GPT-5.2 vs Qwen3.5 122B A10B
Compare GPT-5.2 and Qwen3.5 122B A10B side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.
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GPT-5.2 vs Qwen3.5 122B A10B: Overview
GPT-5.2 is OpenAI’s latest flagship large language model, released in December 2025. It is a proprietary, multimodal system supporting text and vision inputs, along with tool use, and features a 400,000-token context window designed for working with long documents, extended conversations, and complex workflows.
Relative to GPT-5.1, GPT-5.2 is positioned by OpenAI as offering improved long-context reasoning, more capable tool use, and stronger performance on professional tasks such as writing, coding, spreadsheet work, and image interpretation. The model is available in multiple variants (including Instant, Thinking, and Pro) that balance speed, cost, and depth of reasoning, making GPT-5.2 a general-purpose model aimed at reliability and workflow robustness rather than minimal latency or lowest cost.
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.
GPT-5.2 vs Qwen3.5 122B A10B Comparison Table
| Property | GPT-5.2 | Qwen3.5 122B A10B |
|---|---|---|
| Organization | OpenAI | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Dec 2025 | Feb 2026 |
| Context Window | 400K | 256K |
| Parameters | 122B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $1.75 | $0.260 |
| Output $/1M | $14.00 | $2.08 |
| 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 | ||
| Foundation Vision | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Overall Score | 76.12% | |
| Avg Response Time | 1.77s | |
| Median input tokensincl. image tokens | 1.2K | |
| Median output tokens | 7 | |
| Est. cost / taskon this benchmark | $0.0003 | |
| Defect Detection | 86.7%(13/15) | |
| Document Understanding | 77.8%(7/9) | |
| Object Counting | 40%(4/10) | |
| Object Understanding | 92.9%(13/14) | |
| Spatial Understanding | 73.7%(14/19) | |
Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology