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Gemini 3 Flash vs GPT-5.4 Nano

Compare Gemini 3 Flash and GPT-5.4 Nano side-by-side. See how these vision models stack up in Object Detection, Classification, Open Prompt, OCR, and Image Captioning.

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GoogleGemini 3 Flash
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OpenAIGPT-5.4 Nano
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Models in this comparison

Gemini 3 Flash vs GPT-5.4 Nano: Overview

Gemini 3 Flash

Gemini 3 Flash is a proprietary multimodal large language model developed by Google through Google DeepMind, designed to deliver fast, cost-efficient reasoning across real-time products and developer workflows. Released in December 2025, it is the Flash-tier variant of the Gemini 3 family, balancing low latency with reasoning quality approaching Pro models.

The model supports text, images, audio, and video, with an exceptionally large context window of roughly one million input tokens and outputs up to ~65k tokens. It emphasizes rapid responses for coding, summarization, analysis, and agentic tasks, and exposes configurable “thinking levels” via API to trade speed for deeper reasoning. Today, Gemini 3 Flash positions itself as a high-throughput, production-ready model, serving as the default in the Gemini app and Google Search’s AI Mode, optimized for scalable, interactive AI applications.

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 3 Flash vs GPT-5.4 Nano Comparison Table

PropertyGemini 3 FlashGPT-5.4 Nano
OrganizationGoogleOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateDec 2025Mar 2026
Context Window1.0M400K
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.500$0.200
Output $/1M$3.00$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
74.63%
62.69%
Avg Response Time9.85s3.72s
Median input tokensincl. image tokens1.1K1.4K
Median output tokens290105
Est. cost / taskon this benchmark$0.0014$0.0004
Defect Detection
73.3%(11/15)
80%(12/15)
Document Understanding
88.9%(8/9)
77.8%(7/9)
Object Counting
30%(3/10)
30%(3/10)
Object Understanding
85.7%(12/14)
64.3%(9/14)
Spatial Understanding
84.2%(16/19)
57.9%(11/19)
OCR
Overall Score
93.01%
62.45%
Avg Response Time12.40s2.59s
Median input tokensincl. image tokens1.1K105
Median output tokens16087
Est. cost / taskon this benchmark$0.0010$0.0001
Focused Scene OCR
94.9%(94/99)
55.6%(55/99)
Handwritten Math
100%(10/10)
20%(2/10)
License Plate Recognition
100%(30/30)
83.3%(25/30)
Text Recognition
86.7%(26/30)
70%(21/30)
VQA & Extraction
88.3%(53/60)
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