Qwen3.5 122B A10B vs Qwen3.6 Plus
Compare Qwen3.5 122B A10B and Qwen3.6 Plus side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.
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Qwen3.5 122B A10B vs Qwen3.6 Plus: Overview
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.
Qwen3.6 Plus is a flagship model in Alibaba’s Qwen Plus series, designed for agentic workflows, coding, and multi-step reasoning. It supports a 1 million token context window and up to 65,536 output tokens, with built-in reasoning capabilities. The model is available as a hosted, proprietary API through Alibaba Cloud.
Compared to Qwen3.5, it improves reliability in multi-step execution and frontend code generation, with stronger performance on agentic coding tasks. It also supports document and image understanding, though its vision capabilities are more limited than dedicated Qwen-VL models. Qwen3.6 Plus is part of a broader Qwen ecosystem that includes both closed-source APIs and open-weight models.
Qwen3.5 122B A10B vs Qwen3.6 Plus Comparison Table
| Property | Qwen3.5 122B A10B | Qwen3.6 Plus |
|---|---|---|
| Organization | Qwen | Qwen |
| Category | open | closed |
| Modality | multimodal | multimodal |
| Release Date | Feb 2026 | Apr 2026 |
| Context Window | 256K | 1.0M |
| Parameters | 122B | |
| License | Apache 2.0 | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.260 | $0.325 |
| Output $/1M | $2.08 | $1.95 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Object Detection | ||
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Overall Score | 76.12% | 68.66% |
| Avg Response Time | 1.77s | 34.17s |
| Median input tokensincl. image tokens | 1.2K | 1.2K |
| Median output tokens | 7 | 47 |
| Est. cost / taskon this benchmark | $0.0003 | $0.0005 |
| Defect Detection | 86.7%(13/15) | 86.7%(13/15) |
| Document Understanding | 77.8%(7/9) | 77.8%(7/9) |
| Object Counting | 40%(4/10) | 20%(2/10) |
| Object Understanding | 92.9%(13/14) | 78.6%(11/14) |
| Spatial Understanding | 73.7%(14/19) | 68.4%(13/19) |
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