GPT-5 Mini vs Qwen3.5 122B A10B
We don't benchmark GPT-5 Mini or Qwen3.5 122B A10B on Vision Evals. GPT-5 Mini is proprietary under Proprietary: commercial use per vendor terms. Qwen3.5 122B A10B is open-weight under Apache 2.0: free for commercial use. GPT-5 Mini is cheaper per token ($0.25/M vs $0.26/M input). Test them side by side on your own images below.
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GPT-5 Mini vs Qwen3.5 122B A10B Comparison Table
Pricing updated November 10, 2026
| Property | GPT-5 Mini | Qwen3.5 122B A10B |
|---|---|---|
| Organization | OpenAI | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Aug 2025 | Feb 2026 |
| Context Window | 400K | 256K |
| Parameters | Unknown | 122B |
| License | Proprietary | Apache 2.0 |
| Commercial use | commercial use per vendor terms | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | $0.250 | $0.260 |
| Output $/1M | $2.00 | $2.08 |
| Vision Tasks | ||
| Classification | Demo | Supported |
| Object Detection | Demo | Supported |
| Model Features | ||
Both support 3 more features | ||
GPT-5 Mini vs Qwen3.5 122B A10B: Overview
GPT-5 Mini, released by OpenAI on August 7, 2025, is a mid-tier variant of the GPT-5 family that balances cost, speed, and capability. It is multimodal, supporting both text and image inputs, and offers a substantial input context window of ~400,000 tokens with output lengths up to ~128,000 tokens. While less powerful than the full GPT-5, it inherits its safety tuning, instruction-following improvements, and multimodal reasoning, making it a practical choice for developers who need large context handling without the expense of premium models.
GPT-5 Mini is optimized for affordability while retaining strong reasoning performance. Benchmarks show it outperforming earlier models such as GPT-4o on many multimodal and medical VQA tasks, though it lags behind GPT-5 on the most complex problems. Ideal use cases include prototyping, scalable content generation, document analysis, and mid-range reasoning tasks where efficiency and context capacity matter more than top-tier accuracy.
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.