Benchmark scores
Higher is better for all benchmark scores (SWE-bench, GPQA, HLE, Terminal-Bench, etc.). Green marks the model with the higher score.
LLM Comparison
Muse Wonder vs SWE-1.7: benchmark scores, pricing & comparison.
Side-by-side Muse Wonder vs SWE-1.7 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 #87); SWE-1.7 costs less ($0/$0 vs $4.00/$20.0 per 1M tokens).
Rank #11 vs #87AskClash overall scores 64.7 vs —.
Pricing $4.00/$20.0 vs $0/$0Input and output token prices per 1M tokens when published.
Proprietary vs ProprietaryMeta vs Cognition.
Muse Wonder vs SWE-1.7 benchmark comparison
Green cells highlight the winning model for each metric. Scores are cached from the AskClash LLM leaderboard snapshot.
| Metric | Muse Wonder | SWE-1.7 |
|---|---|---|
| Overall Score | 64.7 | — |
| Leaderboard Rank | #11 | #87 |
| ACB | 62.3 | — |
| RWT | — | 8.5 |
| HLE | 50.1 | — |
| Terminal-Bench | 52.0 | — |
| DeepSWE | 78.8 | — |
| GDPval-AA | 1770.3 | — |
| Finance Agent | 56.6 | — |
| Input Price (per 1M tokens) | $4.00 | $0 |
| Output Price (per 1M tokens) | $20.0 | $0 |
| Context Window | 1M | 256K |
| Benchmarks Published | 9 | — |
Muse Wonder vs SWE-1.7 head-to-head charts
Muse Wonder and SWE-1.7 do not fully overlap on published benchmark scores in this snapshot. SWE-1.7 is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.
Muse WonderSWE-1.7
No fully overlapping benchmark scores for these models in the current snapshot.
Muse Wonder
Input$4.00
Output$20.0
Workload$8.00
Context1M
SWE-1.7
Input$0
Output$0
Workload$0.00
Context256K
Workload = published cost of 1M input + 200K output tokens. Open the live leaderboard for interactive compare charts.
More Muse Wonder and SWE-1.7 comparisons
Explore how Muse Wonder and SWE-1.7 stack up against other top-ranked LLMs.
How to read this comparison
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.