GoogleGemini 3 FlashVSOpenAIGPT-5.6 Luna

Analysis by:the whichllmmodel Editorial Team|Updated: June 2026

Our Take

If your budget allows, GPT-5.6 Luna is the superior choice here, offering a clear benchmark advantage while carrying only a moderate price premium (2.0x). We recommend choosing Gemini 3 Flash only if you need to optimize costs for very high-volume pipelines.

Was this recommendation helpful?

Model Specs

Gemini 3 Flash

Website

Benchmarks & Scores

Coding (swe-bench-pro)

34.6%

excellent at multi-file repositories, autonomous agents, and industrial codebases

Reasoning (gpqa-diamond)

90.4%

graduate-level science QA

Cost & Context

Cost (per 1M tokens)2.0x cheaper

$1.13Input: $0.50 | Output: $3.00

Context Window

1.05M tokens

Model Specs

GPT-5.6 Luna

Website

Benchmarks & Scores

Coding (swe-bench-pro)Winner (+28.1%)

62.7%

excellent at multi-file repositories, autonomous agents, and industrial codebases

Reasoning (gpqa-diamond)Winner (+1.9%)

92.3%

graduate-level science QA

Cost & Context

Cost (per 1M tokens)

$2.25Input: $1.00 | Output: $6.00

Context WindowLarger

1.05M tokens

Read our data collection methodology

Frequently Asked Questions about Gemini 3 Flash vs GPT-5.6 Luna

Gemini 3 Flash is cheaper than GPT-5.6 Luna. Gemini 3 Flash has a blended cost of $1.13/1M tokens, which is about 2.0x cheaper than GPT-5.6 Luna at $2.25/1M tokens.

GPT-5.6 Luna is better for coding tasks on this benchmark. It scores 62.7% on swe-bench-pro (excellent at multi-file repositories, autonomous agents, and industrial codebases) compared to Gemini 3 Flash which scores 34.6%.

Related Matchups

Explore similar comparisons for Gemini 3 Flash and GPT-5.6 Luna.

Browse More Comparisons

Do you want to find a model for your constraints?

Use our interactive model finder to filter LLMs by reasoning capability, coding performance, cost, and context length.

Open Model Finder