LLM Comparison

Muse Wonder vs Grok 4.7: benchmark scores, pricing & comparison.

Side-by-side Muse Wonder vs Grok 4.7 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 #25) and wins 5 of 6 shared benchmarks; Grok 4.7 costs less ($2.00/$6.00 vs $4.00/$20.0 per 1M tokens).

Rank #11 vs #25AskClash overall scores 64.7 vs 57.5.
Pricing $4.00/$20.0 vs $2.00/$6.00Input and output token prices per 1M tokens when published.
Proprietary vs ProprietaryMeta vs xAI.

Muse Wonder vs Grok 4.7 benchmark comparison

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

MetricMuse WonderGrok 4.7
Overall Score64.757.5
Leaderboard Rank#11#25
ACB62.366.4
Coding Agent Index—56.0
HLE50.143.1
SWE-Atlas—63.0
Terminal-Bench52.025.8
DeepSWE78.873.0
GDPval-AA1770.31695.2
Finance Agent56.649.2
Input Price (per 1M tokens)$4.00$2.00
Output Price (per 1M tokens)$20.0$6.00
Context Window1M500K
Benchmarks Published911

Muse Wonder vs Grok 4.7 head-to-head charts

Muse Wonder leads 6 and Grok 4.7 leads 1 of 7 shared benchmarks. Grok 4.7 is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

Muse WonderGrok 4.7
Overall
64.7Muse Wonder
57.5Grok 4.7
ACB
62.3Muse Wonder
66.4Grok 4.7
HLE
50.1Muse Wonder
43.1Grok 4.7
Terminal-Bench
52.0Muse Wonder
25.8Grok 4.7
DeepSWE
78.8Muse Wonder
73.0Grok 4.7
GDPval-AA
1770.3Muse Wonder
1695.2Grok 4.7
Finance Agent
56.6Muse Wonder
49.2Grok 4.7
Muse Wonder
Input$4.00
Output$20.0
Workload$8.00
Context1M
Grok 4.7
Input$2.00
Output$6.00
Workload$3.20
Context500K

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

More Muse Wonder and Grok 4.7 comparisons

Explore how Muse Wonder and Grok 4.7 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.