GPT-5.5 vs Qwen3.5 397B A17B
GPT-5.5: 66.0% average across five Vision Evals tasks (#27 of 65 models). We have not run Qwen3.5 397B A17B through Roboflow's Vision Evals, so there is no head-to-head score. Run both on your own image below.
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GPT-5.5 vs Qwen3.5 397B A17B Comparison Table
Evals updated October 26, 2026Pricing updated November 4, 2026
| Property | GPT-5.5 | Qwen3.5 397B A17B |
|---|---|---|
| Organization | OpenAI | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Apr 2026 | Feb 2026 |
| Context Window | 1.0M | 262K |
| Parameters | Unknown | 397B |
| License | Proprietary | Apache 2.0 |
| Commercial use | commercial use per vendor terms | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | $5.00 | $0.500 |
| Output $/1M | $30.00 | $3.60 |
| Vision Tasks | ||
| Classification | Demo | Supported |
| Object Detection | Demo | Supported |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 66.0% | Not evaluated |
| Avg cost / sample | $0.025 | – |
| Avg speed / sample | 10.10s | – |
| 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) | 58.4% | – |
| by category |
| |
| OCR (high) | 60.6% | – |
| by category |
| |
| 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 397B A17B: 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.
Qwen3.5-397B-A17B is a 397B-parameter (17B active) open-weight multimodal model developed by Alibaba’s Qwen team, released on 2026-02-16 under Apache-2.0. It supports text and image inputs with text outputs, combining a sparse Mixture-of-Experts architecture with Gated Delta Networks for efficient scaling. The model provides native vision-language reasoning and a large ~262K token context window, extendable to ~1M tokens.
As the first open-weight release in the Qwen3.5 family, it positions itself as a high-capacity, long-context alternative in the large vision-language space, balancing scale and efficiency via sparse activation. It is designed for advanced reasoning, coding, agent workflows, and multimodal understanding tasks.