LLM Comparison

Claude Sonnet 5 vs GPT-5.6 Luna: benchmark scores, pricing & comparison.

Side-by-side Claude Sonnet 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 #9 vs #13AskClash overall scores 76.2 vs 68.2.
Pricing $3.00/$15.0 vs $1.00/$6.00Input and output token prices per 1M tokens when published.
Proprietary vs ProprietaryAnthropic vs OpenAI.

Claude Sonnet 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.

MetricClaude Sonnet 5GPT-5.6 Luna
Overall Score76.268.2
Leaderboard Rank#9#13
RWT8.59.5
Coding Agent Index75.0
HLE57.4
GPQA91.192.3
SWE-bench85.2
SWE-Pro63.262.7
SWE-Atlas81.0
Terminal-Bench80.484.7
DeepSWE53.867.2
OSWorld81.2
Finance Agent55.0
CharXiv88.3
MMMU-Pro77.378.4
ARC-AGI 259.5
MRCR41.3
Input Price (per 1M tokens)$3.00$1.00
Output Price (per 1M tokens)$15.0$6.00
Context Window1M1M
Benchmarks Published1112

Claude Sonnet 5 vs GPT-5.6 Luna head-to-head charts

Claude Sonnet 5 leads 2 and GPT-5.6 Luna leads 5 of 7 shared benchmarks. GPT-5.6 Luna is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

Claude Sonnet 5GPT-5.6 Luna
Overall
76.2Claude Sonnet 5
68.2GPT-5.6 Luna
RWT
8.5Claude Sonnet 5
9.5GPT-5.6 Luna
GPQA
91.1Claude Sonnet 5
92.3GPT-5.6 Luna
SWE-Pro
63.2Claude Sonnet 5
62.7GPT-5.6 Luna
Terminal-Bench
80.4Claude Sonnet 5
84.7GPT-5.6 Luna
DeepSWE
53.8Claude Sonnet 5
67.2GPT-5.6 Luna
MMMU-Pro
77.3Claude Sonnet 5
78.4GPT-5.6 Luna
Claude Sonnet 5
Input$3.00
Output$15.0
Workload$6.00
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 Claude Sonnet 5 and GPT-5.6 Luna comparisons

Explore how Claude Sonnet 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.