GPT-5.5 vs Qwen3.5 122B A10B
Compare GPT-5.5 and Qwen3.5 122B A10B 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 Qwen3.5 122B A10B Comparison Table
Evals updated September 22, 2026Pricing updated September 22, 2026
| Property | GPT-5.5 | Qwen3.5 122B A10B |
|---|---|---|
| Organization | OpenAI | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Apr 2026 | Feb 2026 |
| Context Window | 1.0M | 256K |
| Parameters | 122B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $5.00 | $0.260 |
| Output $/1M | $30.00 | $2.08 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 74.8% | Not evaluated |
| Avg cost / sample | $0.022 | – |
| Avg speed / sample | 9.03s | – |
| By task | ||
| Object Detection (low) | 43.6% ±2.2, Mean of 3 runs, range 41.7 to 46.1 | – |
| Object Detection (high) | 44.2% ±0.6, Mean of 3 runs, range 43.5 to 44.8 | – |
| Counting (low) | 68.0% ±3.4, Mean of 3 runs, range 64.9 to 71.6 | – |
| Counting (high) | 68.0% ±1.4, Mean of 3 runs, range 66.2 to 68.9 | – |
| Identification (low) | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 | – |
| Identification (high) | 88.5% ±1.6, Mean of 3 runs, range 87.5 to 90.6 | – |
| OCR (low) | 91.2% ±0.3, Mean of 3 runs, range 90.9 to 91.6 | – |
| OCR (high) | 91.7% ±0.6, Mean of 3 runs, range 91.1 to 92.3 | – |
| Data Extraction (low) | 85.9% ±1.5, Mean of 3 runs, range 84.5 to 87.6 | – |
| Data Extraction (high) | 86.9% ±0.5, Mean of 3 runs, range 86.6 to 87.6 | – |
| Reasoning (low) | 70.6% ±1.0, Mean of 3 runs, range 69.5 to 71.5 | – |
| Reasoning (high) | 72.2% ±3.6, Mean of 3 runs, range 68.9 to 76.2 | – |
GPT-5.5 vs Qwen3.5 122B A10B: 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.
Qwen3.5-122B-A10B is a high-capacity multimodal Mixture-of-Experts (MoE) model developed by Alibaba’s Qwen team as part of the Qwen3.5 model family. The architecture contains 122 billion total parameters while activating roughly 10 billion per token through sparse expert routing, allowing the model to balance large-scale reasoning ability with relatively efficient inference compared to dense models of similar size.
The model is designed to process both text and visual inputs within a unified multimodal framework, enabling tasks that require reasoning across images, documents, charts, and natural language. This makes it suitable for applications such as document understanding, diagram interpretation, and complex visual question answering.
Qwen3.5-122B-A10B supports a native context window of approximately 256,000 tokens, which can be extended further through techniques such as YaRN scaling to support very long-context workloads. Released under the Apache 2.0 license, it builds on earlier Qwen multimodal systems and provides developers with an open-weight model capable of handling demanding multimodal reasoning and analysis tasks.