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Claude Opus 4 vs Gemini 3.1 Flash-Lite

Compare Claude Opus 4 and Gemini 3.1 Flash-Lite side-by-side. See how these vision models stack up in Image Captioning, OCR, Object Detection, Open Prompt, and Classification.

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AnthropicClaude Opus 4

Claude Opus 4 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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Models in this comparison

Anthropic

Claude Opus 4 vs Gemini 3.1 Flash-Lite: Overview

Claude Opus 4

Claude 4 Opus, released by Anthropic in May 2025, is the flagship model of the Claude 4 family, built for complex, long-horizon reasoning and advanced coding workflows. It is multimodal, supporting text (including voice), images, and tool use, and operates as a hybrid reasoning model—able to deliver quick answers in fast mode or switch to extended thinking for deeper, multi-step problem solving. With a ~200,000-token context window and a training cutoff around March 2025, it is optimized for handling large documents, long conversations, and sophisticated agentic tasks.

Positioned at the high end of Anthropic’s offerings, Opus 4 achieves state-of-the-art results on coding benchmarks like SWE-Bench (72.5%) and Terminal-Bench (43.2%). It is best suited for research, enterprise automation, and software development at scale. The model is classified at Anthropic’s ASL-3 safety level, denoting advanced oversight and safety features.

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 Opus 4 vs Gemini 3.1 Flash-Lite Comparison Table

PropertyClaude Opus 4Gemini 3.1 Flash-Lite
OrganizationAnthropicGoogle
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateMay 2025Mar 2026
Context Window200K1.0M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$15.00$0.250
Output $/1M$75.00$1.50
Vision Tasks
CaptioningDemo
ClassificationDemo
Object DetectionDemo
OCRDemo
Vision Language
Visual Question AnsweringDemo
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
56.72%
68.66%
Avg Response Time19.74s1.86s
Median input tokensincl. image tokens1.1K
Median output tokens6
Est. cost / taskon this benchmark$0.0003
Defect Detection
66.7%(10/15)
73.3%(11/15)
Document Understanding
88.9%(8/9)
77.8%(7/9)
Object Counting
0%(0/10)
30%(3/10)
Object Understanding
64.3%(9/14)
64.3%(9/14)
Spatial Understanding
57.9%(11/19)
84.2%(16/19)
OCR
Overall Score
89.96%
Avg Response Time1.32s
Median input tokensincl. image tokens1.1K
Median output tokens10
Est. cost / taskon this benchmark$0.0003
Focused Scene OCR
91.9%(91/99)
Handwritten Math
80%(8/10)
License Plate Recognition
100%(30/30)
Text Recognition
90%(27/30)
VQA & Extraction
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