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Claude Opus 4.6 vs Gemini 3.1 Flash-Lite

Compare Claude Opus 4.6 and Gemini 3.1 Flash-Lite side-by-side. See how these vision models stack up in Open Prompt, OCR, Object Detection, Classification, and Image Captioning.

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AnthropicClaude Opus 4.6
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GoogleGemini 3.1 Flash-Lite
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Models in this comparison

Claude Opus 4.6 vs Gemini 3.1 Flash-Lite: Overview

Claude Opus 4.6

Claude Opus 4.6 is the flagship large language model from Anthropic, released on 2026-02-05 for advanced reasoning, complex coding, and enterprise agent workflows. It supports text and image inputs via API, offers a 200K-token standard context window with a 1M-token beta option, and enables outputs up to 128K tokens, with adaptive reasoning and context compaction for sustained tasks.

As of 2026-02-17, Anthropic also released Claude Sonnet 4.6, extending the 1M-token context window to a broader tier. Opus remains positioned for maximum depth and benchmark performance, while Sonnet 4.6 brings long-context capability to more cost- and latency-sensitive production use cases.

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 4.6 vs Gemini 3.1 Flash-Lite Comparison Table

PropertyClaude Opus 4.6 Gemini 3.1 Flash-Lite
OrganizationAnthropicGoogle
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateFeb 2026Mar 2026
Context Window1.0M1.0M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$5.00$0.250
Output $/1M$25.00$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
64.18%
68.66%
Avg Response Time23.35s1.86s
Median input tokensincl. image tokens2.2K1.1K
Median output tokens1306
Est. cost / taskon this benchmark$0.014$0.0003
Defect Detection
73.3%(11/15)
73.3%(11/15)
Document Understanding
77.8%(7/9)
77.8%(7/9)
Object Counting
20%(2/10)
30%(3/10)
Object Understanding
71.4%(10/14)
64.3%(9/14)
Spatial Understanding
68.4%(13/19)
84.2%(16/19)
OCR
Overall Score
82.53%
89.96%
Avg Response Time5.05s1.32s
Median input tokensincl. image tokens7361.1K
Median output tokens9910
Est. cost / taskon this benchmark$0.0062$0.0003
Focused Scene OCR
85.9%(85/99)
91.9%(91/99)
Handwritten Math
70%(7/10)
80%(8/10)
License Plate Recognition
90%(27/30)
100%(30/30)
Text Recognition
80%(24/30)
90%(27/30)
VQA & Extraction
76.7%(46/60)
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