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DeepSeek-V4-Flash-0423 vs Muse Wonder

Muse Wonder leads the LLM Stats Score 49.3 to 35.9.

DeepSeek · Meta · Updated for 2026

Which is better?

Muse Wonder leads the overall LLM Stats Score 49.3 to 35.9, ranking #24 overall.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose DeepSeek-V4-Flash-0423

  • you need open weights you can self-host or fine-tune

Choose Muse Wonder

  • overall performance matters — it scores 49.3 and ranks #24 on LLM Stats
  • your work emphasizes coding and agents — it leads those capability indexes
  • you want the most recent training data — it shipped Oct 2026

At a glance

The differences that matter most.

DeepSeek-V4-Flash-0423
DeepSeek
Muse Wonder
Meta
Core performance indexes
LLM Stats Score
35.9
#99
49.3
#24
Reasoning
36.9
#88
37.8
#82
Coding
26.6
#80
38.4
#21
Agents
15.6
#103
37.2
#17
Cost, coverage & limits
Benchmark wins
—
—
Input price
$0.09 / M
— / M
Output price
$0.18 / M
— / M
Context window
1,048,576
—

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
DeepSeek-V4-Flash-0423
Muse Wonder
Tool use
15.4#104
19.2#74
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

19 reported for DeepSeek-V4-Flash-0423 · 16 for Muse Wonder

No common benchmarks found

DeepSeek-V4-Flash-0423 and Muse Wonderdon'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

Context Window

Maximum input and output token capacity

Only DeepSeek-V4-Flash-0423 specifies input context (1,048,576 tokens). Only DeepSeek-V4-Flash-0423 specifies output context (1,048,576 tokens).

DeepSeek
DeepSeek-V4-Flash-0423
Input1,048,576 tokens
Output1,048,576 tokens
Meta
Muse Wonder
Input- tokens
Output- tokens
Sat Oct 24 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Muse Wonder supports multimodal inputs, whereas DeepSeek-V4-Flash-0423 does not.

Muse Wonder can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-V4-Flash-0423

Text
Images
Audio
Video

Muse Wonder

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4-Flash-0423 is licensed under MIT, while Muse Wonder uses a proprietary license.

License differences may affect how you can use these models in commercial or open-source projects.

DeepSeek-V4-Flash-0423

MIT

Open weights

Muse Wonder

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek-V4-Flash-0423 was released on 2026-04-23, while Muse Wonder was released on 2026-10-12.

Muse Wonder is 6 months newer than DeepSeek-V4-Flash-0423.

DeepSeek-V4-Flash-0423

Apr 23, 2026

6 months ago

Muse Wonder

Oct 12, 2026

1 weeks ago

5mo newer

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Outputs Comparison

Notice missing or incorrect data?

Judge for yourself.

Run your own prompts against DeepSeek-V4-Flash-0423 and Muse Wonder side-by-side, then vote on the output you prefer.

DeepSeek-V4-Flash-0423
✓ Preferred
Muse Wonder
Open in Playground

FAQ

Common questions about DeepSeek-V4-Flash-0423 vs Muse Wonder.

Which is better, DeepSeek-V4-Flash-0423 or Muse Wonder?

Muse Wonder leads the LLM Stats Score 49.3 to 35.9. DeepSeek-V4-Flash-0423 is made by DeepSeek and Muse Wonder is made by Meta. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does DeepSeek-V4-Flash-0423 compare to Muse Wonder in benchmarks?

DeepSeek-V4-Flash-0423 scores CodeForces: 93.9%, HMMT Feb 26: 91.9%, LiveCodeBench: 88.4%, GPQA: 87.4%, MMLU-Pro: 86.4%. Muse Wonder scores MemoryBench: 85.6%, DeepSWE 1.1: 78.8%, OSWorld 2.1 (partial): 72.6%, JobBench: 70.5%, HealthBench Professional: 60.5%.

What are the context window sizes for DeepSeek-V4-Flash-0423 and Muse Wonder?

DeepSeek-V4-Flash-0423 supports 1.0M tokens and Muse Wonder supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between DeepSeek-V4-Flash-0423 and Muse Wonder?

Key differences include LLM Stats Score (35.9 vs 49.3), multimodal support (no vs yes), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4-Flash-0423 and Muse Wonder?

DeepSeek-V4-Flash-0423 is developed by DeepSeek and Muse Wonder is developed by Meta.

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