GPT-5.5 vs Qwen3.6 27B
Qwen3.6 27B averages 67.1% across four Vision Evals tasks and GPT-5.5 66.0% across five (#28 and #29 of 67 models), so Qwen3.6 27B leads overall.
- GPT-5.5 wins 3 of the 4 tasks both ran, but the widest gap is object detection, where Qwen3.6 27B leads 59.7% vs 43.6%.
- Qwen3.6 27B is under a tenth of the cost: $0.0021 vs $0.025 overall average cost per sample.
- GPT-5.5 is faster: 10.1s vs 42.1s per sample.
Roboflow Vision Evals. Full breakdown below.
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GPT-5.5 vs Qwen3.6 27B on Vision Evals
Roboflow Vision Evals. Full breakdown below.
GPT-5.5 vs Qwen3.6 27B Comparison Table
Evals updated November 10, 2026Pricing updated November 10, 2026
| Property | GPT-5.5 | Qwen3.6 27B |
|---|---|---|
| Organization | OpenAI | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Apr 2026 | Apr 2026 |
| Context Window | 1.0M | 262K |
| Parameters | Unknown | 27B |
| License | Proprietary | Apache 2.0 |
| Commercial use | commercial use per vendor terms | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | $5.00 | $0.320 |
| Output $/1M | $30.00 | $3.20 |
| Vision Tasks | ||
| Classification | Demo | Supported |
| Object Detection | Demo | Not listed |
| Video Classification | Not listed | Supported |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 66.0% | 67.1%* |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.025 | $0.0021 |
| Avg speed / sample | 10.10s | 42.09s |
| By task | ||
| Object Detection (low) | 43.6% ±2.2, Mean of 3 runs, range 41.7 to 46.1 | 59.7% ±0.9, Mean of 3 runs, range 59.0 to 60.8 |
| 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 | 67.1% ±4.7, Mean of 3 runs, range 62.2 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 | 82.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 |
| 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 | 59.2% ±1.7, Mean of 3 runs, range 57.6 to 60.9 |
| Reasoning (high) | 72.2% ±3.6, Mean of 3 runs, range 68.9 to 76.2 | – |
GPT-5.5 vs Qwen3.6 27B: 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.
Qwen3.6-27B is a dense 27-billion-parameter multimodal language model developed by Alibaba's Qwen team and released on April 22, 2026. It combines a causal language model with an integrated vision encoder, supporting text, image, and video inputs natively. The architecture employs a hybrid attention design that interleaves Gated DeltaNet linear attention blocks with standard Gated Attention layers across 64 transformer layers with a hidden dimension of 5,120. Unlike Mixture-of-Experts variants in the Qwen3.6 family, all 27 billion parameters are active on every inference pass, simplifying deployment and quantization. The model supports a native context window of 262,144 tokens, extensible to approximately 1,010,000 tokens via YaRN scaling. It is released under the Apache 2.0 license with open weights available on Hugging Face and ModelScope.
The model introduces two notable capabilities relative to prior Qwen releases: enhanced agentic coding support covering frontend workflows and repository-level reasoning, and a Thinking Preservation mechanism that retains chain-of-thought reasoning context across multi-turn conversation history to reduce redundant token generation in iterative agent sessions. It supports both a thinking mode for multi-step reasoning and a non-thinking mode for faster responses within a single model. Vision capabilities include chart understanding (CharXiv RQ: 78.4), OCR (CC-OCR: 81.2), and video understanding (VideoMME with subtitles: 87.7).