Roboflow

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.

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

Detect and compare bounding boxes across models on the same image.

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 4 of the six Vision Evals tasks.

The widest gap is Object Detection, where Qwen3.7 Plus leads 60.1% to 20.2%.

Overall, Grok 4.6 averages 67.8% (#17 of 31) against 67.4% (#18 of 31) for Qwen3.7 Plus.

Qwen3.7 Plus is both cheaper ($0.0008 vs $0.0069 per sample) and faster (7.0s vs 7.4s per sample).

Grok 4.6Qwen3.7 Plus

Grok 4.6 vs Qwen3.7 Plus Comparison Table

Evals updated August 20, 2026Pricing updated August 22, 2026

PropertyGrok 4.6Qwen3.7 Plus
OrganizationSpaceXAIQwen
Categoryclosedclosed
Modalitymultimodal
Release DateAug 2026
Context Window500K
Parameters
LicenseProprietary
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 Answering
Document Question Answering
Image Tagging
Multi-Label Classification
Vision Language
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort
Overall
67.8%
67.4%
Avg cost / sample$0.0069$0.0008
Avg speed / sample7.39s7.01s
By task
Object Detection
20.2%
$0.0068
60.1%
$0.0013
Counting
70.3%
$0.0074
50.0%
$0.0004
Identification
78.1%
$0.0048
84.4%
$0.0003
OCR
92.0%
$0.0086
86.5%
$0.0009
Data Extraction
84.5%
$0.0042
83.5%
$0.0004
Reasoning (low)
61.6%
$0.0087
39.7%
$0.0003
Reasoning (high)
61.6%
$0.027
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

Frequently Asked Questions

On Roboflow's Vision Evals, Grok 4.6 performed better. It scores higher on 4 of the six vision tasks and averages 67.8% (#17 of 31) against 67.4% (#18 of 31) for Qwen3.7 Plus. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.

No. On the Vision Evals Object Detection benchmark, Qwen3.7 Plus leads with 60.1% against 20.2%. This is the widest gap between the two models across the benchmark's tasks.

Qwen3.7 Plus is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0008 per sample against $0.0069. Grok 4.6 is priced at $2.00 per 1M input tokens and $6.00 per 1M output; Qwen3.7 Plus is priced at $0.32 per 1M input tokens and $1.28 per 1M output. Actual costs depend on your image sizes, prompts, and output length.

Qwen3.7 Plus is faster. Across Roboflow's Vision Evals it averaged 7.0s per inference against 7.4s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.

Yes. The comparison demo on this page runs both models on the same image side by side for image captioning and open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.