Ember-1 vs Muse Wonder Full
Muse Wonder Full leads the LLM Stats Score 52.1 to 42.7.
Fireworks AI · Meta · Updated for 2026
Which is better?
Muse Wonder Full leads the overall LLM Stats Score 52.1 to 42.7, ranking #13 overall.
In the 1 individual benchmarks reported for both models, Muse Wonder Full wins 1; this is a narrower head-to-head signal than the composite indexes.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose Ember-1
- you want predictable pricing at $3.00/M input and $15.00/M output
Choose Muse Wonder Full
- overall performance matters — it scores 52.1 and ranks #13 on LLM Stats
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- you want the most recent training data — it shipped Oct 2026
At a glance
The differences that matter most.
Individual benchmarks
4 reported for Ember-1 · 16 for Muse Wonder Full
Ember-1 outperforms in 0 benchmarks, while Muse Wonder Full is better at 1 benchmark (DeepSWE 1.1).
Muse Wonder Full significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Context Window
Maximum input and output token capacity
Only Ember-1 specifies input context (1,040,000 tokens).
Input capabilities
Documented input modalities across available providers
Muse Wonder Full supports multimodal inputs, whereas Ember-1 does not.
Muse Wonder Full can handle both text and other forms of data like images, making it suitable for multimodal applications.
Ember-1
Muse Wonder Full
License
Usage and distribution terms
Both models are licensed under proprietary licenses.
Both models have usage restrictions defined by their respective organizations.
Proprietary
Closed source
Proprietary
Closed source
Release Timeline
When each model was launched
Ember-1 was released on 2026-09-23, while Muse Wonder Full was released on 2026-10-12.
Muse Wonder Full is 1 month newer than Ember-1.
Sep 23, 2026
2 weeks ago
Oct 12, 2026
0 days ago
2w newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Outputs Comparison
Judge for yourself.
Run your own prompts against Ember-1 and Muse Wonder Full side-by-side, then vote on the output you prefer.
FAQ
Common questions about Ember-1 vs Muse Wonder Full.