LLM Comparison

Kimi K3.1 Pro vs GPT-6 Luna: benchmark scores, pricing & comparison.

Side-by-side Kimi K3.1 Pro vs GPT-6 Luna 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 #100) and wins 5 of 5 shared benchmarks; GPT-6 Luna costs less ($0.10/$0.50 vs $2.00/$10.0 per 1M tokens).

Rank #4 vs #100AskClash overall scores 75.5 vs 49.6.
Pricing $2.00/$10.0 vs $0.10/$0.50Input and output token prices per 1M tokens when published.
Open Weight vs ProprietaryMoonshot AI vs OpenAI.

Kimi K3.1 Pro vs GPT-6 Luna benchmark comparison

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

MetricKimi K3.1 ProGPT-6 Luna
Overall Score75.549.6
Leaderboard Rank#4#100
ACB—50.2
HLE70.038.5
GPQA94.2—
Terminal-Bench65.7—
DeepSWE75.566.6
GDPval-AA1846.01431.0
Finance Agent58.649.9
CharXiv89.0—
MMMU-Pro84.179.7
ARC-AGI 2—59.3
Input Price (per 1M tokens)$2.00$0.10
Output Price (per 1M tokens)$10.0$0.50
Context Window1M1.05M
Benchmarks Published910

Kimi K3.1 Pro vs GPT-6 Luna head-to-head charts

Kimi K3.1 Pro leads 6 and GPT-6 Luna leads 0 of 6 shared benchmarks. GPT-6 Luna is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

Kimi K3.1 ProGPT-6 Luna
Overall
75.5Kimi K3.1 Pro
49.6GPT-6 Luna
HLE
70.0Kimi K3.1 Pro
38.5GPT-6 Luna
DeepSWE
75.5Kimi K3.1 Pro
66.6GPT-6 Luna
GDPval-AA
1846.0Kimi K3.1 Pro
1431.0GPT-6 Luna
Finance Agent
58.6Kimi K3.1 Pro
49.9GPT-6 Luna
MMMU-Pro
84.1Kimi K3.1 Pro
79.7GPT-6 Luna
Kimi K3.1 Pro
Input$2.00
Output$10.0
Workload$4.00
Context1M
GPT-6 Luna
Input$0.10
Output$0.50
Workload$0.20
Context1.05M

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

More Kimi K3.1 Pro and GPT-6 Luna comparisons

Explore how Kimi K3.1 Pro and GPT-6 Luna 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.