LLM Comparison

Muse Wonder vs Kimi K3: benchmark scores, pricing & comparison.

Side-by-side Muse Wonder vs Kimi K3 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 #28) and wins 6 of 6 shared benchmarks; Kimi K3 costs less ($3.00/$15.0 vs $4.00/$20.0 per 1M tokens).

Rank #11 vs #28AskClash overall scores 64.7 vs 55.8.
Pricing $4.00/$20.0 vs $3.00/$15.0Input and output token prices per 1M tokens when published.
Proprietary vs Open WeightMeta vs Moonshot AI.

Muse Wonder vs Kimi K3 benchmark comparison

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

MetricMuse WonderKimi K3
Overall Score64.755.8
Leaderboard Rank#11#28
ACB62.357.7
RWT—8.0
Coding Agent Index—51.9
HLE50.143.5
GPQA—93.5
SWE-Atlas—66.1
Terminal-Bench52.012.6
DeepSWE78.868.5
GDPval-AA1770.31668.0
MCP Atlas—84.2
Finance Agent56.653.1
CharXiv—84.8
MMMU-Pro—81.6
ARC-AGI 2—60.4
Input Price (per 1M tokens)$4.00$3.00
Output Price (per 1M tokens)$20.0$15.0
Context Window1M1M
Benchmarks Published916

Muse Wonder vs Kimi K3 head-to-head charts

Muse Wonder leads 7 and Kimi K3 leads 0 of 7 shared benchmarks. Kimi K3 is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

Muse WonderKimi K3
Overall
64.7Muse Wonder
55.8Kimi K3
ACB
62.3Muse Wonder
57.7Kimi K3
HLE
50.1Muse Wonder
43.5Kimi K3
Terminal-Bench
52.0Muse Wonder
12.6Kimi K3
DeepSWE
78.8Muse Wonder
68.5Kimi K3
GDPval-AA
1770.3Muse Wonder
1668.0Kimi K3
Finance Agent
56.6Muse Wonder
53.1Kimi K3
Muse Wonder
Input$4.00
Output$20.0
Workload$8.00
Context1M
Kimi K3
Input$3.00
Output$15.0
Workload$6.00
Context1M

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

More Muse Wonder and Kimi K3 comparisons

Explore how Muse Wonder and Kimi K3 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.