Gemini 3 Flash vs GPT-5.5
Compare Gemini 3 Flash and GPT-5.5 side-by-side. See how these vision models stack up in Object Detection, Classification, Open Prompt, OCR, and Image Captioning.
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Gemini 3 Flash vs GPT-5.5: Overview
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-5.5 is a multimodal large language model released by OpenAI on April 23, 2026, engineered for autonomous, multi-step knowledge work and agentic workflows. It accepts text, images, and code as input, featuring enhanced spatial reasoning and visual grounding to support its computer use capabilities for operating software and navigating UI elements. Built to execute complex workflows end-to-end, the model interprets loosely defined tasks, selects appropriate tools, and performs self-verification with minimal user intervention. It is available in a standard version, a Thinking mode for extended reasoning budgets, and a Pro variant that uses parallel test-time compute for maximum precision on complex tasks.
Co-optimized with NVIDIA for GB200 NVL72 infrastructure, GPT-5.5 delivers per-token latency comparable to its predecessor GPT-5.4 while maintaining a 1-million-token context window. Despite increased capability, the model achieves greater token efficiency in coding and data analysis workflows, often completing tasks with fewer total tokens than previous versions. OpenAI reports a 60% reduction in hallucination rate compared to GPT-5.4, improving reliability for accuracy-sensitive applications. API access is available via the Responses and Chat Completions endpoints at $5 per million input tokens and $30 per million output tokens, double the unit price of GPT-5.4.
Gemini 3 Flash vs GPT-5.5 Comparison Table
| Property | Gemini 3 Flash | GPT-5.5 |
|---|---|---|
| Organization | OpenAI | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Dec 2025 | Apr 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.500 | $5.00 |
| Output $/1M | $3.00 | $30.00 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Classification | Demo | Demo |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
| Foundation Vision | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Visual Understanding | ||
| Overall Score | 74.63% | 77.61% |
| Avg Response Time | 9.85s | 30.12s |
| Median input tokensincl. image tokens | 1.1K | 1.4K |
| Median output tokens | 290 | 138 |
| Est. cost / taskon this benchmark | $0.0014 | $0.011 |
| Defect Detection | 73.3%(11/15) | 86.7%(13/15) |
| Document Understanding | 88.9%(8/9) | 88.9%(8/9) |
| Object Counting | 30%(3/10) | 30%(3/10) |
| Object Understanding | 85.7%(12/14) | 92.9%(13/14) |
| Spatial Understanding | 84.2%(16/19) | 78.9%(15/19) |
| OCR | ||
| Overall Score | 93.01% | 81.22% |
| Avg Response Time | 12.40s | 5.16s |
| Median input tokensincl. image tokens | 1.1K | 105 |
| Median output tokens | 160 | 83 |
| Est. cost / taskon this benchmark | $0.0010 | $0.0030 |
| Focused Scene OCR | 94.9%(94/99) | 77.8%(77/99) |
| Handwritten Math | 100%(10/10) | 40%(4/10) |
| License Plate Recognition | 100%(30/30) | 93.3%(28/30) |
| Text Recognition | 86.7%(26/30) | 83.3%(25/30) |
| VQA & Extraction | 88.3%(53/60) | 86.7%(52/60) |
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