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Grok 4.7 vs Qwen3 VL 8B Instruct

Compare Grok 4.7 and Qwen3 VL 8B Instruct side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, and OCR.

Compare Grok 4.7 vs Qwen3 VL 8B Instruct live

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

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GrokGrok 4.7
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QwenQwen3 VL 8B Instruct
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Models in this comparison

Grok 4.7 vs Qwen3 VL 8B Instruct Comparison Table

Evals updated October 8, 2026Pricing updated October 11, 2026

PropertyGrok 4.7Qwen3 VL 8B Instruct
OrganizationSpaceXAIQwen
Categoryclosedopen
Modality—multimodal
Release DateSep 2026Oct 2025
Context Window500K256K
ParametersUnknown8.8B
LicenseProprietaryApache 2.0
Pricing per 1M tokens
Input $/1M$2.00$0.250
Output $/1M$6.00$0.750
Vision Tasks
CaptioningDemoDemo
Chart Question AnsweringSupportedSupported
ClassificationDemoSupported
Document Question AnsweringSupportedSupported
Image TaggingSupportedSupported
Multi-Label ClassificationSupportedSupported
Object DetectionDemoSupported
OCRDemoDemo
Vision LanguageSupportedSupported
Visual Question AnsweringDemoDemo
Model Features
Foundation VisionNot listedSupported
LLMs with Vision CapabilitiesNot listedSupported
Multimodal VisionNot listedSupported
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
63.5%
Not evaluated
Avg cost / sample$0.020–
Avg speed / sample33.31s–
By task
Object Detection (low)
40.4%
±0.6, Mean of 3 runs, range 39.8 to 41.0
$0.021
–
Object Detection (high)
41.2%
±1.6, Mean of 3 runs, range 39.6 to 42.8
$0.028
–
Counting (low)
61.7%
±1.3, Mean of 3 runs, range 60.8 to 63.5
$0.011
–
Counting (high)
60.8%
±1.3, Mean of 3 runs, range 59.5 to 62.2
$0.017
–
Identification (low)
87.5%
±3.1, Mean of 3 runs, range 84.4 to 90.6
$0.0063
–
Identification (high)
80.2%
±1.6, Mean of 3 runs, range 78.1 to 81.3
$0.0093
–
OCR
63.6%
$0.023
–
by category
Single value
54.4%
Transcription
89.5%
Structured JSON
86.4%
Text localization
14.8%
Reasoning (low)
64.2%
±2.3, Mean of 3 runs, range 62.3 to 66.9
$0.015
–
Reasoning (high)
66.9%
±1.3, Mean of 3 runs, range 65.6 to 68.2
$0.024
–

Grok 4.7 vs Qwen3 VL 8B Instruct: Overview

Grok 4.7

Grok 4.7 is a proprietary multimodal reasoning model from SpaceXAI that accepts images alongside text and returns text-only output. On visual inputs it supports image captioning, visual question answering, OCR, document and chart question answering, and image classification and tagging, with all results expressed as generated text rather than bounding boxes or masks. Its 500,000 token context window leaves room for several images, long documents, or extended conversations about visual content in a single request.

The model exposes a configurable reasoning effort setting with low, medium, high, and xhigh levels (high by default), letting callers trade latency for the amount of deliberation spent on a prompt, including multi-step questions about an image. Built on a larger base model than Grok 4.6 with extended reinforcement learning on harder tasks, it works longer on difficult problems and checks its own work more carefully at the same serving speed. SpaceXAI's launch materials focus on coding and agentic knowledge work and report no image benchmark results.

Qwen3 VL 8B Instruct

Qwen3 VL 8B Instruct is an open-weight multimodal vision-language model developed by Qwen / Alibaba Cloud as part of the Qwen3-VL series, designed for instruction-following tasks that combine text with visual inputs such as images and video. Released around October 2025 under the Apache-2.0 license, it targets developers who need capable multimodal reasoning without the scale or cost of very large models.

The model contains roughly 8.8 billion dense parameters and supports text, image, and video understanding with strong spatial perception, visual reasoning, and emerging visual agent abilities such as GUI interaction. A standout feature is its native ~256K token context window, extendable to around 1M tokens, enabling long-document reading and extended video comprehension. In today’s landscape, it balances openness, long-context capacity, and solid multimodal performance against heavier proprietary models. Typical applications include multimodal assistants, document and video analysis, visual question answering, and research or product prototyping where transparency and deployability matter.

Frequently Asked Questions

Qwen3 VL 8B Instruct has not yet been evaluated on Roboflow's current Vision Evals, so this comparison shows specs, licensing, and pricing rather than benchmark scores.

Grok 4.7 is released under Proprietary, while Qwen3 VL 8B Instruct uses Apache 2.0. Licensing often matters more than raw accuracy for commercial deployments, so check the terms against how you plan to ship.

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.