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Gemini 2.5 Flash-Lite vs GPT-5.4 Nano

Compare Gemini 2.5 Flash-Lite and GPT-5.4 Nano side-by-side. See how these vision models stack up in Image Captioning, Object Detection, OCR, Open Prompt, and Classification.

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GoogleGemini 2.5 Flash-Lite
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OpenAIGPT-5.4 Nano
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Gemini 2.5 Flash-Lite vs GPT-5.4 Nano: Overview

Gemini 2.5 Flash-Lite

Gemini 2.5 Flash-Lite, released for general availability on July 22, 2025, is the most cost-efficient model in the Gemini 2.5 family, designed for high-volume and latency-sensitive tasks. It is multimodal, supporting text, images, video, audio, and PDFs as inputs, with text as its primary output. The model handles up to 1 million input tokens and generates outputs up to 64K tokens, making it suitable for large-scale document or media processing at low cost. It is built on a Sparse Mixture-of-Experts architecture with native multimodal support, though exact parameter counts are undisclosed.

Flash-Lite offers the lowest usage cost among Gemini 2.5 models. It introduces developer controls for “thinking mode,” allowing fine-tuning of reasoning depth vs. efficiency. It also integrates native tools such as code execution, search grounding, and URL context. While strong on translation, classification, coding, and general multimodal reasoning, it lacks support for image or audio generation in its stable release and is less capable than Gemini 2.5 Flash or Pro on complex reasoning-heavy workflows.

GPT-5.4 Nano

GPT-5.4 nano is a high-throughput model developed by OpenAI and released on March 17, 2026, as the efficiency-optimized entry in the GPT-5.4 family. Engineered for cost-sensitive production environments and latency-critical workloads, it features an expanded 400,000-token context window that enables the processing of large document batches or extensive logs in a single pass. The model is primarily optimized for text-heavy operations, serving as a premier engine for high-volume classification, data extraction, ranking, and the orchestration of lightweight sub-agents where speed and low per-token costs are the primary requirements.

While it supports text and image inputs, GPT-5.4 nano is designed as a text-first worker rather than a specialized visual reasoning tool. In multi-model architectures, it is best utilized for structured text tasks and simple coding sub-tasks, leaving intensive vision reasoning and UI navigation to its sibling, GPT-5.4 mini. Compared to the previous GPT-5 nano, this version provides a significant leap in reliability for structured outputs and tool calling, making it a dependable and economical choice for developers building scalable, automated pipelines that require rapid execution at the edge of the GPT-5.4 ecosystem.

Gemini 2.5 Flash-Lite vs GPT-5.4 Nano Comparison Table

PropertyGemini 2.5 Flash-LiteGPT-5.4 Nano
OrganizationGoogleOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateJul 2025Mar 2026
Context Window1.0M400K
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.100$0.200
Output $/1M$0.400$1.25
Vision Tasks
CaptioningDemoDemo
ClassificationDemoDemo
Object DetectionDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Visual Understanding
Overall Score
53.73%
62.69%
Avg Response Time7.19s3.72s
Median input tokensincl. image tokens2941.4K
Median output tokens6105
Est. cost / taskon this benchmark<$0.0001$0.0004
Defect Detection
66.7%(10/15)
80%(12/15)
Document Understanding
66.7%(6/9)
77.8%(7/9)
Object Counting
10%(1/10)
30%(3/10)
Object Understanding
71.4%(10/14)
64.3%(9/14)
Spatial Understanding
47.4%(9/19)
57.9%(11/19)
OCR
Overall Score
77.73%
62.45%
Avg Response Time7.45s2.59s
Median input tokensincl. image tokens290105
Median output tokens1287
Est. cost / taskon this benchmark<$0.0001$0.0001
Focused Scene OCR
75.8%(75/99)
55.6%(55/99)
Handwritten Math
70%(7/10)
20%(2/10)
License Plate Recognition
90%(27/30)
83.3%(25/30)
Text Recognition
80%(24/30)
70%(21/30)
VQA & Extraction
75%(45/60)
66.7%(40/60)

Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology