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Claude 3 Haiku vs Gemini 3.1 Flash-Lite

Compare Claude 3 Haiku and Gemini 3.1 Flash-Lite side-by-side. See how these vision models stack up in Open Prompt, OCR, Image Captioning, Object Detection, and Classification.

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AnthropicClaude 3 Haiku

Claude 3 Haiku is deprecated and can no longer be run. Details and evals are still available on its model page.

GoogleGemini 3.1 Flash-Lite
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Claude 3 Haiku vs Gemini 3.1 Flash-Lite Comparison Table

Evals updated August 20, 2026Pricing updated August 23, 2026

PropertyClaude 3 HaikuGemini 3.1 Flash-Lite
OrganizationAnthropicGoogle
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateMar 2024Mar 2026
Context Window200K1.0M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.250
Output $/1M$1.50
Vision Tasks
CaptioningDemo
Chart Question Answering
ClassificationDemo
Document Question Answering
Image Tagging
Multi-Label Classification
Object DetectionDemo
OCRDemo
Vision Language
Visual Question AnsweringDemo
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision

Claude 3 Haiku vs Gemini 3.1 Flash-Lite: Overview

Claude 3 Haiku

Claude 3 Haiku is a large language model developed by Anthropic and released in March 2024 as part of the Claude 3 family, alongside Claude 3 Sonnet and Claude 3 Opus. It is designed to be the fastest and most cost-efficient model in the series, optimized for high-throughput applications.

Like the other Claude 3 models, Haiku is multimodal, able to process both text and image inputs while generating text outputs. It supports a context window of up to 200,000 tokens, with Anthropic noting that the Claude 3 models are technically capable of handling inputs exceeding one million tokens in special cases.

Haiku is positioned as a model well-suited for scenarios that demand speed and scalability at lower cost, such as customer support, summarization, and other tasks where rapid responses are prioritized. Compared to the larger Claude 3 Sonnet and Opus, Haiku provides lower latency and higher efficiency, while the larger models offer stronger reasoning and depth of analysis.

Gemini 3.1 Flash-Lite

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.