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DeepSeek-V4.1-Flash-1017 vs Muse Wonder Full

DeepSeek-V4.1-Flash-1017 and Muse Wonder Full are closely matched at 51.0 and 52.5 on the LLM Stats Score.

DeepSeek · Meta · Updated for 2026

Which is better?

DeepSeek-V4.1-Flash-1017 and Muse Wonder Full are closely matched on the overall LLM Stats Score at 51.0 and 52.5.

In the 2 individual benchmarks reported for both models, Muse Wonder Full 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-1017

  • you want the most recent training data — it shipped Oct 2026
  • you need open weights you can self-host or fine-tune

Choose Muse Wonder Full

  • you value its reported benchmark strengths — it wins 2 of 2 exact shared results

At a glance

The differences that matter most.

DeepSeek-V4.1-Flash-1017
DeepSeek
Muse Wonder Full
Meta
Core performance indexes
LLM Stats Score
51.0
#15
52.5
#12
Reasoning
48.6
#18
41.2
#61
Coding
39.1
#15
41.2
#9
Agents
38.6
#9
40.6
#6
Cost, coverage & limits
Benchmark wins
0 of 2
2 of 2
Input price
$0.30 / M
— / M
Output price
$1.20 / M
— / M
Context window
1,048,576
—

Capability indexes

Additional strengths measured across groups of related public benchmarks

3 shared
Index
DeepSeek-V4.1-Flash-1017
Muse Wonder Full
Vision
32.2#26
31.4#31
Tool use
30.9#7
24.6#39
Multimodal
35.0#14
31.4#31
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

11 reported for DeepSeek-V4.1-Flash-1017 · 16 for Muse Wonder Full

2 shared

DeepSeek-V4.1-Flash-1017 outperforms in 0 benchmarks, while Muse Wonder Full is better at 2 benchmarks (DeepSWE 1.1, Program Bench).

Muse Wonder Full significantly outperforms across most benchmarks.

Mon Oct 19 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-1017 specifies input context (1,048,576 tokens). Only DeepSeek-V4.1-Flash-1017 specifies output context (393,216 tokens).

DeepSeek
DeepSeek-V4.1-Flash-1017
Input1,048,576 tokens
Output393,216 tokens
Meta
Muse Wonder Full
Input- tokens
Output- tokens
Mon Oct 19 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Both DeepSeek-V4.1-Flash-1017 and Muse Wonder Full support multimodal inputs.

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

DeepSeek-V4.1-Flash-1017

Text
Images
Audio
Video

Muse Wonder Full

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4.1-Flash-1017 is licensed under MIT, while Muse Wonder Full uses a proprietary license.

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

DeepSeek-V4.1-Flash-1017

MIT

Open weights

Muse Wonder Full

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek-V4.1-Flash-1017 was released on 2026-10-17, while Muse Wonder Full was released on 2026-10-12.

DeepSeek-V4.1-Flash-1017 is 0 month newer than Muse Wonder Full.

DeepSeek-V4.1-Flash-1017

Oct 17, 2026

2 days ago

5d newer
Muse Wonder Full

Oct 12, 2026

1 weeks ago

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-1017 and Muse Wonder Full side-by-side, then vote on the output you prefer.

DeepSeek-V4.1-Flash-1017
✓ Preferred
Muse Wonder Full
Open in Playground

FAQ

Common questions about DeepSeek-V4.1-Flash-1017 vs Muse Wonder Full.

Which is better, DeepSeek-V4.1-Flash-1017 or Muse Wonder Full?

DeepSeek-V4.1-Flash-1017 and Muse Wonder Full are closely matched on the LLM Stats Score at 51.0 and 52.5. DeepSeek-V4.1-Flash-1017 is made by DeepSeek and Muse Wonder Full 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-1017 compare to Muse Wonder Full in benchmarks?

DeepSeek-V4.1-Flash-1017 scores BabyVision: 92.5%, Chartography: 83.0%, DeepSWE 1.1: 77.0%, NL2Repo: 68.2%, AutomationBench: 60.5%. Muse Wonder Full scores MemoryBench: 85.5%, DeepSWE 1.1: 79.0%, OSWorld 2.1 (partial): 78.4%, JobBench: 75.3%, HealthBench Professional: 62.3%.

What are the context window sizes for DeepSeek-V4.1-Flash-1017 and Muse Wonder Full?

DeepSeek-V4.1-Flash-1017 supports 1.0M tokens and Muse Wonder Full 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-1017 and Muse Wonder Full?

Key differences include LLM Stats Score (51.0 vs 52.5), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4.1-Flash-1017 and Muse Wonder Full?

DeepSeek-V4.1-Flash-1017 is developed by DeepSeek and Muse Wonder Full is developed by Meta.

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