DeepSeek-V4-Flash-0731 vs Muse Glimmer-30B
DeepSeek-V4-Flash-0731 leads the LLM Stats Score 46.1 to 35.5.
DeepSeek · Meta · Updated for 2026
Which is better?
DeepSeek-V4-Flash-0731 leads the overall LLM Stats Score 46.1 to 35.5, ranking #23 overall.
In the 1 individual benchmarks reported for both models, DeepSeek-V4-Flash-0731 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 DeepSeek-V4-Flash-0731
- overall performance matters — it scores 46.1 and ranks #23 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
Choose Muse Glimmer-30B
- you want the most recent training data — it shipped Aug 2026
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
9 reported for DeepSeek-V4-Flash-0731 · 25 for Muse Glimmer-30B
DeepSeek-V4-Flash-0731 outperforms in 1 benchmarks (Terminal-Bench 2.1), while Muse Glimmer-30B is better at 0 benchmarks.
DeepSeek-V4-Flash-0731 significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Model Size
Parameter count comparison
DeepSeek-V4-Flash-0731 has 274.4B more parameters than Muse Glimmer-30B, making it 927.0% larger.
Context Window
Maximum input and output token capacity
Only DeepSeek-V4-Flash-0731 specifies input context (1,048,576 tokens). Only DeepSeek-V4-Flash-0731 specifies output context (384,000 tokens).
Input capabilities
Documented input modalities across available providers
Muse Glimmer-30B supports multimodal inputs, whereas DeepSeek-V4-Flash-0731 does not.
Muse Glimmer-30B can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4-Flash-0731
Muse Glimmer-30B
License
Usage and distribution terms
DeepSeek-V4-Flash-0731 is licensed under MIT, while Muse Glimmer-30B uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
DeepSeek-V4-Flash-0731 was released on 2026-07-31, while Muse Glimmer-30B was released on 2026-08-10.
Muse Glimmer-30B is 0 month newer than DeepSeek-V4-Flash-0731.
Jul 31, 2026
4 weeks ago
Aug 10, 2026
2 weeks ago
1w newerKnowledge Cutoff
When training data ends
Muse Glimmer-30B has a documented knowledge cutoff of 2026-01-04, while DeepSeek-V4-Flash-0731's cutoff date is not specified.
We can confirm Muse Glimmer-30B's training data extends to 2026-01-04, but cannot make a direct comparison without DeepSeek-V4-Flash-0731's cutoff date.
—
Jan 2026
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Flash-0731 and Muse Glimmer-30B side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Flash-0731 vs Muse Glimmer-30B.