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 Full vs Kimi K2.7: benchmark scores, pricing & comparison.
Side-by-side Muse Wonder Full vs Kimi K2.7 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 Full ranks higher (#6 vs #44) and wins 3 of 3 shared benchmarks.
Rank #6 vs #44AskClash overall scores 71.7 vs 41.6.
Pricing —/— vs $0.95/$4.00Input and output token prices per 1M tokens when published.
Proprietary vs Open WeightMeta vs Moonshot AI.
Muse Wonder Full vs Kimi K2.7 benchmark comparison
Green cells highlight the winning model for each metric. Scores are cached from the AskClash LLM leaderboard snapshot.
| Metric | Muse Wonder Full | Kimi K2.7 |
|---|---|---|
| Overall Score | 71.7 | 41.6 |
| Leaderboard Rank | #6 | #44 |
| ACB | — | 43.0 |
| RWT | — | 7.5 |
| HLE | 53.6 | 35.0 |
| GPQA | — | 89.6 |
| IFEval | — | 63.1 |
| Terminal-Bench | 57.1 | — |
| DeepSWE | 79.0 | 30.5 |
| GDPval-AA | 1857.1 | 1037.2 |
| MCP Atlas | — | 76.0 |
| Finance Agent | 59.1 | — |
| Tau2 | — | 90.1 |
| Input Price (per 1M tokens) | — | $0.95 |
| Output Price (per 1M tokens) | — | $4.00 |
| Context Window | 1M | 256K |
| Benchmarks Published | 8 | 9 |
Muse Wonder Full vs Kimi K2.7 head-to-head charts
Muse Wonder Full leads 4 and Kimi K2.7 leads 0 of 4 shared benchmarks. Charts show only benchmarks both models publish.
Muse Wonder FullKimi K2.7
Overall
HLE
DeepSWE
GDPval-AA
Muse Wonder Full
Input—
Output—
Workload—
Context1M
Kimi K2.7
Input$0.95
Output$4.00
Workload$1.75
Context256K
Workload = published cost of 1M input + 200K output tokens. Open the live leaderboard for interactive compare charts.
More Muse Wonder Full and Kimi K2.7 comparisons
Explore how Muse Wonder Full and Kimi K2.7 stack up against other top-ranked LLMs.
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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.