Kimi K2-Thinking-0905 vs Muse Wonder
Muse Wonder leads the LLM Stats Score 47.9 to 35.8. Kimi K2-Thinking-0905 is 9.4x cheaper per token.
Moonshot AI · Meta · Updated for 2026
Which is better?
Muse Wonder leads the overall LLM Stats Score 47.9 to 35.8, ranking #29 overall.
On price, Kimi K2-Thinking-0905 is roughly 9.4x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Muse Wonder also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose Kimi K2-Thinking-0905
- cost matters — it's about 9.4x cheaper per token
- you need open weights you can self-host or fine-tune
Choose Muse Wonder
- overall performance matters — it scores 47.9 and ranks #29 on LLM Stats
- your work emphasizes coding and agents — it leads those capability indexes
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Oct 2026
At a glance
The differences that matter most.
Individual benchmarks
21 reported for Kimi K2-Thinking-0905 · 16 for Muse Wonder
Kimi K2-Thinking-0905 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
Pricing Analysis
Price comparison per million tokens
For input processing, Kimi K2-Thinking-0905 ($0.47/1M tokens) is 8.5x cheaper than Muse Wonder ($4.00/1M tokens).
For output processing, Kimi K2-Thinking-0905 ($2.00/1M tokens) is 10.0x cheaper than Muse Wonder ($20.00/1M tokens).
In conclusion, Muse Wonder is more expensive than Kimi K2-Thinking-0905.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Muse Wonder accepts 1,048,576 input tokens compared to Kimi K2-Thinking-0905's 262,144 tokens. Only Kimi K2-Thinking-0905 specifies output context (262,144 tokens).
Input capabilities
Documented input modalities across available providers
Muse Wonder supports multimodal inputs, whereas Kimi K2-Thinking-0905 does not.
Muse Wonder can handle both text and other forms of data like images, making it suitable for multimodal applications.
Kimi K2-Thinking-0905
Muse Wonder
License
Usage and distribution terms
Kimi K2-Thinking-0905 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.
MIT
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
Kimi K2-Thinking-0905 was released on 2025-09-05, while Muse Wonder was released on 2026-10-12.
Muse Wonder is 13 months newer than Kimi K2-Thinking-0905.
Sep 5, 2025
1.1 years ago
Oct 12, 2026
-1 days ago
1.1yr newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
Kimi K2-Thinking-0905 is available from DeepInfra, Novita, Fireworks. Muse Wonder is available from Meta Model API.
Kimi K2-Thinking-0905
Muse Wonder
Outputs Comparison
Judge for yourself.
Run your own prompts against Kimi K2-Thinking-0905 and Muse Wonder side-by-side, then vote on the output you prefer.
FAQ
Common questions about Kimi K2-Thinking-0905 vs Muse Wonder.