Gemini 3.1 Flash-Lite vs GPT-5.4
Compare Gemini 3.1 Flash-Lite and GPT-5.4 side-by-side. See how these vision models stack up in Object Detection, Classification, Image Captioning, Open Prompt, and OCR.
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Gemini 3.1 Flash-Lite vs GPT-5.4: 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 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 3.1 Flash-Lite vs GPT-5.4 Comparison Table
| Property | Gemini 3.1 Flash-Lite | GPT-5.4 |
|---|---|---|
| Organization | OpenAI | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Mar 2026 | Mar 2026 |
| Context Window | 1.0M | 1.1M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.250 | $2.50 |
| Output $/1M | $1.50 | $15.00 |
| 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 | 7.16s |
| Median input tokensincl. image tokens | 1.1K | 1.4K |
| Median output tokens | 6 | 108 |
| Est. cost / taskon this benchmark | $0.0003 | $0.0052 |
| Defect Detection | 73.3%(11/15) | 86.7%(13/15) |
| Document Understanding | 77.8%(7/9) | 88.9%(8/9) |
| Object Counting | 30%(3/10) | 40%(4/10) |
| Object Understanding | 64.3%(9/14) | 85.7%(12/14) |
| Spatial Understanding | 84.2%(16/19) | 78.9%(15/19) |
| OCR | ||
| Overall Score | 89.96% | 79.48% |
| Avg Response Time | 1.32s | 3.98s |
| Median input tokensincl. image tokens | 1.1K | 105 |
| Median output tokens | 10 | 95 |
| Est. cost / taskon this benchmark | $0.0003 | $0.0017 |
| Focused Scene OCR | 91.9%(91/99) | 75.8%(75/99) |
| Handwritten Math | 80%(8/10) | 60%(6/10) |
| License Plate Recognition | 100%(30/30) | 90%(27/30) |
| Text Recognition | 90%(27/30) | 83.3%(25/30) |
| VQA & Extraction | 83.3%(50/60) | 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