchmarks, not guarantees for every language or coding task.
- Lower for Swift: the clearest listed math differences are AIME 2026, down 4.67 percentage points (98.67% to 94.00%), and HMMT Nov 2025, down 3.33 points (99.33% to 96.00%). IFBench is 1.73 points lower under strict scoring.
Does a 58.3% token reduction mean Swift is 50% faster?
No. The 58.3% figure is the median token reduction on GPQA-Diamond in UkisAI’s comparison, while its mean reduction on that benchmark is 41.0%. Neither figure is a universal wall-clock speed measurement. Across the nine suites, reported mean token reductions range from 24.3% on LiveCodeBench v6 to 50.6% on ERQA. (UkisAI benchmark page)
UkisAI says token reductions can produce speed-ups approaching 1.95× on some tasks, but the comparison’s primary tables emphasize token counts and accuracy. Actual response time also depends on serving hardware, software, concurrency, and the workload. The publisher’s token results therefore support a claim of reduced generation in these evaluations—not a promise that every response will arrive 50% sooner.
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UkisAI reports using vLLM 0.27.1, the Qwen3 reasoning parser, a 262,144-token context, xhigh reasoning effort, temperature 1.0, top_p 0.95, top_k 20, min_p 0, presence penalty 0, and repetition penalty 1. It reports five seeds per model; Terminal-Bench used five trials per task. IFBench used strict scoring. (published evaluation)
UkisAI says some Qwen base-model outputs were reused from saved runs. Its public evaluation repository includes per-sample responses, scores, configurations, and logs for nine benchmarks, but omits two large Terminal-Bench files because of GitHub size limits. UkisAI says exact replay depends on dataset snapshots and harness manifests retained internally. The released artifacts aid inspection, but do not make this an independent replication.
Quantized results are separate comparisons
The model card also lists quantized evaluations. They should not be merged with the BF16 figures because the precision and evaluation conditions differ.
| Quantization and benchmark | Qwen3.8-27B accuracy | Swift accuracy | Mean token reduction |
|---|---|---|---|
| Mixed-precision W4A16, GPQA-Diamond | 88.69% | 88.38% | 32.1% |
| Mixed-precision W4A16, IFBench | 72.58% | 71.25% | 30.1% |
| Mixed-precision W4A16, AIME 2026 | 84.00% | 84.00% | 19.0% |
| AWQ INT4, AIME 2026 | 82.67% | 84.00% | 22.8% |
Source: UkisAI model card.
Which model should you choose?
Use the published results as a reason to test Swift, not as a substitute for testing your own prompts. A benchmark measures a defined task under a particular setup; production results can differ.
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- For math-heavy work: validate Swift against your own problems, with particular attention to AIME 2026 and HMMT, where its listed BF16 accuracy is lower.
- For coding: Swift’s higher LiveCodeBench v6 score is encouraging for that evaluation, but does not establish better performance on every coding workflow.
- For reasoning workloads where shorter outputs matter: compare both models on the same prompts and measure accuracy, output length, and actual latency on your serving stack.
- For constrained hardware: compare the required quantization, context length, and tensor-parallel setup on the GPUs you intend to use. The model card’s local-serving examples say to adjust tensor parallelism and context length to available GPU memory.
Can you run Swift locally, and what license applies?
The model card provides local-serving examples for vLLM and SGLang and describes access through UkisAI’s OpenAI-compatible API. It described the API as free for research purposes at the time of the card’s review; availability and terms can change. The publisher reports using 8× NVIDIA H100 GPUs made available through NVIDIA Innovation Lab for training, but that is a training-resource statement, not a minimum hardware requirement for users. (model card)
The same card identifies Qwen3.8-27B as Apache License 2.0 and Swift’s fine-tuned weights as Swift Open License v1.0. It says individuals and organizations with gross annual revenue, including affiliates, up to US$1,000,000 may use Swift for personal, research, educational, evaluation, and commercial purposes without charge; above that threshold, commercial use requires a separate Swift Enterprise License. Review the current license text before deployment, particularly for commercial use.
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