LLM Comparison

GPT-6 Luna vs MiniMax-M3: benchmark scores, pricing & comparison.

Side-by-side GPT-6 Luna vs MiniMax-M3 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: GPT-6 Luna ranks higher (#41 vs #47) and wins 4 of 4 shared benchmarks; GPT-6 Luna costs less ($0.10/$0.50 vs $0.30/$1.20 per 1M tokens).

Rank #41 vs #47AskClash overall scores 42.2 vs 33.9.
Pricing $0.10/$0.50 vs $0.30/$1.20Input and output token prices per 1M tokens when published.
Proprietary vs ProprietaryOpenAI vs MiniMax.

GPT-6 Luna vs MiniMax-M3 benchmark comparison

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

MetricGPT-6 LunaMiniMax-M3
Overall Score42.233.9
Leaderboard Rank#41#47
ACB50.229.8
RWT—8.5
HLE38.537.1
GPQA—92.9
IFEval—82.9
SWE-bench—80.5
SWE-Pro—59.0
DeepSWE66.6—
GDPval-AA1444.558.4
OSWorld—70.1
MCP Atlas—74.2
Finance Agent49.9—
MMMU-Pro79.778.6
ARC-AGI 259.3—
Tau2—88.9
Input Price (per 1M tokens)$0.10$0.30
Output Price (per 1M tokens)$0.50$1.20
Context Window1.05M1M
Benchmarks Published1010

GPT-6 Luna vs MiniMax-M3 head-to-head charts

GPT-6 Luna leads 5 and MiniMax-M3 leads 0 of 5 shared benchmarks. GPT-6 Luna is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

GPT-6 LunaMiniMax-M3
Overall
42.2GPT-6 Luna
33.9MiniMax-M3
ACB
50.2GPT-6 Luna
29.8MiniMax-M3
HLE
38.5GPT-6 Luna
37.1MiniMax-M3
GDPval-AA
1444.5GPT-6 Luna
58.4MiniMax-M3
MMMU-Pro
79.7GPT-6 Luna
78.6MiniMax-M3
GPT-6 Luna
Input$0.10
Output$0.50
Workload$0.20
Context1.05M
MiniMax-M3
Input$0.30
Output$1.20
Workload$0.54
Context1M

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

More GPT-6 Luna and MiniMax-M3 comparisons

Explore how GPT-6 Luna and MiniMax-M3 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.