GPT-5.6 Luna vs Muse Wonder
Muse Wonder leads the LLM Stats Score 47.9 to 44.2. GPT-5.6 Luna is 17.8x cheaper per token.
OpenAI · Meta · Updated for 2026
Which is better?
Muse Wonder leads the overall LLM Stats Score 47.9 to 44.2, ranking #29 overall.
In the 4 individual benchmarks reported for both models, Muse Wonder wins 4; this is a narrower head-to-head signal than the composite indexes.
On price, GPT-5.6 Luna is roughly 17.8x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GPT-5.6 Luna also accepts a larger context window (1,050,000 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 GPT-5.6 Luna
- cost matters — it's about 17.8x cheaper per token
- you process long inputs — it offers a 1,050,000 token context window
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 value its reported benchmark strengths — it wins 4 of 4 exact shared results
- you want the most recent training data — it shipped Oct 2026
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
45 reported for GPT-5.6 Luna · 16 for Muse Wonder
GPT-5.6 Luna outperforms in 0 benchmarks, while Muse Wonder is better at 4 benchmarks (Artificial Analysis, DeepSWE 1.1, GDP.pdf, HealthBench Professional).
Muse Wonder significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, GPT-5.6 Luna ($0.20/1M tokens) is 20.0x cheaper than Muse Wonder ($4.00/1M tokens).
For output processing, GPT-5.6 Luna ($1.20/1M tokens) is 16.7x cheaper than Muse Wonder ($20.00/1M tokens).
In conclusion, Muse Wonder is more expensive than GPT-5.6 Luna.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
GPT-5.6 Luna accepts 1,050,000 input tokens compared to Muse Wonder's 1,048,576 tokens. Only GPT-5.6 Luna specifies output context (128,000 tokens).
Input capabilities
Documented input modalities across available providers
Both GPT-5.6 Luna and Muse Wonder support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GPT-5.6 Luna
Muse Wonder
License
Usage and distribution terms
Both models are licensed under proprietary licenses.
Both models have usage restrictions defined by their respective organizations.
Proprietary
Closed source
Proprietary
Closed source
Release Timeline
When each model was launched
GPT-5.6 Luna was released on 2026-07-09, while Muse Wonder was released on 2026-10-12.
Muse Wonder is 3 months newer than GPT-5.6 Luna.
Jul 9, 2026
3 months ago
Oct 12, 2026
5 days ago
3mo newerKnowledge Cutoff
When training data ends
GPT-5.6 Luna has a documented knowledge cutoff of 2026-02-16, while Muse Wonder's cutoff date is not specified.
We can confirm GPT-5.6 Luna's training data extends to 2026-02-16, but cannot make a direct comparison without Muse Wonder's cutoff date.
Feb 2026
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Provider Availability
GPT-5.6 Luna is available from OpenAI. Muse Wonder is available from Meta Model API.
GPT-5.6 Luna
Muse Wonder
Outputs Comparison
Judge for yourself.
Run your own prompts against GPT-5.6 Luna and Muse Wonder side-by-side, then vote on the output you prefer.
FAQ
Common questions about GPT-5.6 Luna vs Muse Wonder.