LLM Comparison

GPT-5.6 Luna vs Gemini 3.6 Flash: benchmark scores, pricing & comparison.

Side-by-side GPT-5.6 Luna vs Gemini 3.6 Flash 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 68.9 vs 62.9.
Pricing $1.00/$6.00 vs $1.50/$7.50Input and output token prices per 1M tokens when published.
Proprietary vs ProprietaryOpenAI vs Google.

GPT-5.6 Luna vs Gemini 3.6 Flash benchmark comparison

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

MetricGPT-5.6 LunaGemini 3.6 Flash
Overall Score68.962.9
Leaderboard Rank#13#17
RWT9.5
Coding Agent Index75.0
HLE38.0
GPQA92.392.8
SWE-Pro62.758.7
SWE-Atlas81.0
Terminal-Bench84.778.0
DeepSWE67.249.0
OSWorld83.0
Finance Agent55.0
CharXiv85.2
MMMU-Pro78.483.2
ARC-AGI 259.5
MRCR41.391.8
Input Price (per 1M tokens)$1.00$1.50
Output Price (per 1M tokens)$6.00$7.50
Context Window1M1M
Benchmarks Published1211

GPT-5.6 Luna vs Gemini 3.6 Flash head-to-head charts

GPT-5.6 Luna leads 4 and Gemini 3.6 Flash leads 3 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.

GPT-5.6 LunaGemini 3.6 Flash
Overall
68.9GPT-5.6 Luna
62.9Gemini 3.6 Flash
GPQA
92.3GPT-5.6 Luna
92.8Gemini 3.6 Flash
SWE-Pro
62.7GPT-5.6 Luna
58.7Gemini 3.6 Flash
Terminal-Bench
84.7GPT-5.6 Luna
78.0Gemini 3.6 Flash
DeepSWE
67.2GPT-5.6 Luna
49.0Gemini 3.6 Flash
MMMU-Pro
78.4GPT-5.6 Luna
83.2Gemini 3.6 Flash
MRCR
41.3GPT-5.6 Luna
91.8Gemini 3.6 Flash
GPT-5.6 Luna
Input$1.00
Output$6.00
Workload$2.20
Context1M
Gemini 3.6 Flash
Input$1.50
Output$7.50
Workload$3.00
Context1M

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

More GPT-5.6 Luna and Gemini 3.6 Flash comparisons

Explore how GPT-5.6 Luna and Gemini 3.6 Flash 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.