Muse Wonder vs Qwen2.5 7B Instruct
Muse Wonder leads the LLM Stats Score 47.9 to 2.5. Qwen2.5 7B Instruct is 26.7x cheaper per token.
Meta · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Muse Wonder leads the overall LLM Stats Score 47.9 to 2.5, ranking #29 overall.
On price, Qwen2.5 7B Instruct is roughly 26.7x 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
- overall performance matters — it scores 47.9 and ranks #29 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- 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 Qwen2.5 7B Instruct
- cost matters — it's about 26.7x cheaper per token
- you need open weights you can self-host or fine-tune
At a glance
The differences that matter most.
Individual benchmarks
16 reported for Muse Wonder · 14 for Qwen2.5 7B Instruct
Muse Wonder and Qwen2.5 7B Instructdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
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 13.3x more expensive than Qwen2.5 7B Instruct ($0.30/1M tokens).
For output processing, Muse Wonder ($20.00/1M tokens) is 66.7x more expensive than Qwen2.5 7B Instruct ($0.30/1M tokens).
In conclusion, Muse Wonder is more expensive than Qwen2.5 7B Instruct.*
* 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 Qwen2.5 7B Instruct's 131,072 tokens. Only Qwen2.5 7B Instruct specifies output context (8,192 tokens).
Input capabilities
Documented input modalities across available providers
Muse Wonder supports multimodal inputs, whereas Qwen2.5 7B Instruct does not.
Muse Wonder can handle both text and other forms of data like images, making it suitable for multimodal applications.
Muse Wonder
Qwen2.5 7B Instruct
License
Usage and distribution terms
Muse Wonder is licensed under a proprietary license, while Qwen2.5 7B Instruct 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 Qwen2.5 7B Instruct was released on 2024-09-19.
Muse Wonder is 25 months newer than Qwen2.5 7B Instruct.
Oct 12, 2026
0 days ago
2.1yr newerSep 19, 2024
2.1 years 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. Qwen2.5 7B Instruct is available from Together.
Muse Wonder
Qwen2.5 7B Instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against Muse Wonder and Qwen2.5 7B Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about Muse Wonder vs Qwen2.5 7B Instruct.