LLM Comparison

GPT-5.5 vs GPT-6 Luna: benchmark scores, pricing & comparison.

Side-by-side GPT-5.5 vs GPT-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.

Verdict: GPT-5.5 ranks higher (#31 vs #41) and wins 5 of 5 shared benchmarks; GPT-6 Luna costs less ($0.10/$0.50 vs $5.00/$30.0 per 1M tokens).

Rank #31 vs #41AskClash overall scores 51.5 vs 42.2.
Pricing $5.00/$30.0 vs $0.10/$0.50Input and output token prices per 1M tokens when published.
Proprietary vs ProprietaryOpenAI vs OpenAI.

GPT-5.5 vs GPT-6 Luna benchmark comparison

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

MetricGPT-5.5GPT-6 Luna
Overall Score51.542.2
Leaderboard Rank#31#41
ACB62.050.2
RWT8.0—
HLE52.238.5
GPQA93.6—
SWE-Pro58.6—
Terminal-Bench14.7—
DeepSWE67.066.6
GDPval-AA—1444.5
OSWorld78.7—
MCP Atlas75.3—
Finance Agent—49.9
MMMU-Pro81.279.7
ARC-AGI 285.059.3
Tau298.0—
Input Price (per 1M tokens)$5.00$0.10
Output Price (per 1M tokens)$30.0$0.50
Context Window1M1.05M
Benchmarks Published1210

GPT-5.5 vs GPT-6 Luna head-to-head charts

GPT-5.5 leads 6 and GPT-6 Luna leads 0 of 6 shared benchmarks. GPT-6 Luna is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

GPT-5.5GPT-6 Luna
Overall
51.5GPT-5.5
42.2GPT-6 Luna
ACB
62.0GPT-5.5
50.2GPT-6 Luna
HLE
52.2GPT-5.5
38.5GPT-6 Luna
DeepSWE
67.0GPT-5.5
66.6GPT-6 Luna
MMMU-Pro
81.2GPT-5.5
79.7GPT-6 Luna
ARC-AGI 2
85.0GPT-5.5
59.3GPT-6 Luna
GPT-5.5
Input$5.00
Output$30.0
Workload$11
Context1M
GPT-6 Luna
Input$0.10
Output$0.50
Workload$0.20
Context1.05M

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

More GPT-5.5 and GPT-6 Luna comparisons

Explore how GPT-5.5 and GPT-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.