GoogleGemini 2.5 Flash-LiteVSOpenAIGPT-5.4 nano

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

Our Take

We recommend GPT-5.4 nano for complex complex codebases, multi-file repositories, and architectural planning, or the 2.6x cheaper Gemini 2.5 Flash-Lite if your budget requires optimizing costs for very high-volume pipelines. While GPT-5.4 nano offers a clear reasoning advantage, it carries a moderate price premium. Choose GPT-5.4 nano for architectural codebase planning, or Gemini 2.5 Flash-Lite to save on API costs for simple scripts.

▶WHY?

Benchmark Calculations & Evidence:

Coding Evaluation: Gemini 2.5 Flash-Lite was evaluated on SWE-bench Verified (scoring 41.3%), while GPT-5.4 nano was evaluated on SWE-bench Pro (scoring 52.4%).

Reasoning Accuracy: Both models were evaluated on the GPQA Diamond benchmark. GPT-5.4 nano scored 82.8%, while Gemini 2.5 Flash-Lite scored 62.5%.

Cost Efficiency: Gemini 2.5 Flash-Lite pricing ($0.1/M input, $0.4/M output) is 2.6x cheaper than GPT-5.4 nano ($0.2/M input, $1.25/M output).

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Model Specs

Gemini 2.5 Flash-Lite

Website

Benchmarks & Scores

Coding (swe-bench-verified)

41.3%

multi-file code and clearly defined tasks

Reasoning (gpqa-diamond)

62.5%

graduate-level science QA

Cost & Context

Cost (per 1M tokens)2.6x cheaper

$0.17Input: $0.10 | Output: $0.40

Context WindowLarger

1.05M tokens

Model Specs

GPT-5.4 nano

Website

Benchmarks & Scores

Coding (swe-bench-pro)

52.4%

complex codebases, multi-file repositories, and architectural planning

Reasoning (gpqa-diamond)Winner (+20.3%)

82.8%

graduate-level science QA

Cost & Context

Cost (per 1M tokens)

$0.46Input: $0.20 | Output: $1.25

Context Window

400k tokens

Read our data collection methodology

Frequently Asked Questions about Gemini 2.5 Flash-Lite vs GPT-5.4 nano

Gemini 2.5 Flash-Lite is cheaper than GPT-5.4 nano. Gemini 2.5 Flash-Lite has a blended cost of $0.17/1M tokens, which is about 2.6x cheaper than GPT-5.4 nano at $0.46/1M tokens.

For coding tasks, Gemini 2.5 Flash-Lite scores 41.3% on swe-bench-verified (multi-file code and clearly defined tasks), while GPT-5.4 nano scores 52.4% on swe-bench-pro (complex codebases, multi-file repositories, and architectural planning).

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