LLM Comparison

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

Side-by-side Kimi K3.1 Pro vs Muse Wonder Full 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 #8); Muse Wonder Full wins 3 of 5 shared benchmarks.

Rank #4 vs #8AskClash overall scores 75.5 vs 70.7.
Pricing $2.00/$10.0 vs —/—Input and output token prices per 1M tokens when published.
Open Weight vs ProprietaryMoonshot AI vs Meta.

Kimi K3.1 Pro vs Muse Wonder Full 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 Full
Overall Score75.570.7
Leaderboard Rank#4#8
HLE70.053.6
GPQA94.2—
Terminal-Bench65.757.1
DeepSWE75.579.0
GDPval-AA1846.01857.1
Finance Agent58.659.1
CharXiv89.0—
MMMU-Pro84.1—
Input Price (per 1M tokens)$2.00—
Output Price (per 1M tokens)$10.0—
Context Window1M1M
Benchmarks Published98

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

Kimi K3.1 Pro leads 3 and Muse Wonder Full leads 3 of 6 shared benchmarks. Charts show only benchmarks both models publish.

Kimi K3.1 ProMuse Wonder Full
Overall
75.5Kimi K3.1 Pro
70.7Muse Wonder Full
HLE
70.0Kimi K3.1 Pro
53.6Muse Wonder Full
Terminal-Bench
65.7Kimi K3.1 Pro
57.1Muse Wonder Full
DeepSWE
75.5Kimi K3.1 Pro
79.0Muse Wonder Full
GDPval-AA
1846.0Kimi K3.1 Pro
1857.1Muse Wonder Full
Finance Agent
58.6Kimi K3.1 Pro
59.1Muse Wonder Full
Kimi K3.1 Pro
Input$2.00
Output$10.0
Workload$4.00
Context1M
Muse Wonder Full
Input—
Output—
Workload—
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 Full comparisons

Explore how Kimi K3.1 Pro and Muse Wonder Full 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.