LLM Comparison

GPT-6 Astra vs Muse Wonder: benchmark scores, pricing & comparison.

Side-by-side GPT-6 Astra vs Muse Wonder 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: GPT-6 Astra ranks higher (#8 vs #11); Muse Wonder costs less ($4.00/$20.0 vs $10.0/$50.0 per 1M tokens).

Rank #8 vs #11AskClash overall scores 70.9 vs 64.7.
Pricing $10.0/$50.0 vs $4.00/$20.0Input and output token prices per 1M tokens when published.
Proprietary vs ProprietaryOpenAI vs Meta.

GPT-6 Astra vs Muse Wonder benchmark comparison

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

MetricGPT-6 AstraMuse Wonder
Overall Score70.964.7
Leaderboard Rank#8#11
ACB64.562.3
RWT9.5—
Coding Agent Index67.0—
HLE57.250.1
GPQA96.0—
SWE-Atlas61.8—
Terminal-Bench59.152.0
DeepSWE73.278.8
GDPval-AA1629.31770.3
Finance Agent53.556.6
MMMU-Pro87.0—
ARC-AGI 295.0—
MRCR100.0—
Input Price (per 1M tokens)$10.0$4.00
Output Price (per 1M tokens)$50.0$20.0
Context Window1.05M1M
Benchmarks Published149

GPT-6 Astra vs Muse Wonder head-to-head charts

GPT-6 Astra leads 4 and Muse Wonder leads 3 of 7 shared benchmarks. Muse Wonder is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

GPT-6 AstraMuse Wonder
Overall
70.9GPT-6 Astra
64.7Muse Wonder
ACB
64.5GPT-6 Astra
62.3Muse Wonder
HLE
57.2GPT-6 Astra
50.1Muse Wonder
Terminal-Bench
59.1GPT-6 Astra
52.0Muse Wonder
DeepSWE
73.2GPT-6 Astra
78.8Muse Wonder
GDPval-AA
1629.3GPT-6 Astra
1770.3Muse Wonder
Finance Agent
53.5GPT-6 Astra
56.6Muse Wonder
GPT-6 Astra
Input$10.0
Output$50.0
Workload$20
Context1.05M
Muse Wonder
Input$4.00
Output$20.0
Workload$8.00
Context1M

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

More GPT-6 Astra and Muse Wonder comparisons

Explore how GPT-6 Astra and Muse Wonder 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.