GoogleGemini 3.6 FlashVSOpenAIGPT-5.6 Luna

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

Our Take

Both models perform almost identically, with an average score gap of just 2.4%. We recommend choosing GPT-5.6 Luna as it is the more cost-effective option (being 1.3x cheaper) without sacrificing any real-world capability.

Was this recommendation helpful?

Model Specs

Gemini 3.6 Flash

Website

Benchmarks & Scores

Coding (swe-bench-pro)

58.7%

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

Reasoning (gpqa-diamond)Winner (+0.7%)

93%

graduate-level science QA

Cost & Context

Cost (per 1M tokens)

$3.00Input: $1.50 | Output: $7.50

Context Window

1.05M tokens

Model Specs

GPT-5.6 Luna

Website

Benchmarks & Scores

Coding (swe-bench-pro)Winner (+4.0%)

62.7%

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

Reasoning (gpqa-diamond)

92.3%

graduate-level science QA

Cost & Context

Cost (per 1M tokens)1.3x cheaper

$2.25Input: $1.00 | Output: $6.00

Context WindowLarger

1.05M tokens

Read our data collection methodology

Frequently Asked Questions about Gemini 3.6 Flash vs GPT-5.6 Luna

GPT-5.6 Luna is cheaper than Gemini 3.6 Flash. GPT-5.6 Luna has a blended cost of $2.25/1M tokens, which is about 1.3x cheaper than Gemini 3.6 Flash at $3.00/1M 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 3.6 Flash which scores 58.7%.

Related Matchups

Explore similar comparisons for Gemini 3.6 Flash and GPT-5.6 Luna.

Browse More Comparisons

GoogleGemini 3.6 Flash

GoogleGemini 3.6 Flash

OpenAIGPT-5.6 Terra

Compare Specs

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