Swift 1.5 Qwen3.8-Flash-Next

Swift 1.5 Qwen3.8-Flash-Next is UkisAI's reasoning-efficient derivative of Qwen3.8-Flash-Next. It uses 63.4% fewer thinking tokens, with a 1.8x speed up while keeping the accuracy loss <1% vs base on xhigh.

Demo

We gave base Qwen3.8-Flash-Next and Swift 1.5 Qwen3.8-Flash-Next the same prompt:

Create a 3D endless runner that has the fast, playful feel of Subway Surfers, but make the world and characters your own. I want to run through a lively place, dodge things, collect rewards, and feel the pace build the longer I survive. Make it fun to control and visually memorable. Use your judgment for the setting, mechanics, and little details that make it feel like a real game. Build it so I can launch and play it locally, then tell me how to run it.

Try the game yourself here: https://ukisai.com/swift-games/flash-next

Base Qwen3.8-Flash-Next took 8 minutes 52 seconds to build its game. Swift 1.5 took 4 minutes 56 seconds.

Training approach

We made Swift Flash Next efficient by figuring out which tokens were linked to pathological overthinking and penalizing them without "attacking" the reasoning length directly then regained the accuracy with RL and OPD, leading to "compressed" token usage while maintaining accuracy.

Swift 1.5 produces shorter reasoning traces. In our testing, we also observe fewer overthinking errors.

This release also features our previously mentioned post-training methods adapted specifically for coding and long-horizon agent work such as personal agents, terminal use and software engineering.

Our training data is viewable here: https://huggingface.co/datasets/ukisai/Qwen3.8-27B-multi-turn-agent-sft albeit it is not used out of the box, but rather re-sampled, turned into proper RL environments etc.

Evaluation

All scores compare the Qwen3.8-Flash-Next BF16 base with the Swift 1.5 BF16 checkpoint. Token columns report thinking tokens, except Terminal-Bench 2.1, which reports total generated output tokens.

Efficiency across reasoning efforts

GPQA-Diamond at each reasoning_effort setting, Swift 1.5 against the base at the same setting:

At xhigh, Swift 1.5 trails base by 0.20 percentage points while using 55.8% fewer mean and 63.4% fewer median thinking tokens. Medium and low save tokens but also lose 2.62 and 2.42 percentage points respectively.

Quantized models

How to use

The BF16 checkpoint is about 360 GB, so it needs a multi-GPU node. We evaluated it with tensor parallelism 8.

GGUF download

The GGUF version is available for compatible llama.cpp-based runtimes. For the smallest files, use the GSQ-RCO GGUF version: 66.5–76 GB mixed-precision quants refined for Swift 1.5 Flash-Next.

UkisAI API

Swift 1.5 Flash-Next is served through an OpenAI-compatible API at https://ukisai.com/api/flash-next/v1. It is free for research purposes and needs no API key. The model id is flash-next.

from openai import OpenAI

client = OpenAI(base_url="https://ukisai.com/api/flash-next/v1", api_key="none")
response = client.chat.completions.create(
    model="flash-next",
    messages=[{"role": "user", "content": "Explain speculative decoding in two sentences."}],
)
print(response.choices[0].message.content)

curl https://ukisai.com/api/flash-next/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "flash-next", "messages": [{"role": "user", "content": "Hello, Swift."}]}'

vLLM

Use a vLLM build with Qwen3.8-Flash-Next support:

vllm serve ukisai/Swift-Qwen3.8-Flash-Next \
  --dtype bfloat16 \
  --tensor-parallel-size 8 \
  --max-model-len 262144 \
  --reasoning-parser qwen3 \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_xml \
  --trust-remote-code \
  --port 8000

Reasoning effort is set per request through the chat template, for example "chat_template_kwargs": {"reasoning_effort": "low"}. xhigh is the default; medium and low are also supported.

SGLang

Alternatively, use an SGLang build with Qwen3.8-Flash-Next support, such as the lmsysorg/sglang:qwen38flashnext image:

sglang serve \
  --model-path ukisai/Swift-Qwen3.8-Flash-Next \
  --tp 8 \
  --context-length 262144 \
  --mem-fraction-static 0.85 \
  --reasoning-parser qwen3 \
  --tool-call-parser qwen3_coder \
  --port 8000

Adjust tensor parallelism and context length to your GPU memory. See the base model's vLLM recipe and SGLang cookbook for installation and hardware-specific settings.

Transformers

Use a Transformers release with Qwen3.8-Flash-Next support:

import torch
from transformers import AutoModelForImageTextToText, AutoProcessor

model_id = "ukisai/Swift-Qwen3.8-Flash-Next"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

To extend the context to 1,000,000 tokens, follow the YaRN settings on the Qwen3.8-Flash-Next model card.

Optional MTP decoding

The checkpoint includes the base model's one-layer MTP head. To enable self-speculative decoding, append the corresponding flags to the server command above:

# vLLM
--speculative-config '{"method":"mtp","num_speculative_tokens":1}'

# SGLang
--speculative-algorithm NEXTN --speculative-num-steps 3 \
  --speculative-eagle-topk 1 --speculative-num-draft-tokens 4

The benchmark scores above were measured without MTP.

License and access

Swift 1.5 Qwen3.8-Flash-Next is a derivative of Qwen3.8-Flash-Next (Copyright (c) 2026 Qwen, Qwen Community License 1.0). UkisAI's contribution, including the adapted weights, is licensed under the Swift Open License v1.0. See NOTICE for the change notice and attribution details.

Personal, research, educational, evaluation, and commercial use of the Swift contribution are free for individuals and organizations with gross annual revenue, including affiliates, of up to US$1,000,000. Above that threshold, commercial use requires a separate Swift Enterprise License. Contact UkisAI for terms.

The base model's terms still apply to it. Under the Qwen Community License 1.0, organizations that run a Model-as-a-Service or AI Work Assistant business need a separate license from Qwen before any commercial use, and products above 100 million monthly active users or US$20 million monthly revenue must prominently display the model name. Nothing in the Swift Open License limits your rights in Qwen3.8-Flash-Next itself under the Qwen Community License.

Citation

@misc{swift-qwen3.8-flash-next,
  title  = {Swift 1.5 Qwen3.8-Flash-Next},
  author = {UkisAI},
  year   = {2026},
  url    = {https://huggingface.co/ukisai/Swift-Qwen3.8-Flash-Next}
}

Acknowledgements

We acknowledge the NVIDIA Innovation Lab, Amazon Web Services, and Google Cloud for providing compute credits and infrastructure support for Swift's development, training, and evaluation.