LLM Comparison

Muse Wonder Full vs GPT-5.5: benchmark scores, pricing & comparison.

Side-by-side Muse Wonder Full vs GPT-5.5 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 #30) and wins 5 of 5 shared benchmarks.

Rank #6 vs #30AskClash overall scores 71.7 vs 54.2.
Pricing —/— vs $5.00/$30.0Input and output token prices per 1M tokens when published.
Proprietary vs ProprietaryMeta vs OpenAI.

Muse Wonder Full vs GPT-5.5 benchmark comparison

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

MetricMuse Wonder FullGPT-5.5
Overall Score71.754.2
Leaderboard Rank#6#30
ACB—62.0
RWT—8.0
HLE53.652.2
GPQA—93.6
IFEval—75.9
SWE-Pro—58.6
Terminal-Bench57.114.7
DeepSWE79.067.0
GDPval-AA1857.11350.1
OSWorld—78.7
MCP Atlas—75.3
Finance Agent59.151.8
MMMU-Pro—81.2
ARC-AGI 2—85.0
Tau2—98.0
Input Price (per 1M tokens)—$5.00
Output Price (per 1M tokens)—$30.0
Context Window1M1M
Benchmarks Published814

Muse Wonder Full vs GPT-5.5 head-to-head charts

Muse Wonder Full leads 6 and GPT-5.5 leads 0 of 6 shared benchmarks. Charts show only benchmarks both models publish.

Muse Wonder FullGPT-5.5
Overall
71.7Muse Wonder Full
54.2GPT-5.5
HLE
53.6Muse Wonder Full
52.2GPT-5.5
Terminal-Bench
57.1Muse Wonder Full
14.7GPT-5.5
DeepSWE
79.0Muse Wonder Full
67.0GPT-5.5
GDPval-AA
1857.1Muse Wonder Full
1350.1GPT-5.5
Finance Agent
59.1Muse Wonder Full
51.8GPT-5.5
Muse Wonder Full
Input—
Output—
Workload—
Context1M
GPT-5.5
Input$5.00
Output$30.0
Workload$11
Context1M

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

More Muse Wonder Full and GPT-5.5 comparisons

Explore how Muse Wonder Full and GPT-5.5 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.