Gemini 3.1 Flash-Lite vs GPT-5.4 Mini
Compare Gemini 3.1 Flash-Lite and GPT-5.4 Mini side-by-side. See how these vision models stack up in Object Detection, Classification, Image Captioning, Open Prompt, and OCR.
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Models in this comparison
Gemini 3.1 Flash-Lite vs GPT-5.4 Mini: Overview
Gemini 3.1 Flash-Lite is a natively multimodal reasoning model from Google DeepMind in the Gemini 3 series, based on the Gemini 3 Pro architecture. It processes text, image, video, audio, and PDF inputs within a 1 million token context window and produces text output up to 64K tokens. The model targets high-volume, latency-sensitive workloads and supports visual question answering, image and document data extraction, content moderation, classification, translation, automated speech recognition, and agentic data pipelines. It exposes configurable thinking levels of minimal, low, medium, and high, which set the depth of internal reasoning applied per request and let developers balance response quality against cost and latency.
On benchmarks reported at launch, Gemini 3.1 Flash-Lite scores 86.9% on GPQA Diamond and 76.8% on the MMMU Pro multimodal benchmark, and reaches an Elo score of 1432 on the Arena.ai leaderboard. According to Artificial Analysis benchmarks, it produces a 2.5 times faster time to first answer token and a 45% increase in output speed relative to Gemini 2.5 Flash. It also shows improved instruction following, higher audio input quality for automated speech recognition tasks, and support for structured JSON output used in data extraction pipelines.
GPT-5.4 mini is a fast, cost-efficient model developed by OpenAI and released on March 17, 2026, optimized for high-throughput workloads and subagent orchestration. It supports text and image inputs within a 400,000-token context window, making it ideal for processing extensive visual datasets and large codebases in a single request. Designed for low-latency production environments, the model integrates with key API features including function calling, web search, and tool-based computer use, allowing it to assist in automated workflows that require navigating digital interfaces.
Compared to the previous GPT-5 mini, this version runs more than twice as fast while approaching the performance levels of the flagship GPT-5.4 on reasoning and coding benchmarks. While the larger GPT-5.4 introduces native, state-of-the-art computer-use capabilities, GPT-5.4 mini provides a scalable alternative for interpreting screenshots and reasoning over dense UI layouts. For vision tasks on Playground, it excels at extracting structured information from visual documents and assisting in agentic tasks that involve real-time interpretation of software interfaces alongside text.
Gemini 3.1 Flash-Lite vs GPT-5.4 Mini Comparison Table
| Property | Gemini 3.1 Flash-Lite | GPT-5.4 Mini |
|---|---|---|
| Organization | OpenAI | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Mar 2026 | Mar 2026 |
| Context Window | 1.0M | 400K |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.250 | $0.750 |
| Output $/1M | $1.50 | $4.50 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Classification | Demo | Demo |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| 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 | 68.66% | 77.61% |
| Avg Response Time | 1.86s | 5.80s |
| Median input tokensincl. image tokens | 1.1K | 1.4K |
| Median output tokens | 6 | 104 |
| Est. cost / taskon this benchmark | $0.0003 | $0.0015 |
| Defect Detection | 73.3%(11/15) | 73.3%(11/15) |
| Document Understanding | 77.8%(7/9) | 88.9%(8/9) |
| Object Counting | 30%(3/10) | 40%(4/10) |
| Object Understanding | 64.3%(9/14) | 92.9%(13/14) |
| Spatial Understanding | 84.2%(16/19) | 84.2%(16/19) |
| OCR | ||
| Overall Score | 89.96% | 77.29% |
| Avg Response Time | 1.32s | 3.24s |
| Median input tokensincl. image tokens | 1.1K | 105 |
| Median output tokens | 10 | 126 |
| Est. cost / taskon this benchmark | $0.0003 | $0.0006 |
| Focused Scene OCR | 91.9%(91/99) | 75.8%(75/99) |
| Handwritten Math | 80%(8/10) | 40%(4/10) |
| License Plate Recognition | 100%(30/30) | 86.7%(26/30) |
| Text Recognition | 90%(27/30) | 73.3%(22/30) |
| VQA & Extraction | 83.3%(50/60) | 83.3%(50/60) |
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