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Swift-Qwen3.8-27B uses fewer tokens than its Qwen3.8-27B base model in every benchmark suite reported by UkisAI, but that does not establish a universal 50% speedup. In UkisAI’s BF16 evaluation, Swift’s accuracy was close on several tests, lower on AIME 2026 and HMMT, and higher on LiveCodeBench v6. The practical choice depends on the task, and token savings should be checked separately from real response time.
What is the difference between 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 through fine-tuning. UkisAI says it identified tokens associated with overthinking and penalized their use during training. The stated goal is to produce answers with fewer reasoning tokens; the publisher says Swift retains text, image, and video support. UkisAI’s comparison and its model card describe the models and approach.
In UkisAI’s reported BF16 results, Swift used fewer tokens on all nine benchmark suites. Accuracy, however, varied by task. The table below reports the publisher’s five-seed average scores and token reductions. The token measures are not identical across rows: LiveCodeBench reports completion tokens, most other suites report thinking tokens, and Terminal-Bench counts tokens per trial. UkisAI’s benchmark report provides the figures.
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How do the benchmark results compare?
| Benchmark | Qwen3.8-27B accuracy | Swift accuracy | Mean token reduction with Swift | Median token reduction with Swift |
|---|---|---|---|---|
| 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% |
All values are from UkisAI’s BF16 comparison, which reports five seeds per model and average scores. They are publisher-reported results, not independent measurements. Token reduction is not a wall-clock speed measurement.
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Where accuracy is close or higher with Swift
GPQA-Diamond scores were nearly equal: 88.28% for Swift versus 88.38% for Qwen3.8-27B. MMLU-Pro was also close, with Swift 0.52 percentage points lower. Swift scored higher on C-Eval by 0.62 points and on LiveCodeBench v6 by 4.79 points. Those results describe these particular evaluations; they do not guarantee the same outcome on a different dataset or production workload.
Where Swift scored lower
The largest listed BF16 accuracy gap was on AIME 2026: Swift scored 94.00% compared with 98.67% for the base, a difference of 4.67 percentage points. On HMMT (Nov 2025), Swift scored 96.00% versus 99.33%. Swift was also lower on IFBench, ERQA, and Terminal-Bench 2.1, though the size of the difference varied. If mathematical reasoning or strict instruction following is central to your use case, test the target workload rather than relying on the overall token reductions.
Does 50% fewer tokens mean 50% faster?
No. The prominent 58.3% figure is the median token reduction on GPQA-Diamond in UkisAI’s BF16 comparison—not a measured 58.3% reduction in response time, and not a universal 50% latency improvement. UkisAI separately says token reduction can produce speedups approaching 1.95× on some tasks, but its reviewed primary tables emphasize token use and accuracy. Actual end-to-end latency also depends on the serving system, hardware, concurrency, and workload. The benchmark report and publisher page should be read with that distinction in mind.
The Tool Desk
Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Across the nine suites, UkisAI reports mean token reductions ranging from 24.3% to 50.6%. Because the measured token type differs among benchmarks, those values are not a single, directly comparable speed score. Measure response time on the hardware and serving stack you plan to use if latency is the deciding factor.
Rank #3
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How was the BF16 comparison run?
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. The reported averages use five request seeds per model. Terminal-Bench used five trials per task, and IFBench used strict scoring. These settings are important context: results from other inference settings, quantizations, or evaluation harnesses may differ. The published evaluation details list the setup.
UkisAI says some base-model outputs were reused from saved runs. Its public evaluation repository includes per-sample responses, scores, configurations, and logs for nine benchmarks, but two large Terminal-Bench files were omitted because of GitHub size limits. UkisAI also says exact replay requires dataset snapshots and harness manifests retained internally. The materials make parts of the evaluation inspectable, but do not amount to an independent replication. The evaluation repository describes the available artifacts.
Rank #4
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Do quantized results tell the same story?
No: the model card reports separate comparisons under different quantization conditions, and those figures should not be combined with the BF16 table above. For example, in the reported mixed-precision W4A16 results, GPQA-Diamond accuracy was 88.69% for the base and 88.38% for Swift, with 32.1% mean and 50.2% median token reduction. On AIME 2026 under W4A16, both models scored 84.00%, with 19.0% mean token reduction. In the listed AWQ INT4 AIME comparison, the base scored 82.67% and Swift 84.00%, with 22.8% mean token reduction. These are the model card’s results for those specific conditions, not direct substitutes for the BF16 comparison. The model card contains the quantized figures.
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Choose based on the task and deployment you actually have, rather than the headline token statistic alone.
Best Value
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- Consider Swift if reducing token use is valuable and your own evaluation confirms acceptable accuracy. UkisAI’s results show fewer tokens on all nine suites, with a higher score on LiveCodeBench v6 in this comparison.
- Validate Qwen3.8-27B against Swift on math-heavy work. The reported AIME 2026 and HMMT scores are lower for Swift, so those tasks merit a direct comparison if they matter to your users.
- Measure both under your serving setup. Compare latency and token consumption using the same hardware, quantization, context length, concurrency, and representative prompts.
- Check the conditions behind any result. BF16 and quantized evaluations are distinct, and changes to reasoning effort or inference configuration can change outcomes.
Can you run Swift locally, and what about its license?
UkisAI’s model card provides local-serving examples for vLLM and SGLang and advises adjusting tensor parallelism and context length to available GPU memory. It does not establish one required or optimal GPU; the training report’s mention of access to 8× NVIDIA H100 GPUs describes training resources, not end-user requirements. The model card also says Swift is available through UkisAI’s OpenAI-compatible API and describes access as free for research purposes at the time of publication; availability and terms can change. Check the model card for current deployment details.
The card identifies the Qwen3.8-27B base as Apache License 2.0 and Swift’s fine-tuned weights as Swift Open License v1.0. It says personal, research, educational, evaluation, and commercial use is free for individuals and organizations whose gross annual revenue, including affiliates, is up to US$1,000,000; above that threshold, commercial use requires a separate Swift Enterprise License. Review the current license text before deploying, particularly for commercial use. The model card provides the stated terms.
Quick Recap
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