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DeepSeek-V4.1-Flash vs Muse Wonder

DeepSeek-V4.1-Flash and Muse Wonder are closely matched at 50.0 and 49.3 on the LLM Stats Score.

DeepSeek · Meta · Updated for 2026

Which is better?

DeepSeek-V4.1-Flash and Muse Wonder are closely matched on the overall LLM Stats Score at 50.0 and 49.3.

In the 2 individual benchmarks reported for both models, Muse Wonder wins 2; 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-V4.1-Flash

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

Choose Muse Wonder

  • you value its reported benchmark strengths — it wins 2 of 2 exact shared results
  • you want the most recent training data — it shipped Oct 2026

At a glance

The differences that matter most.

DeepSeek-V4.1-Flash
DeepSeek
Muse Wonder
Meta
Core performance indexes
LLM Stats Score
50.0
#19
49.3
#24
Reasoning
48.0
#22
37.8
#82
Coding
40.5
#10
38.4
#21
Agents
38.3
#11
37.2
#17
Cost, coverage & limits
Benchmark wins
0 of 2
2 of 2
Input price
$0.22 / M
— / M
Output price
$0.66 / M
— / M
Context window
1,040,000
—

Capability indexes

Additional strengths measured across groups of related public benchmarks

3 shared
Index
DeepSeek-V4.1-Flash
Muse Wonder
Vision
30.6#37
26.6#53
Tool use
32.3#5
19.2#74
Multimodal
32.3#24
26.6#53
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

20 reported for DeepSeek-V4.1-Flash · 16 for Muse Wonder

2 shared

DeepSeek-V4.1-Flash outperforms in 0 benchmarks, while Muse Wonder is better at 2 benchmarks (DeepSWE 1.1, Program Bench).

Muse Wonder significantly outperforms across most benchmarks.

Wed Oct 21 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Context Window

Maximum input and output token capacity

Only DeepSeek-V4.1-Flash specifies input context (1,040,000 tokens). Only DeepSeek-V4.1-Flash specifies output context (393,216 tokens).

DeepSeek
DeepSeek-V4.1-Flash
Input1,040,000 tokens
Output393,216 tokens
Meta
Muse Wonder
Input- tokens
Output- tokens
Wed Oct 21 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Both DeepSeek-V4.1-Flash and Muse Wonder support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

DeepSeek-V4.1-Flash

Text
Images
Audio
Video

Muse Wonder

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4.1-Flash 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-V4.1-Flash

MIT

Open weights

Muse Wonder

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek-V4.1-Flash was released on 2026-09-10, while Muse Wonder was released on 2026-10-12.

Muse Wonder is 1 month newer than DeepSeek-V4.1-Flash.

DeepSeek-V4.1-Flash

Sep 10, 2026

1 months ago

Muse Wonder

Oct 12, 2026

1 weeks ago

1mo 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-V4.1-Flash and Muse Wonder side-by-side, then vote on the output you prefer.

DeepSeek-V4.1-Flash
✓ Preferred
Muse Wonder
Open in Playground

FAQ

Common questions about DeepSeek-V4.1-Flash vs Muse Wonder.

Which is better, DeepSeek-V4.1-Flash or Muse Wonder?

DeepSeek-V4.1-Flash and Muse Wonder are closely matched on the LLM Stats Score at 50.0 and 49.3. DeepSeek-V4.1-Flash 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-V4.1-Flash compare to Muse Wonder in benchmarks?

DeepSeek-V4.1-Flash scores CodeForces: 100.0%, GPQA: 90.9%, Terminal-Bench 2.1: 90.6%, BabyVision: 89.6%, CyberGym: 88.1%. 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-V4.1-Flash and Muse Wonder?

DeepSeek-V4.1-Flash 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-V4.1-Flash and Muse Wonder?

Key differences include LLM Stats Score (50.0 vs 49.3), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4.1-Flash and Muse Wonder?

DeepSeek-V4.1-Flash is developed by DeepSeek and Muse Wonder is developed by Meta.

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