LLM Comparison

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

Side-by-side Kimi K3 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 ranks higher (#28 vs #41) and wins 6 of 6 shared benchmarks; GPT-6 Luna costs less ($0.10/$0.50 vs $3.00/$15.0 per 1M tokens).

Rank #28 vs #41AskClash overall scores 52.2 vs 42.2.
Pricing $3.00/$15.0 vs $0.10/$0.50Input and output token prices per 1M tokens when published.
Open Weight vs ProprietaryMoonshot AI vs OpenAI.

Kimi K3 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 K3GPT-6 Luna
Overall Score52.242.2
Leaderboard Rank#28#41
ACB57.750.2
RWT8.0—
HLE43.538.5
GPQA93.5—
Terminal-Bench12.6—
DeepSWE68.566.6
GDPval-AA1668.01444.5
MCP Atlas84.2—
Finance Agent53.149.9
CharXiv84.8—
MMMU-Pro81.679.7
ARC-AGI 2—59.3
Input Price (per 1M tokens)$3.00$0.10
Output Price (per 1M tokens)$15.0$0.50
Context Window1M1.05M
Benchmarks Published1110

Kimi K3 vs GPT-6 Luna head-to-head charts

Kimi K3 leads 7 and GPT-6 Luna leads 0 of 7 shared benchmarks. GPT-6 Luna is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

Kimi K3GPT-6 Luna
Overall
52.2Kimi K3
42.2GPT-6 Luna
ACB
57.7Kimi K3
50.2GPT-6 Luna
HLE
43.5Kimi K3
38.5GPT-6 Luna
DeepSWE
68.5Kimi K3
66.6GPT-6 Luna
GDPval-AA
1668.0Kimi K3
1444.5GPT-6 Luna
Finance Agent
53.1Kimi K3
49.9GPT-6 Luna
MMMU-Pro
81.6Kimi K3
79.7GPT-6 Luna
Kimi K3
Input$3.00
Output$15.0
Workload$6.00
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 and GPT-6 Luna comparisons

Explore how Kimi K3 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.