LLM Comparison

Kimi K3.1 Pro vs Muse Wonder: benchmark scores, pricing & comparison.

Side-by-side Kimi K3.1 Pro vs Muse Wonder 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: Kimi K3.1 Pro ranks higher (#4 vs #23) and wins 4 of 5 shared benchmarks; Kimi K3.1 Pro costs less ($2.00/$10.0 vs $4.00/$20.0 per 1M tokens).

Rank #4 vs #23AskClash overall scores 75.5 vs 64.1.
Pricing $2.00/$10.0 vs $4.00/$20.0Input and output token prices per 1M tokens when published.
Open Weight vs ProprietaryMoonshot AI vs Meta.

Kimi K3.1 Pro vs Muse Wonder benchmark comparison

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

MetricKimi K3.1 ProMuse Wonder
Overall Score75.564.1
Leaderboard Rank#4#23
ACB—62.3
HLE70.050.1
GPQA94.2—
Terminal-Bench65.752.0
DeepSWE75.578.8
GDPval-AA1846.01770.3
Finance Agent58.656.6
CharXiv89.0—
MMMU-Pro84.1—
Input Price (per 1M tokens)$2.00$4.00
Output Price (per 1M tokens)$10.0$20.0
Context Window1M1M
Benchmarks Published99

Kimi K3.1 Pro vs Muse Wonder head-to-head charts

Kimi K3.1 Pro leads 5 and Muse Wonder leads 1 of 6 shared benchmarks. Kimi K3.1 Pro is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

Kimi K3.1 ProMuse Wonder
Overall
75.5Kimi K3.1 Pro
64.1Muse Wonder
HLE
70.0Kimi K3.1 Pro
50.1Muse Wonder
Terminal-Bench
65.7Kimi K3.1 Pro
52.0Muse Wonder
DeepSWE
75.5Kimi K3.1 Pro
78.8Muse Wonder
GDPval-AA
1846.0Kimi K3.1 Pro
1770.3Muse Wonder
Finance Agent
58.6Kimi K3.1 Pro
56.6Muse Wonder
Kimi K3.1 Pro
Input$2.00
Output$10.0
Workload$4.00
Context1M
Muse Wonder
Input$4.00
Output$20.0
Workload$8.00
Context1M

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

More Kimi K3.1 Pro and Muse Wonder comparisons

Explore how Kimi K3.1 Pro and Muse Wonder 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.