LLM Comparison

Muse Wonder Full vs GPT-5.6 Luna: benchmark scores, pricing & comparison.

Side-by-side Muse Wonder Full vs GPT-5.6 Luna comparison across SWE-bench, GPQA, HLE, Terminal-Bench, coding agent scores, token pricing, context window, and AskClash RWT. Green marks the winner on each benchmark.

Verdict: Muse Wonder Full ranks higher (#6 vs #37) and wins 5 of 5 shared benchmarks.

Rank #6 vs #37AskClash overall scores 71.7 vs 51.7.
Pricing —/— vs $1.00/$6.00Input and output token prices per 1M tokens when published.
Proprietary vs ProprietaryMeta vs OpenAI.

Muse Wonder Full vs GPT-5.6 Luna benchmark comparison

Green cells highlight the winning model for each metric. Scores are cached from the AskClash LLM leaderboard snapshot.

MetricMuse Wonder FullGPT-5.6 Luna
Overall Score71.751.7
Leaderboard Rank#6#37
ACB—56.1
RWT—9.5
Coding Agent Index—75.0
HLE53.639.5
GPQA—92.3
SWE-Pro—62.7
SWE-Atlas—81.0
Terminal-Bench57.111.6
DeepSWE79.067.2
GDPval-AA1857.11592.0
Finance Agent59.155.0
MMMU-Pro—78.4
ARC-AGI 2—59.5
MRCR—41.3
Input Price (per 1M tokens)—$1.00
Output Price (per 1M tokens)—$6.00
Context Window1M1M
Benchmarks Published815

Muse Wonder Full vs GPT-5.6 Luna head-to-head charts

Muse Wonder Full leads 6 and GPT-5.6 Luna leads 0 of 6 shared benchmarks. Charts show only benchmarks both models publish.

Muse Wonder FullGPT-5.6 Luna
Overall
71.7Muse Wonder Full
51.7GPT-5.6 Luna
HLE
53.6Muse Wonder Full
39.5GPT-5.6 Luna
Terminal-Bench
57.1Muse Wonder Full
11.6GPT-5.6 Luna
DeepSWE
79.0Muse Wonder Full
67.2GPT-5.6 Luna
GDPval-AA
1857.1Muse Wonder Full
1592.0GPT-5.6 Luna
Finance Agent
59.1Muse Wonder Full
55.0GPT-5.6 Luna
Muse Wonder Full
Input—
Output—
Workload—
Context1M
GPT-5.6 Luna
Input$1.00
Output$6.00
Workload$2.20
Context1M

Workload = published cost of 1M input + 200K output tokens. Open the live leaderboard for interactive compare charts.

More Muse Wonder Full and GPT-5.6 Luna comparisons

Explore how Muse Wonder Full and GPT-5.6 Luna stack up against other top-ranked LLMs.

How to read this comparison

Benchmark scores

Higher is better for all benchmark scores (SWE-bench, GPQA, HLE, Terminal-Bench, etc.). Green marks the model with the higher score.

Token pricing

Lower is better for input and output prices. Green marks the cheaper model per 1M tokens.

Coverage matters

Models with fewer disclosed benchmark cells may have inflated percentile scores. Check the benchmark cell count for context.

This comparison page is generated from the AskClash LLM leaderboard cache. Open the live leaderboard for real-time scores and interactive filtering.