Muse Wonder vs Qwen3.8-27B
Muse Wonder and Qwen3.8-27B are closely matched at 47.9 and 44.6 on the LLM Stats Score. Qwen3.8-27B is 7.6x cheaper per token.
Meta · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Muse Wonder and Qwen3.8-27B are closely matched on the overall LLM Stats Score at 47.9 and 44.6.
In the 1 individual benchmarks reported for both models, Muse Wonder wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, Qwen3.8-27B is roughly 7.6x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Muse Wonder also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose Muse Wonder
- your work emphasizes coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Oct 2026
Choose Qwen3.8-27B
- cost matters — it's about 7.6x cheaper per token
- you need open weights you can self-host or fine-tune
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
16 reported for Muse Wonder · 26 for Qwen3.8-27B
Muse Wonder outperforms in 1 benchmarks (DeepSWE 1.1), while Qwen3.8-27B is better at 0 benchmarks.
Muse Wonder significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, Muse Wonder ($4.00/1M tokens) is 10.0x more expensive than Qwen3.8-27B ($0.40/1M tokens).
For output processing, Muse Wonder ($20.00/1M tokens) is 6.7x more expensive than Qwen3.8-27B ($3.00/1M tokens).
In conclusion, Muse Wonder is more expensive than Qwen3.8-27B.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Muse Wonder accepts 1,048,576 input tokens compared to Qwen3.8-27B's 262,144 tokens. Only Qwen3.8-27B specifies output context (262,144 tokens).
Input capabilities
Documented input modalities across available providers
Both Muse Wonder and Qwen3.8-27B support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Muse Wonder
Qwen3.8-27B
License
Usage and distribution terms
Muse Wonder is licensed under a proprietary license, while Qwen3.8-27B uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
Proprietary
Closed source
Apache 2.0
Open weights
Release Timeline
When each model was launched
Muse Wonder was released on 2026-10-12, while Qwen3.8-27B was released on 2026-08-14.
Muse Wonder is 2 months newer than Qwen3.8-27B.
Oct 12, 2026
-1 days ago
1mo newerAug 14, 2026
1 months ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
Muse Wonder is available from Meta Model API. Qwen3.8-27B is available from DeepInfra, FriendliAI.
Muse Wonder
Qwen3.8-27B
Outputs Comparison
Judge for yourself.
Run your own prompts against Muse Wonder and Qwen3.8-27B side-by-side, then vote on the output you prefer.
FAQ
Common questions about Muse Wonder vs Qwen3.8-27B.