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Google: Gemini 3.1 Flash-Lite

Gemini 3.1 Flash-Lite 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.

Gemini 3.1 Flash-Lite Interactive Demo

Gemini 3.1 Flash-Lite Details & Performance

Details

Resources

Tutorial

Vision Tasks

CaptioningClassificationDocument Question AnsweringImage TaggingMulti-Label ClassificationOCRObject DetectionVision LanguageVisual Question Answering

Features

Multimodal VisionLLMs with Vision Capabilities

Usage

Past 30 Days

Performance

Avg. Latency

Arena Rankings

Gemini 3.1 Flash-Lite Vision Evals

Gemini 3.1 Flash-Lite has not yet been evaluated on the current benchmark. The results below are from the legacy version of Vision Evals, our previous benchmark. See the current Vision Evals

Visual Understanding

77 models · 67 tasks
HighestLowest
This model#25 of 7768.66% pass rate · better than 62%
Score68.66%pass rate across 67 tasks
Speed1.86savg response per task
Cost$0.0003 / task$0.250 in · $1.50 out / 1M
Tokens1.1K / task1.1K in · 6 out
Score key:≥75%40–74%<40%
CategoryPassedScore
Spatial Understanding16 / 19
84.2%
Document Understanding7 / 9
77.8%
Defect Detection11 / 15
73.3%
Object Understanding9 / 14
64.3%
Object Counting3 / 10
30%

OCR

58 models · 229 tasks
HighestLowest
This model#4 of 5889.96% pass rate · better than 93%
Score89.96%pass rate across 229 tasks
Speed1.32savg response per task
Cost$0.0003 / task$0.250 in · $1.50 out / 1M
Tokens1.1K / task1.1K in · 10 out
Score key:≥75%40–74%<40%
CategoryPassedScore
License Plate Recognition30 / 30
100%
Focused Scene OCR91 / 99
91.9%
Text Recognition27 / 30
90%
VQA & Extraction50 / 60
83.3%
Handwritten Math8 / 10
80%

Scores based on a single evaluation run · Methodology

View all legacy Vision Evals results →

Gemini 3.1 Flash-Lite Pricing

Gemini 3.1 Flash-Lite costs $0.250 per 1M input tokens and $1.50 per 1M output tokens.

Input$0.250 / 1M tokens
Output$1.50 / 1M tokens
Cached input$0.025 / 1M tokens

Pricing updated Jul 21, 2026

Price vs. performance

Estimated cost per task vs. Visual Understanding score, for this model and others ranked near it. Upper-left is the sweet spot (high quality, low cost). Based on Vision Evals (legacy) results.

11 of 11 models plotted

ModelScoreMedian tokensEst. cost / taskCompare
QwenQwen3.5 27B71.6%1.2K$0.0003Compare
AnthropicClaude Sonnet 570.2%2.2K$0.0048Compare
AnthropicClaude Sonnet 4.670.2%2.3K$0.0080Compare
OpenAIGPT-5.6 Luna70.2%1.5K$0.0017Compare
GoogleGemini 2.5 Pro70.2%856$0.0060Compare
GoogleGemini 3.1 Flash-Lite(this model)68.7%1.1K$0.0003—
GoogleGemma 4 26B A4B68.7%531$0.0001Compare
QwenQwen3.6 Plus68.7%1.6K$0.0005Compare
AnthropicClaude Opus 4.867.2%2.2K$0.012Compare
AnthropicClaude Opus 4.767.2%2.6K$0.015Compare
GoogleGemma 4 31B67.2%467$0.0001Compare

Alternatives to Gemini 3.1 Flash-Lite

Other models worth comparing for similar use cases.

OpenAI
GPT-5 Nano
GPT-5 Nano, released by OpenAI on August 7, 2025, is the smallest and most cost-efficient model in the GPT-5 family. Like its larger counterparts, it is multimodal—accepting text and images, supporting tool use, structured outputs, and reasoning—but it is optimized for speed, low latency, and affordability. It features input and output token limits of roughly 272K and 128K tokens respectively, enabling large-context processing even at its compact scale. Its knowledge cutoff is around May 2024, slightly earlier than the full GPT-5 model.GPT-5 Nano is well-suited for high-volume or cost-sensitive deployments such as mobile apps, embedded AI systems, or rapid-response APIs. While it offers less depth on complex reasoning and coding tasks compared to GPT-5 Mini or Pro, it retains core multimodal and agentic capabilities, making it an attractive option where efficiency and scale matter more than maximum performance.
OpenAI
GPT-5.4 Nano
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.
Anthropic
Claude Haiku 4.5
Claude Haiku 4.5 is Anthropic’s lightweight model in the Claude 4.5 series, released in October 2025 under a proprietary license. Designed for speed and cost efficiency, it delivers near-frontier performance while maintaining Anthropic’s AI Safety Level 2 standard. Haiku 4.5 supports both text and multimodal (text and image) inputs, integrates tool use and extended reasoning, and features a 200,000 token context window, making it adept at handling long or complex workflows. Though the parameter count remains undisclosed, it achieves about 73.3% on SWE-bench Verified, reflecting strong coding and reasoning ability. Haiku 4.5 is ideal for developers and researchers seeking rapid, cost-effective model calls for analysis, coding, or multimodal understanding.
Qwen
Qwen2.5 VL 7B Instruct
Qwen2.5-VL-7B-Instruct is a 7-billion parameter vision-language model from Alibaba’s QwenLM team, released on January 26, 2025 under the Apache 2.0 license. It is the instruction-tuned variant of the 7B scale in the Qwen2.5-VL family, designed to process multimodal inputs such as text, images, charts, documents, and video. The model enables structured outputs—including JSON for structured content and bounding boxes for visual localization. Weights are publicly available on Hugging Face and GitHub, making it suitable for both research and applied multimodal use.

Other Google Gemini Flash-Lite models

Other versions in the same family as Gemini 3.1 Flash-Lite.

GoogleGemini 2.5 Flash-Lite

Gemini 3.1 Flash-Lite License

Proprietary

License terms and commercial-use guidance for Gemini 3.1 Flash-Lite.

This model is proprietary. The author retains all rights, and use of the model is governed by their specific terms of service or license agreement.

Commercial use depends on the terms set by the model author. Most proprietary commercial models require a paid subscription, API key, or per-call billing. Check the provider’s pricing and terms-of-service for details.

License information is provided as a guide and is not legal advice.

Frequently Asked Questions About Gemini 3.1 Flash-Lite Vision

Yes. Gemini 3.1 Flash-Lite accepts image input, and on Roboflow's previous vision benchmark it passed 68.7% of visual understanding tasks (#25 of 77) and scored 90% on OCR. You can test it on your own image in the demo above.

Gemini 3.1 Flash-Lite has not yet been evaluated on Roboflow's current Vision Evals. The results on this page are from the previous benchmark.

Yes. The demo on this page runs Gemini 3.1 Flash-Lite in the free Roboflow Playground: upload an image and see results in seconds. A free account unlocks unlimited runs.