LLM Comparison

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

Side-by-side Muse Wonder Full 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 Full ranks higher (#6 vs #28) and wins 5 of 5 shared benchmarks.

Rank #6 vs #28AskClash overall scores 71.7 vs 55.8.
Pricing —/— vs $3.00/$15.0Input and output token prices per 1M tokens when published.
Proprietary vs Open WeightMeta vs Moonshot AI.

Muse Wonder Full vs Kimi K3 benchmark comparison

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

MetricMuse Wonder FullKimi K3
Overall Score71.755.8
Leaderboard Rank#6#28
ACB—57.7
RWT—8.0
Coding Agent Index—51.9
HLE53.643.5
GPQA—93.5
SWE-Atlas—66.1
Terminal-Bench57.112.6
DeepSWE79.068.5
GDPval-AA1857.11668.0
MCP Atlas—84.2
Finance Agent59.153.1
CharXiv—84.8
MMMU-Pro—81.6
ARC-AGI 2—60.4
Input Price (per 1M tokens)—$3.00
Output Price (per 1M tokens)—$15.0
Context Window1M1M
Benchmarks Published816

Muse Wonder Full vs Kimi K3 head-to-head charts

Muse Wonder Full leads 6 and Kimi K3 leads 0 of 6 shared benchmarks. Charts show only benchmarks both models publish.

Muse Wonder FullKimi K3
Overall
71.7Muse Wonder Full
55.8Kimi K3
HLE
53.6Muse Wonder Full
43.5Kimi K3
Terminal-Bench
57.1Muse Wonder Full
12.6Kimi K3
DeepSWE
79.0Muse Wonder Full
68.5Kimi K3
GDPval-AA
1857.1Muse Wonder Full
1668.0Kimi K3
Finance Agent
59.1Muse Wonder Full
53.1Kimi K3
Muse Wonder Full
Input—
Output—
Workload—
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 Full and Kimi K3 comparisons

Explore how Muse Wonder Full 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.