GPT-5.5 vs Qwen3 VL 235B A22B Instruct
Compare GPT-5.5 and Qwen3 VL 235B A22B Instruct side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, and OCR.
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GPT-5.5 vs Qwen3 VL 235B A22B Instruct on Vision Evals
GPT-5.5 scores higher on 4 of the six Vision Evals tasks.
The widest gap is Reasoning, where GPT-5.5 leads 82.6% to 43.5%.
Overall, GPT-5.5 averages 71.8% (#8 of 16) against 66.4% (#10 of 16) for Qwen3 VL 235B A22B Instruct.
Qwen3 VL 235B A22B Instruct is both cheaper ($0.0007 vs $0.021 per sample) and faster (8.2s vs 10.4s per sample).
GPT-5.5 vs Qwen3 VL 235B A22B Instruct Comparison Table
Evals updated July 10, 2026Pricing updated July 20, 2026
| Property | GPT-5.5 | Qwen3 VL 235B A22B Instruct |
|---|---|---|
| Organization | OpenAI | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Apr 2026 | Sep 2025 |
| Context Window | 1.0M | 256K |
| Parameters | 235B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $5.00 | $0.210 |
| Output $/1M | $30.00 | $1.90 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Object Detection | Demo | |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Classification | Demo | |
| Model Features | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks | ||
| Overall | 71.8% | 66.4% |
| Object Detection | 13.8% | 42.3% |
| Counting | 64.9% | 47.3% |
| Identification | 90.6% | 90.6% |
| OCR | 91.2% | 88.1% |
| Data Extraction | 87.6% | 86.6% |
| Reasoning | 82.6% | 43.5% |
| Avg cost / sample | $0.021 | $0.0007 |
| Avg speed / sample | 10.4s | 8.2s |
GPT-5.5 vs Qwen3 VL 235B A22B Instruct: Overview
GPT-5.5 is a multimodal large language model released by OpenAI on April 23, 2026, engineered for autonomous, multi-step knowledge work and agentic workflows. It accepts text, images, and code as input, featuring enhanced spatial reasoning and visual grounding to support its computer use capabilities for operating software and navigating UI elements. Built to execute complex workflows end-to-end, the model interprets loosely defined tasks, selects appropriate tools, and performs self-verification with minimal user intervention. It is available in a standard version, a Thinking mode for extended reasoning budgets, and a Pro variant that uses parallel test-time compute for maximum precision on complex tasks.
Co-optimized with NVIDIA for GB200 NVL72 infrastructure, GPT-5.5 delivers per-token latency comparable to its predecessor GPT-5.4 while maintaining a 1-million-token context window. Despite increased capability, the model achieves greater token efficiency in coding and data analysis workflows, often completing tasks with fewer total tokens than previous versions. OpenAI reports a 60% reduction in hallucination rate compared to GPT-5.4, improving reliability for accuracy-sensitive applications. API access is available via the Responses and Chat Completions endpoints at $5 per million input tokens and $30 per million output tokens, double the unit price of GPT-5.4.
Qwen3 VL 235B A22B Instruct is a flagship multimodal vision-language model developed by Qwen (Alibaba Cloud), designed for instruction-following tasks that combine advanced text generation with visual understanding. It serves as a high-end open-weight model for developers and researchers building multimodal AI systems that require strong reasoning, perception, and long-context capabilities.
The model supports interleaved text and image inputs, very long context windows (up to roughly 256K tokens), and efficient inference through a mixture-of-experts architecture with about 22B active parameters out of 235B total. In today’s landscape, it competes with top-tier proprietary vision-language models while offering the advantages of open weights and flexible deployment. Typical applications include multimodal assistants, document and image analysis, visual reasoning, and large-context instruction-based workflows.