LLM Comparison

Grok 4.5 vs GPT-5.6 Luna: benchmark scores, pricing & comparison.

Side-by-side Grok 4.5 vs GPT-5.6 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.

Rank #5 vs #13AskClash overall scores 78.7 vs 68.2.
Pricing $2.00/$6.00 vs $1.00/$6.00Input and output token prices per 1M tokens when published.
Proprietary vs ProprietaryxAI vs OpenAI.

Grok 4.5 vs GPT-5.6 Luna benchmark comparison

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

MetricGrok 4.5GPT-5.6 Luna
Overall Score78.768.2
Leaderboard Rank#5#13
RWT9.09.5
Coding Agent Index76.475.0
HLE40.3
GPQA93.192.3
SWE-Pro64.762.7
SWE-Atlas83.981.0
Terminal-Bench83.384.7
DeepSWE67.2
Finance Agent55.0
MMMU-Pro80.478.4
ARC-AGI 259.5
MRCR41.3
Input Price (per 1M tokens)$2.00$1.00
Output Price (per 1M tokens)$6.00$6.00
Context Window500K1M
Benchmarks Published912

Grok 4.5 vs GPT-5.6 Luna head-to-head charts

Grok 4.5 leads 6 and GPT-5.6 Luna leads 2 of 8 shared benchmarks. GPT-5.6 Luna is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

Grok 4.5GPT-5.6 Luna
Overall
78.7Grok 4.5
68.2GPT-5.6 Luna
RWT
9.0Grok 4.5
9.5GPT-5.6 Luna
Coding Agent Index
76.4Grok 4.5
75.0GPT-5.6 Luna
GPQA
93.1Grok 4.5
92.3GPT-5.6 Luna
SWE-Pro
64.7Grok 4.5
62.7GPT-5.6 Luna
SWE-Atlas
83.9Grok 4.5
81.0GPT-5.6 Luna
Terminal-Bench
83.3Grok 4.5
84.7GPT-5.6 Luna
MMMU-Pro
80.4Grok 4.5
78.4GPT-5.6 Luna
Grok 4.5
Input$2.00
Output$6.00
Workload$3.20
Context500K
GPT-5.6 Luna
Input$1.00
Output$6.00
Workload$2.20
Context1M

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

More Grok 4.5 and GPT-5.6 Luna comparisons

Explore how Grok 4.5 and GPT-5.6 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.