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Claude Sonnet 4.6 vs Claude Sonnet 5

Compare Claude Sonnet 4.6 and Claude Sonnet 5 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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AnthropicClaude Sonnet 5
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Claude Sonnet 4.6 vs Claude Sonnet 5: 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.

Claude Sonnet 5

Claude Sonnet 5 is a mid-tier large language model from Anthropic, released on June 30, 2026, as the latest model in the Sonnet series and a direct successor to Claude Sonnet 4.6. It is a hybrid reasoning model designed primarily for agentic workflows, software coding, and professional tasks. The model features a 1 million token context window, a 128k maximum output token limit, and runs adaptive thinking by default, giving API users fine-grained control over reasoning effort across five levels (low, medium, high, max, and extra-high). It uses an updated tokenizer shared with Opus 4.7 and later models, which produces approximately 30% more tokens for equivalent text compared to earlier Claude models. On benchmarks, Sonnet 5 scores 63.2% on agentic coding and 81.2% on OSWorld, narrowing the gap with Opus 4.8 while remaining at Sonnet-tier pricing.

The model supports text and image input with text output, and accepts tools including browsers and terminals for autonomous multi-step task execution. Anthropic's safety evaluations report that Sonnet 5 shows a lower rate of undesirable behaviors than Sonnet 4.6 and is generally safer in agentic contexts, with improved resistance to prompt injection and reduced sycophancy. Cybersecurity safeguards equivalent to those on Opus 4.7 and 4.8 are active, though Anthropic notes the model was not deliberately trained on cybersecurity tasks. The model is proprietary and API-only, with no open weights.

Claude Sonnet 4.6 vs Claude Sonnet 5 Comparison Table

PropertyClaude Sonnet 4.6Claude Sonnet 5
OrganizationAnthropicAnthropic
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateFeb 2026Jun 2026
Context Window1.0M1.0M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$3.00$2.00
Output $/1M$15.00$10.00
Vision Tasks
CaptioningDemoDemo
ClassificationDemoDemo
Object DetectionDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Document Question Answering
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%
70.15%
Avg Response Time4.24s3.90s
Median input tokensincl. image tokens2.2K2.1K
Median output tokens10561
Est. cost / taskon this benchmark$0.0080$0.0048
Defect Detection
80%(12/15)
73.3%(11/15)
Document Understanding
77.8%(7/9)
66.7%(6/9)
Object Counting
30%(3/10)
20%(2/10)
Object Understanding
71.4%(10/14)
92.9%(13/14)
Spatial Understanding
78.9%(15/19)
78.9%(15/19)
OCR
Overall Score
81.66%
83.84%
Avg Response Time3.42s2.77s
Median input tokensincl. image tokens736642
Median output tokens8564
Est. cost / taskon this benchmark$0.0035$0.0019
Focused Scene OCR
85.9%(85/99)
88.9%(88/99)
Handwritten Math
50%(5/10)
50%(5/10)
License Plate Recognition
90%(27/30)
90%(27/30)
Text Recognition
86.7%(26/30)
80%(24/30)
VQA & Extraction
73.3%(44/60)
80%(48/60)

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