LLM Reference

Qwen3.8-Flash-Next

Released
2026-08-26
Last refreshed
2026-08-26
Status
Researched 1d ago
Open weightsCommercial use: conditionalMultimodalCodingLong contextVision

Qwen3.8-Flash-Next is available now for coding, long context, and vision with open-weight and 262k context; evaluate it while provider pricing coverage matures.

Use it for

  • Teams evaluating coding, long context, and vision
  • Workloads that can use a 262k context window

Do not use it for

  • Cost-sensitive launches that need sourced token pricing
  • Strict JSON or tool-calling flows
  • Teams that need a tracked hosted API route today
Specifications
Family
Qwen3.8
Released
2026-08-26
Context
262k
Parameters
125B total, 6B active (+51B n-gram embedding, 4B MTP)
Architecture
Mixture of Experts
Specialization
general
Openness
Open weights
License
Qwen Community License 1.0Commercial use: conditional
Weights
Available
Code
Unknown
Created by

AI research institute of Alibaba Group.

Hangzhou, Zhejiang, China
Founded 2017
Website
Pricing

No tracked provider token pricing is available yet.

About

Qwen3.8-Flash-Next is Alibaba's experimental open-weight preview of the architecture planned for Qwen4. It is a 125B-total / 6B-active Mixture-of-Experts causal language model with a vision encoder, plus 51B n-gram embedding and 4B MTP parameters. Native context is 262,144 tokens (extensible to 1,000,000). Weights are on Hugging Face under the Qwen Community License 1.0. No first-party hosted token prices are seeded.

Top use-case fit: coding, agents, and build tasks

Coding

1 relevant benchmark in the decision map.

Long context

Included by capability and metadata signals in the decision map.

Vision

Included by capability and metadata signals in the decision map.

Provider price ladder

No tracked provider token pricing is available for this model yet.

Capabilities

VisionMultimodalReasoning

Benchmark peer barsfor Coding

Benchmark scores(3)

Scores are benchmark-specific and are direction-aware: the same numeric gap can mean very different outcomes across suites. Use the leaderboard context and this model's provider route to decide whether the winning margin is meaningful for your workload.
BenchmarkScoreVersionEvaluationSource
DeepSWE 1.158.7DeepSWE 1.1Observed 2026-08-26Source
SWE-bench Pro62.5SWE-bench ProObserved 2026-08-26Source
Google-Proof Q&A91.7GPQA DiamondObserved 2026-08-26Source

Migration checks

No linked migration route is available for this model yet.

API versions

Qwen3.8-Flash-NextQwen/Qwen3.8-Flash-Next