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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Gemini 3 Flash vs GPT-5.4 Nano: 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-5.4 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
| Property | Gemini 3 Flash | GPT-5.4 Nano |
|---|---|---|
| Organization | OpenAI | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Dec 2025 | Mar 2026 |
| Context Window | 1.0M | 400K |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.500 | $0.200 |
| Output $/1M | $3.00 | $1.25 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Classification | Demo | Demo |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | 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 | 74.63% | 62.69% |
| Avg Response Time | 9.85s | 3.72s |
| Median input tokensincl. image tokens | 1.1K | 1.4K |
| Median output tokens | 290 | 105 |
| 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 Time | 12.40s | 2.59s |
| Median input tokensincl. image tokens | 1.1K | 105 |
| Median output tokens | 160 | 87 |
| 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