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Claude Sonnet 4.6 vs Gemini 3.1 Flash-Lite

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

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AnthropicClaude Sonnet 4.6
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GoogleGemini 3.1 Flash-Lite
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Claude Sonnet 4.6 vs Gemini 3.1 Flash-Lite: Overview

Claude Sonnet 4.6

Claude Sonnet 4.6 is Anthropic's mid-tier large language model, released February 17, 2026, designed to balance performance, cost, and versatility for professional and developer use. It supports text and vision-based tasks with advanced reasoning, agentic capabilities, and Adaptive Thinking — a mode where the model dynamically scales its internal reasoning depth. A beta context window of up to 1,000,000 tokens (200K standard) enables processing of entire codebases or document collections in a single request. Parameters are undisclosed.

Optimized for coding, computer use, long-context reasoning, agent planning, and knowledge work, Sonnet 4.6 delivers a full generational upgrade over Sonnet 4.5 and approaches Opus 4.5-level performance across many benchmarks at a fraction of the cost. It is the default model on Claude.ai, Claude Cowork, and is available via API and major cloud platforms — making it well suited for production workloads requiring strong reasoning without flagship pricing.

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 Sonnet 4.6 vs Gemini 3.1 Flash-Lite Comparison Table

PropertyClaude Sonnet 4.6Gemini 3.1 Flash-Lite
OrganizationAnthropicGoogle
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateFeb 2026Mar 2026
Context Window1.0M1.0M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$3.00$0.250
Output $/1M$15.00$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
70.15%
68.66%
Avg Response Time4.24s1.86s
Median input tokensincl. image tokens2.2K1.1K
Median output tokens1056
Est. cost / taskon this benchmark$0.0080$0.0003
Defect Detection
80%(12/15)
73.3%(11/15)
Document Understanding
77.8%(7/9)
77.8%(7/9)
Object Counting
30%(3/10)
30%(3/10)
Object Understanding
71.4%(10/14)
64.3%(9/14)
Spatial Understanding
78.9%(15/19)
84.2%(16/19)
OCR
Overall Score
81.66%
89.96%
Avg Response Time3.42s1.32s
Median input tokensincl. image tokens7361.1K
Median output tokens8510
Est. cost / taskon this benchmark$0.0035$0.0003
Focused Scene OCR
85.9%(85/99)
91.9%(91/99)
Handwritten Math
50%(5/10)
80%(8/10)
License Plate Recognition
90%(27/30)
100%(30/30)
Text Recognition
86.7%(26/30)
90%(27/30)
VQA & Extraction
73.3%(44/60)
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