How do GPT-5.6 Sol and GPT-5.6 Luna compare on coding benchmarks?

The comparison table shows SWE-bench, SWE-Pro, SWE-Atlas, Terminal-Bench, DeepSWE, and coding agent index scores for both GPT-5.6 Sol and GPT-5.6 Luna when publicly disclosed.

LLM Comparison

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

Q: Which is better: GPT-5.6 Sol or GPT-5.6 Luna?

AskClash compares GPT-5.6 Sol and GPT-5.6 Luna side by side across SWE-bench, GPQA, HLE, Terminal-Bench, coding agent scores, token pricing, and context window so you can see which model wins on each benchmark.

Side-by-side GPT-5.6 Sol 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.

Open live leaderboard GPT-5.6 Sol model page GPT-5.6 Luna model page

Rank #2 vs #13AskClash overall scores 85.6 vs 67.9.

Pricing $5.00/$30.0 vs $1.00/$6.00Input and output token prices per 1M tokens when published.

Proprietary vs ProprietaryOpenAI vs OpenAI.

GPT-5.6 Sol 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.

Metric	GPT-5.6 Sol	GPT-5.6 Luna
Overall Score	85.6	67.9
Leaderboard Rank	#2	#13
RWT	9.5	9.5
Coding Agent Index	80.0	75.0
HLE	47.2	—
GPQA	94.1	92.3
SWE-Pro	64.6	62.7
SWE-Atlas	84.0	81.0
Terminal-Bench	88.8	84.7
DeepSWE	72.7	67.2
Finance Agent	53.8	55.0
MMMU-Pro	83.0	78.4
ARC-AGI 2	92.5	59.5
Tau2	85.1	—
MRCR	91.5	41.3
Input Price (per 1M tokens)	$5.00	$1.00
Output Price (per 1M tokens)	$30.0	$6.00
Context Window	1M	1M
Benchmarks Published	14	12

GPT-5.6 Sol vs GPT-5.6 Luna head-to-head charts

GPT-5.6 Sol leads 10 and GPT-5.6 Luna leads 1 of 12 shared benchmarks. GPT-5.6 Luna is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

GPT-5.6 SolGPT-5.6 Luna

Overall

85.6GPT-5.6 Sol

67.9GPT-5.6 Luna

RWT

9.5GPT-5.6 Sol

9.5GPT-5.6 Luna

Coding Agent Index

80.0GPT-5.6 Sol

75.0GPT-5.6 Luna

GPQA

94.1GPT-5.6 Sol

92.3GPT-5.6 Luna

SWE-Pro

64.6GPT-5.6 Sol

62.7GPT-5.6 Luna

SWE-Atlas

84.0GPT-5.6 Sol

81.0GPT-5.6 Luna

Terminal-Bench

88.8GPT-5.6 Sol

84.7GPT-5.6 Luna

DeepSWE

72.7GPT-5.6 Sol

67.2GPT-5.6 Luna

Finance Agent

53.8GPT-5.6 Sol

55.0GPT-5.6 Luna

MMMU-Pro

83.0GPT-5.6 Sol

78.4GPT-5.6 Luna

ARC-AGI 2

92.5GPT-5.6 Sol

59.5GPT-5.6 Luna

MRCR

91.5GPT-5.6 Sol

41.3GPT-5.6 Luna

GPT-5.6 Sol

Input$5.00

Output$30.0

Workload$11

Context1M

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 GPT-5.6 Sol and GPT-5.6 Luna comparisons

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Claude Fable 5 vs GPT-5.6 Sol Claude Fable 5 vs GPT-5.6 Luna GPT-5.6 Sol vs Kimi K3 Kimi K3 vs GPT-5.6 Luna GPT-5.6 Sol vs Claude Opus 4.8 Claude Opus 4.8 vs GPT-5.6 Luna GPT-5.6 Sol vs Grok 4.5 Grok 4.5 vs GPT-5.6 Luna GPT-5.6 Sol vs Muse Spark 1.1 Muse Spark 1.1 vs GPT-5.6 Luna

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.