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Claude Haiku 4.5 vs Gemini 3.1 Flash-Lite

Compare Claude Haiku 4.5 and Gemini 3.1 Flash-Lite side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, OCR, Classification, and Object Detection.

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AnthropicClaude Haiku 4.5
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GoogleGemini 3.1 Flash-Lite
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Models in this comparison

Claude Haiku 4.5 vs Gemini 3.1 Flash-Lite: Overview

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.

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.

Claude Haiku 4.5 vs Gemini 3.1 Flash-Lite Comparison Table

PropertyClaude Haiku 4.5Gemini 3.1 Flash-Lite
OrganizationAnthropicGoogle
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateOct 2025Mar 2026
Context Window200K1.0M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$1.00$0.250
Output $/1M$5.00$1.50
Vision Tasks
CaptioningDemoDemo
ClassificationDemoDemo
Object DetectionDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
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
58.21%
68.66%
Avg Response Time3.15s1.86s
Median input tokensincl. image tokens2.2K1.1K
Median output tokens1746
Est. cost / taskon this benchmark$0.0030$0.0003
Defect Detection
80%(12/15)
73.3%(11/15)
Document Understanding
77.8%(7/9)
77.8%(7/9)
Object Counting
0%(0/10)
30%(3/10)
Object Understanding
71.4%(10/14)
64.3%(9/14)
Spatial Understanding
52.6%(10/19)
84.2%(16/19)
OCR
Overall Score
61.57%
89.96%
Avg Response Time2.13s1.32s
Median input tokensincl. image tokens7351.1K
Median output tokens10110
Est. cost / taskon this benchmark$0.0012$0.0003
Focused Scene OCR
61.6%(61/99)
91.9%(91/99)
Handwritten Math
20%(2/10)
80%(8/10)
License Plate Recognition
66.7%(20/30)
100%(30/30)
Text Recognition
63.3%(19/30)
90%(27/30)
VQA & Extraction
65%(39/60)
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