GPT-6 Sol vs Qwen3 VL 8B Instruct
Compare GPT-6 Sol and Qwen3 VL 8B Instruct side-by-side.
Compare GPT-6 Sol vs Qwen3 VL 8B Instruct live
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GPT-6 Sol vs Qwen3 VL 8B Instruct Comparison Table
Evals updated September 22, 2026Pricing updated September 22, 2026
| Property | GPT-6 Sol | Qwen3 VL 8B Instruct |
|---|---|---|
| Organization | OpenAI | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Oct 2025 |
| Context Window | 1.1M | 256K |
| Parameters | undisclosed | 8.8B |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $0.117 |
| Output $/1M | $10.00 | $0.455 |
| Vision Tasks | ||
| Captioning | Demo | |
| Chart Question Answering | ||
| Classification | ||
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | ||
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 80.7% | Not evaluated |
| Avg cost / sample | $0.0065 | – |
| Avg speed / sample | 8.15s | – |
| By task | ||
| Object Detection (low) | 73.6% ±0.6, Mean of 3 runs, range 72.9 to 74.2 | – |
| Object Detection (high) | 75.2% ±0.9, Mean of 3 runs, range 74.1 to 75.9 | – |
| Counting (low) | 74.8% ±2.7, Mean of 3 runs, range 71.6 to 77.0 | – |
| Counting (high) | 76.1% ±3.4, Mean of 3 runs, range 71.6 to 78.4 | – |
| Identification (low) | 91.7% ±3.1, Mean of 3 runs, range 87.5 to 93.8 | – |
| Identification (high) | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 | – |
| OCR (low) | 91.7% ±0.4, Mean of 3 runs, range 91.3 to 92.1 | – |
| OCR (high) | 91.9% ±0.3, Mean of 3 runs, range 91.6 to 92.2 | – |
| Data Extraction (low) | 80.4% ±0.0, Mean of 3 runs, range 80.4 to 80.4 | – |
| Data Extraction (high) | 82.5% ±1.0, Mean of 3 runs, range 81.4 to 83.5 | – |
| Reasoning (low) | 72.2% ±1.7, Mean of 3 runs, range 70.9 to 74.2 | – |
| Reasoning (high) | 77.9% ±2.6, Mean of 3 runs, range 75.5 to 80.8 | – |
GPT-6 Sol vs Qwen3 VL 8B Instruct: Overview
GPT-6 Sol is a proprietary multimodal reasoning model from OpenAI, released on September 22, 2026 alongside GPT-6 Luna as an efficiency-oriented tier of the GPT-6 family that began with GPT-6 Astra. OpenAI states that Sol and Luna are trained with methods similar to those used for Astra, carrying the same work on professional tasks, factuality, coding, computer use, and alignment into models that run faster. Sol accepts text and image input and returns text output, and OpenAI documents a context window of roughly one million tokens together with a knowledge cutoff of April 20, 2026.
The model targets complex coding and agentic workflows and exposes a configurable reasoning effort setting with levels of none, low, medium, high, xhigh, and max, which trades latency and token consumption against answer quality. OpenAI reports results including 33.2% on AutomationBench at xhigh effort and 56.4% on Agents' Last Exam at max effort, while its reported DeepSWE and OSWorld 2.0 figures of 68.8% and 64.4% fall below those of the earlier GPT-5.6 Sol. Its vision behavior covers image understanding tasks such as visual question answering, captioning, document and chart interpretation, and text recognition.
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.
GPT-6 Sol 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.