LLM Comparison

GPT-5.6 Luna vs MiniMax-M3: benchmark scores, pricing & comparison.

Side-by-side GPT-5.6 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.

Rank #13 vs #17AskClash overall scores 67.9 vs 59.4.
Pricing $1.00/$6.00 vs $0.30/$1.20Input and output token prices per 1M tokens when published.
Proprietary vs Open WeightOpenAI vs MiniMax.

GPT-5.6 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-5.6 LunaMiniMax-M3
Overall Score67.959.4
Leaderboard Rank#13#17
RWT9.58.5
Coding Agent Index75.0
HLE37.1
GPQA92.392.9
IFEval82.9
SWE-bench80.5
SWE-Pro62.759.0
SWE-Atlas81.0
Terminal-Bench84.766.0
DeepSWE67.2
OSWorld70.1
MCP Atlas74.2
Finance Agent55.048.3
MMMU-Pro78.478.1
ARC-AGI 259.5
Tau288.9
MRCR41.3
Input Price (per 1M tokens)$1.00$0.30
Output Price (per 1M tokens)$6.00$1.20
Context Window1M1M
Benchmarks Published1213

GPT-5.6 Luna vs MiniMax-M3 head-to-head charts

GPT-5.6 Luna leads 6 and MiniMax-M3 leads 1 of 7 shared benchmarks. MiniMax-M3 is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

GPT-5.6 LunaMiniMax-M3
Overall
67.9GPT-5.6 Luna
59.4MiniMax-M3
RWT
9.5GPT-5.6 Luna
8.5MiniMax-M3
GPQA
92.3GPT-5.6 Luna
92.9MiniMax-M3
SWE-Pro
62.7GPT-5.6 Luna
59.0MiniMax-M3
Terminal-Bench
84.7GPT-5.6 Luna
66.0MiniMax-M3
Finance Agent
55.0GPT-5.6 Luna
48.3MiniMax-M3
MMMU-Pro
78.4GPT-5.6 Luna
78.1MiniMax-M3
GPT-5.6 Luna
Input$1.00
Output$6.00
Workload$2.20
Context1M
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-5.6 Luna and MiniMax-M3 comparisons

Explore how GPT-5.6 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.