Muse Wonder vs Phi-3.5-mini-instruct
Muse Wonder leads the LLM Stats Score 47.9 to -3.8. Phi-3.5-mini-instruct is 80.0x cheaper per token.
Meta · Microsoft · Updated for 2026
Which is better?
Muse Wonder leads the overall LLM Stats Score 47.9 to -3.8, ranking #29 overall.
On price, Phi-3.5-mini-instruct is roughly 80.0x 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 Phi-3.5-mini-instruct
- cost matters — it's about 80.0x 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 · 31 for Phi-3.5-mini-instruct
Muse Wonder and Phi-3.5-mini-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 40.0x more expensive than Phi-3.5-mini-instruct ($0.10/1M tokens).
For output processing, Muse Wonder ($20.00/1M tokens) is 200.0x more expensive than Phi-3.5-mini-instruct ($0.10/1M tokens).
In conclusion, Muse Wonder is more expensive than Phi-3.5-mini-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 Phi-3.5-mini-instruct's 128,000 tokens. Only Phi-3.5-mini-instruct specifies output context (128,000 tokens).
Input capabilities
Documented input modalities across available providers
Muse Wonder supports multimodal inputs, whereas Phi-3.5-mini-instruct does not.
Muse Wonder can handle both text and other forms of data like images, making it suitable for multimodal applications.
Muse Wonder
Phi-3.5-mini-instruct
License
Usage and distribution terms
Muse Wonder is licensed under a proprietary license, while Phi-3.5-mini-instruct uses MIT.
License differences may affect how you can use these models in commercial or open-source projects.
Proprietary
Closed source
MIT
Open weights
Release Timeline
When each model was launched
Muse Wonder was released on 2026-10-12, while Phi-3.5-mini-instruct was released on 2024-08-23.
Muse Wonder is 26 months newer than Phi-3.5-mini-instruct.
Oct 12, 2026
0 days ago
2.1yr newerAug 23, 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. Phi-3.5-mini-instruct is available from Azure.
Muse Wonder
Phi-3.5-mini-instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against Muse Wonder and Phi-3.5-mini-instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about Muse Wonder vs Phi-3.5-mini-instruct.