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Grok 4.6 vs Qwen3.7 Plus

Compare Grok 4.6 and Qwen3.7 Plus side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, Classification, OCR, and Object Detection.

Compare Grok 4.6 vs Qwen3.7 Plus live

Run the same image across every model that supports a task and compare their outputs side-by-side.

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Open Object Detection in the full playground
GrokGrok 4.6
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QwenQwen3.7 Plus
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Models in this comparison

Grok 4.6 vs Qwen3.7 Plus on Vision Evals

Grok 4.6 scores higher on 3 of the five Vision Evals tasks.

The widest gap is Object Detection, where Qwen3.7 Plus leads 60.1% to 23.8%.

Overall, Grok 4.6 averages 59.1% (#36 of 63) against 58.9% (#37 of 63) for Qwen3.7 Plus.

Qwen3.7 Plus is both cheaper ($0.0008 vs $0.011 per sample) and faster (7.8s vs 19.3s per sample).

Grok 4.6Qwen3.7 Plus

Grok 4.6 vs Qwen3.7 Plus Comparison Table

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

PropertyGrok 4.6Qwen3.7 Plus
OrganizationSpaceXAIQwen
Categoryclosedclosed
Modalitymultimodal—
Release DateAug 2026Jun 2026
Context Window500K—
ParametersUnknownUnknown
LicenseProprietaryUnknown
Commercial usecommercial use per vendor termsSee license terms
Pricing per 1M tokens
Input $/1M$2.00$0.320
Output $/1M$6.00$1.28
Vision Tasks
CaptioningDemoDemo
ClassificationDemoDemo
Object DetectionDemoDemo
OCRDemoDemo
Visual Question AnsweringDemoDemo
Chart Question AnsweringSupportedNot listed
Document Question AnsweringSupportedNot listed
Image TaggingSupportedNot listed
Multi-Label ClassificationSupportedNot listed
Vision LanguageSupportedNot listed
Model Features
Foundation VisionSupportedNot listed
LLMs with Vision CapabilitiesSupportedNot listed
Multimodal VisionSupportedNot listed
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
59.1%
58.9%
Avg cost / sample$0.011$0.0008
Avg speed / sample19.32s7.77s
By task
Object Detection (low)
23.8%
±2.8, Mean of 3 runs, range 20.2 to 25.9
$0.013
60.1%
$0.0013
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
50.0%
$0.0004
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
84.4%
$0.0003
Identification (high)
85.4%
±1.6, Mean of 3 runs, range 84.4 to 87.5
$0.015
–
OCR (low)
60.6%
$0.011
60.3%
$0.0009
by category
Single value
50.0%
Transcription
89.0%
Structured JSON
84.4%
Text localization
11.0%
Single value
53.5%
Transcription
86.7%
Structured JSON
75.8%
Text localization
23.1%
OCR (high)
61.2%
$0.030
65.5%
$0.0042
by category
Single value
50.9%
Transcription
88.7%
Structured JSON
85.7%
Text localization
9.4%
Single value
58.3%
Transcription
89.7%
Structured JSON
81.3%
Text localization
30.4%
Reasoning (low)
61.1%
±1.3, Mean of 3 runs, range 59.6 to 62.3
$0.0093
39.7%
$0.0003
Reasoning (high)
63.8%
±2.0, Mean of 3 runs, range 62.3 to 66.2
$0.032
68.2%
$0.0043

Grok 4.6 vs Qwen3.7 Plus: Overview

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.

Qwen3.7 Plus
No description available