LLM Comparison

Muse Wonder vs Claude Haiku 5.5: benchmark scores, pricing & comparison.

Side-by-side Muse Wonder vs Claude Haiku 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 #19); Claude Haiku 5.5 costs less ($0.10/$0.50 vs $4.00/$20.0 per 1M tokens).

Rank #11 vs #19AskClash overall scores 64.7 vs 59.5.
Pricing $4.00/$20.0 vs $0.10/$0.50Input and output token prices per 1M tokens when published.
Proprietary vs ProprietaryMeta vs Anthropic.

Muse Wonder vs Claude Haiku 5.5 benchmark comparison

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

MetricMuse WonderClaude Haiku 5.5
Overall Score64.759.5
Leaderboard Rank#11#19
ACB62.363.5
HLE50.157.4
SWE-Pro—64.8
Terminal-Bench52.032.8
DeepSWE78.8—
GDPval-AA1770.31620.0
Finance Agent56.6—
Input Price (per 1M tokens)$4.00$0.10
Output Price (per 1M tokens)$20.0$0.50
Context Window1M1M
Benchmarks Published98

Muse Wonder vs Claude Haiku 5.5 head-to-head charts

Muse Wonder leads 3 and Claude Haiku 5.5 leads 2 of 5 shared benchmarks. Claude Haiku 5.5 is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

Muse WonderClaude Haiku 5.5
Overall
64.7Muse Wonder
59.5Claude Haiku 5.5
ACB
62.3Muse Wonder
63.5Claude Haiku 5.5
HLE
50.1Muse Wonder
57.4Claude Haiku 5.5
Terminal-Bench
52.0Muse Wonder
32.8Claude Haiku 5.5
GDPval-AA
1770.3Muse Wonder
1620.0Claude Haiku 5.5
Muse Wonder
Input$4.00
Output$20.0
Workload$8.00
Context1M
Claude Haiku 5.5
Input$0.10
Output$0.50
Workload$0.20
Context1M

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

More Muse Wonder and Claude Haiku 5.5 comparisons

Explore how Muse Wonder and Claude Haiku 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.