LLM Comparison

Muse Wonder vs Kimi K2.7: benchmark scores, pricing & comparison.

Side-by-side Muse Wonder 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 ranks higher (#11 vs #44) and wins 4 of 4 shared benchmarks; Kimi K2.7 costs less ($0.95/$4.00 vs $4.00/$20.0 per 1M tokens).

Rank #11 vs #44AskClash overall scores 64.7 vs 41.6.
Pricing $4.00/$20.0 vs $0.95/$4.00Input and output token prices per 1M tokens when published.
Proprietary vs Open WeightMeta vs Moonshot AI.

Muse Wonder vs Kimi K2.7 benchmark comparison

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

MetricMuse WonderKimi K2.7
Overall Score64.741.6
Leaderboard Rank#11#44
ACB62.343.0
RWT—7.5
HLE50.135.0
GPQA—89.6
IFEval—63.1
Terminal-Bench52.0—
DeepSWE78.830.5
GDPval-AA1770.31037.2
MCP Atlas—76.0
Finance Agent56.6—
Tau2—90.1
Input Price (per 1M tokens)$4.00$0.95
Output Price (per 1M tokens)$20.0$4.00
Context Window1M256K
Benchmarks Published99

Muse Wonder vs Kimi K2.7 head-to-head charts

Muse Wonder leads 5 and Kimi K2.7 leads 0 of 5 shared benchmarks. Kimi K2.7 is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

Muse WonderKimi K2.7
Overall
64.7Muse Wonder
41.6Kimi K2.7
ACB
62.3Muse Wonder
43.0Kimi K2.7
HLE
50.1Muse Wonder
35.0Kimi K2.7
DeepSWE
78.8Muse Wonder
30.5Kimi K2.7
GDPval-AA
1770.3Muse Wonder
1037.2Kimi K2.7
Muse Wonder
Input$4.00
Output$20.0
Workload$8.00
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 and Kimi K2.7 comparisons

Explore how Muse Wonder and Kimi K2.7 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.