whichLlmmodel
Back to Dashboard

OpenAIGPT-5.4VSOpenAIGPT-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 2.8%. We recommend choosing GPT-5.6 Luna as it is the more cost-effective option (being 2.5x cheaper) without sacrificing any real-world capability.
Was this recommendation helpful?
Model Specs

GPT-5.4

Benchmarks & Scores

Coding (swe-bench-pro)
57.7%

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

Reasoning (gpqa-diamond)Winner (+0.5%)
92.8%

graduate-level science QA

Cost & Context

Cost (per 1M tokens)
$5.63Input: $2.50 | Output: $15.00
Context Window
1.05M tokens
Model Specs

GPT-5.6 Luna

Benchmarks & Scores

Coding (swe-bench-pro)Winner (+5.0%)
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)2.5x cheaper
$2.25Input: $1.00 | Output: $6.00
Context Window
1.05M tokens

Frequently Asked Questions about GPT-5.4 vs GPT-5.6 Luna

GPT-5.6 Luna is cheaper than GPT-5.4. GPT-5.6 Luna has a blended cost of $2.25/1M tokens, which is about 2.5x cheaper than GPT-5.4 at $5.63/1M 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 GPT-5.4 which scores 57.7%.

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