LLM Comparison

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

Side-by-side Muse Wonder 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 ranks higher (#11 vs #30) and wins 5 of 6 shared benchmarks; Muse Wonder costs less ($4.00/$20.0 vs $5.00/$30.0 per 1M tokens).

Rank #11 vs #30AskClash overall scores 64.7 vs 54.2.
Pricing $4.00/$20.0 vs $5.00/$30.0Input and output token prices per 1M tokens when published.
Proprietary vs ProprietaryMeta vs OpenAI.

Muse Wonder 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 WonderGPT-5.5
Overall Score64.754.2
Leaderboard Rank#11#30
ACB62.362.0
RWT—8.0
HLE50.152.2
GPQA—93.6
IFEval—75.9
SWE-Pro—58.6
Terminal-Bench52.014.7
DeepSWE78.867.0
GDPval-AA1770.31350.1
OSWorld—78.7
MCP Atlas—75.3
Finance Agent56.651.8
MMMU-Pro—81.2
ARC-AGI 2—85.0
Tau2—98.0
Input Price (per 1M tokens)$4.00$5.00
Output Price (per 1M tokens)$20.0$30.0
Context Window1M1M
Benchmarks Published914

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

Muse Wonder leads 6 and GPT-5.5 leads 1 of 7 shared benchmarks. Muse Wonder is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

Muse WonderGPT-5.5
Overall
64.7Muse Wonder
54.2GPT-5.5
ACB
62.3Muse Wonder
62.0GPT-5.5
HLE
50.1Muse Wonder
52.2GPT-5.5
Terminal-Bench
52.0Muse Wonder
14.7GPT-5.5
DeepSWE
78.8Muse Wonder
67.0GPT-5.5
GDPval-AA
1770.3Muse Wonder
1350.1GPT-5.5
Finance Agent
56.6Muse Wonder
51.8GPT-5.5
Muse Wonder
Input$4.00
Output$20.0
Workload$8.00
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 and GPT-5.5 comparisons

Explore how Muse Wonder 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.