Gemini 3 Pro vs GPT-5.4
Compare Gemini 3 Pro and GPT-5.4 side-by-side. See how these vision models stack up in Object Detection, Classification, OCR, Image Captioning, and Open Prompt.
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Gemini 3 Pro is deprecated and can no longer be run. Details and evals are still available on its model page.
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Gemini 3 Pro vs GPT-5.4: Overview
Gemini 3 Pro is Google DeepMind’s flagship multimodal frontier model, built for high-accuracy reasoning and large-scale context understanding across text, images, audio, video, code, and documents. It delivers major gains over Gemini 2.5 Pro, supported by a 1M-token window and strong performance on Google-reported benchmarks such as GPQA Diamond, MMMU-Pro, and Video-MMMU.
The model excels at structured outputs, tool use, and agentic coding, enabling complex multi-step workflows and analysis of entire books, codebases, or long videos in a single prompt. Positioned as Google’s top production model, it balances advanced reasoning with broad multimodal capabilities, making it well suited for research assistants, automation agents, coding systems, and enterprise-scale document and media analysis.
GPT-5.4 is a proprietary multimodal large language model developed by OpenAI and released on March 5, 2026. It is designed for professional workloads such as advanced software development, research, and agentic automation. The model combines the general reasoning capabilities of the GPT-5 series with software engineering improvements derived from GPT-5.3-Codex. In the API and Codex environments it supports context windows of up to 1 million tokens, enabling long-context reasoning and large-scale code or document workflows.
Compared with GPT-5.2, GPT-5.4 reduces false individual claims by 33% and lowers overall response errors by 18%, improving factual reliability across complex tasks. It is also the first general-purpose OpenAI release with native computer-use capabilities, allowing agents to interact with desktops, browsers, and external applications to complete multi-step workflows. The model family includes three variants: GPT-5.4 (standard), GPT-5.4 Pro for higher-performance workloads, and GPT-5.4 Thinking, a reasoning-oriented version in ChatGPT that presents an upfront plan before generating its response. The API also introduces a Tool Search system that allows models to retrieve tool definitions dynamically, reducing token usage in tool-heavy integrations.
Gemini 3 Pro vs GPT-5.4 Comparison Table
| Property | Gemini 3 Pro | GPT-5.4 |
|---|---|---|
| Organization | OpenAI | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Nov 2025 | Mar 2026 |
| Context Window | 1.0M | 1.1M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $2.50 | |
| Output $/1M | $15.00 | |
| Vision Tasks | ||
| Captioning | Demo | |
| Classification | Demo | |
| Object Detection | Demo | |
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| 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 | 77.61% | |
| Avg Response Time | 7.16s | |
| Median input tokensincl. image tokens | 1.4K | |
| Median output tokens | 108 | |
| Est. cost / taskon this benchmark | $0.0052 | |
| Defect Detection | 86.7%(13/15) | |
| Document Understanding | 88.9%(8/9) | |
| Object Counting | 40%(4/10) | |
| Object Understanding | 85.7%(12/14) | |
| Spatial Understanding | 78.9%(15/19) | |
| OCR | ||
| Overall Score | 79.48% | |
| Avg Response Time | 3.98s | |
| Median input tokensincl. image tokens | 105 | |
| Median output tokens | 95 | |
| Est. cost / taskon this benchmark | $0.0017 | |
| Focused Scene OCR | 75.8%(75/99) | |
| Handwritten Math | 60%(6/10) | |
| License Plate Recognition | 90%(27/30) | |
| Text Recognition | 83.3%(25/30) | |
| VQA & Extraction | 81.7%(49/60) | |
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