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.
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Grok 4.7 vs Qwen3 VL 8B Instruct Comparison Table
Evals updated October 8, 2026Pricing updated October 11, 2026
| Property | Grok 4.7 | Qwen3 VL 8B Instruct |
|---|---|---|
| Organization | SpaceXAI | Qwen |
| Category | closed | open |
| Modality | — | multimodal |
| Release Date | Sep 2026 | Oct 2025 |
| Context Window | 500K | 256K |
| Parameters | Unknown | 8.8B |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $0.250 |
| Output $/1M | $6.00 | $0.750 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | Supported | Supported |
| Classification | Demo | Supported |
| Document Question Answering | Supported | Supported |
| Image Tagging | Supported | Supported |
| Multi-Label Classification | Supported | Supported |
| Object Detection | Demo | Supported |
| OCR | Demo | Demo |
| Vision Language | Supported | Supported |
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | Not listed | Supported |
| LLMs with Vision Capabilities | Not listed | Supported |
| Multimodal Vision | Not listed | Supported |
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 63.5% | Not evaluated |
| Avg cost / sample | $0.020 | – |
| Avg speed / sample | 33.31s | – |
| By task | ||
| Object Detection (low) | 40.4% ±0.6, Mean of 3 runs, range 39.8 to 41.0 | – |
| Object Detection (high) | 41.2% ±1.6, Mean of 3 runs, range 39.6 to 42.8 | – |
| Counting (low) | 61.7% ±1.3, Mean of 3 runs, range 60.8 to 63.5 | – |
| Counting (high) | 60.8% ±1.3, Mean of 3 runs, range 59.5 to 62.2 | – |
| Identification (low) | 87.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 | – |
| Identification (high) | 80.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 | – |
| OCR | 63.6% | – |
| by category |
| |
| Reasoning (low) | 64.2% ±2.3, Mean of 3 runs, range 62.3 to 66.9 | – |
| Reasoning (high) | 66.9% ±1.3, Mean of 3 runs, range 65.6 to 68.2 | – |
Grok 4.7 vs Qwen3 VL 8B Instruct: Overview
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 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.