LLM Comparison

Gemini 3.7 Flash vs GPT-5.6 Luna: benchmark scores, pricing & comparison.

Side-by-side Gemini 3.7 Flash 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 #12 vs #15AskClash overall scores 69.1 vs 64.9.
Pricing $1.50/$7.50 vs $1.00/$6.00Input and output token prices per 1M tokens when published.
Proprietary vs ProprietaryGoogle vs OpenAI.

Gemini 3.7 Flash 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.

MetricGemini 3.7 FlashGPT-5.6 Luna
Overall Score69.164.9
Leaderboard Rank#12#15
ACB38.956.1
RWT9.5
Coding Agent Index75.0
HLE53.6
GPQA94.592.3
SWE-Pro62.7
SWE-Atlas81.0
Terminal-Bench85.884.7
DeepSWE65.367.2
Finance Agent55.0
CharXiv84.5
MMMU-Pro85.578.4
ARC-AGI 259.5
MRCR97.041.3
Input Price (per 1M tokens)$1.50$1.00
Output Price (per 1M tokens)$7.50$6.00
Context Window1M1M
Benchmarks Published1013

Gemini 3.7 Flash vs GPT-5.6 Luna head-to-head charts

Gemini 3.7 Flash leads 5 and GPT-5.6 Luna leads 2 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.

Gemini 3.7 FlashGPT-5.6 Luna
Overall
69.1Gemini 3.7 Flash
64.9GPT-5.6 Luna
ACB
38.9Gemini 3.7 Flash
56.1GPT-5.6 Luna
GPQA
94.5Gemini 3.7 Flash
92.3GPT-5.6 Luna
Terminal-Bench
85.8Gemini 3.7 Flash
84.7GPT-5.6 Luna
DeepSWE
65.3Gemini 3.7 Flash
67.2GPT-5.6 Luna
MMMU-Pro
85.5Gemini 3.7 Flash
78.4GPT-5.6 Luna
MRCR
97.0Gemini 3.7 Flash
41.3GPT-5.6 Luna
Gemini 3.7 Flash
Input$1.50
Output$7.50
Workload$3.00
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 Gemini 3.7 Flash and GPT-5.6 Luna comparisons

Explore how Gemini 3.7 Flash 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.