LLM Comparison

Gemini 3.8 Flash vs GPT-6 Luna: benchmark scores, pricing & comparison.

Side-by-side Gemini 3.8 Flash 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.

Rank #11 vs #43AskClash overall scores 65.6 vs 51.0.
Pricing $1.50/$7.50 vs $0.10/$0.50Input and output token prices per 1M tokens when published.
Proprietary vs ProprietaryGoogle vs OpenAI.

Gemini 3.8 Flash vs GPT-6 Luna benchmark comparison

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

MetricGemini 3.8 FlashGPT-6 Luna
Overall Score65.651.0
Leaderboard Rank#11#43
ACB61.150.2
Coding Agent Index41.9
HLE54.938.5
GPQA95.3
SWE-Atlas45.2
Terminal-Bench89.4
DeepSWE73.866.6
GDPval-AA1545.01367.1
Finance Agent61.4
CharXiv86.2
MMMU-Pro85.675.5
Input Price (per 1M tokens)$1.50$0.10
Output Price (per 1M tokens)$7.50$0.50
Context Window1M1.05M
Benchmarks Published126

Gemini 3.8 Flash vs GPT-6 Luna head-to-head charts

Gemini 3.8 Flash 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.

Gemini 3.8 FlashGPT-6 Luna
Overall
65.6Gemini 3.8 Flash
51.0GPT-6 Luna
ACB
61.1Gemini 3.8 Flash
50.2GPT-6 Luna
HLE
54.9Gemini 3.8 Flash
38.5GPT-6 Luna
DeepSWE
73.8Gemini 3.8 Flash
66.6GPT-6 Luna
GDPval-AA
1545.0Gemini 3.8 Flash
1367.1GPT-6 Luna
MMMU-Pro
85.6Gemini 3.8 Flash
75.5GPT-6 Luna
Gemini 3.8 Flash
Input$1.50
Output$7.50
Workload$3.00
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 Gemini 3.8 Flash and GPT-6 Luna comparisons

Explore how Gemini 3.8 Flash 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.