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Claude Sonnet 4.6 vs Grok 4.6

Compare Claude Sonnet 4.6 and Grok 4.6 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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GrokGrok 4.6
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Claude Sonnet 4.6 vs Grok 4.6 Comparison Table

Evals updated October 13, 2026Pricing updated October 17, 2026

PropertyClaude Sonnet 4.6Grok 4.6
OrganizationAnthropicSpaceXAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateFeb 2026Aug 2026
Context Window1.0M500K
ParametersUnknownUnknown
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$3.00$2.00
Output $/1M$15.00$6.00
Vision Tasks
CaptioningDemoDemo
Chart Question AnsweringSupportedSupported
ClassificationDemoDemo
Document Question AnsweringSupportedSupported
Image TaggingSupportedSupported
Multi-Label ClassificationSupportedSupported
Object DetectionDemoDemo
OCRDemoDemo
Vision LanguageSupportedSupported
Visual Question AnsweringDemoDemo
Model Features
Foundation VisionSupportedSupported
LLMs with Vision CapabilitiesSupportedSupported
Multimodal VisionSupportedSupported
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
OverallNot evaluated
59.1%
Avg cost / sample–$0.011
Avg speed / sample–19.32s
By task
Object Detection (low)–
23.8%
±2.8, Mean of 3 runs, range 20.2 to 25.9
$0.013
Object Detection (high)–
24.0%
±1.0, Mean of 3 runs, range 23.1 to 25.1
$0.041
Counting (low)–
65.8%
±4.1, Mean of 3 runs, range 62.2 to 70.3
$0.0079
Counting (high)–
56.8%
±1.4, Mean of 3 runs, range 55.4 to 58.1
$0.027
Identification (low)–
84.4%
±3.1, Mean of 3 runs, range 81.3 to 87.5
$0.0055
Identification (high)–
85.4%
±1.6, Mean of 3 runs, range 84.4 to 87.5
$0.015
OCR (low)–
60.6%
$0.011
by category
Single value
50.0%
Transcription
89.0%
Structured JSON
84.4%
Text localization
11.0%
OCR (high)–
61.2%
$0.030
by category
Single value
50.9%
Transcription
88.7%
Structured JSON
85.7%
Text localization
9.4%
Reasoning (low)–
61.1%
±1.3, Mean of 3 runs, range 59.6 to 62.3
$0.0093
Reasoning (high)–
63.8%
±2.0, Mean of 3 runs, range 62.3 to 66.2
$0.032

Claude Sonnet 4.6 vs Grok 4.6: 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.

Grok 4.6

Grok 4.6 is a proprietary reasoning model from xAI aimed at long-running agentic workflows, coding, and knowledge work. It accepts text and image input and returns text, with a 500,000 token context window and a knowledge cutoff of February 1, 2026. The model exposes an adjustable reasoning budget with low, medium, high, and xhigh settings, where high is the default, and it supports function calling, structured outputs, web and X search, and code execution as documented tool behaviors. Its visual capability covers interpreting images supplied alongside text prompts, which places it in the visual question answering and document understanding family, and it can also return object detection boxes as text coordinates when prompted.

xAI characterizes Grok 4.6 as the result of an extended post-training run over the Grok 4.5 lineage rather than a new pretrained base. The described recipe combines curated model-generated reasoning and technical data, engineering data, a revised optimizer, regenerated supervised fine-tuning trajectories, and reinforcement learning across agent environments spanning knowledge work, coding, kernel optimization, web development, and computer-aided design. Parameter count and architecture specifics are not disclosed. Independent measurement from Artificial Analysis places the model at 61 on its Intelligence Index, five points above Grok 4.5.