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 at $5 per million input tokens and $30 per million output tokens, double the unit price of GPT-5.4.
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Past 30 DaysVision Evals is Roboflow's ground-truth benchmark: every model runs the same real-world samples across six vision tasks, and answers are scored against ground truth.
Evals updated July 10, 2026Pricing updated July 21, 2026
GPT-5.5 averages 71.8% across the six Vision Evals tasks, ranking #8 of 16 models overall.
Its weakest relative showing is Object Detection, ranking #15 of 16 at 13.8%.
At $0.021 per sample it is the 14th cheapest of the 16 benchmarked models, and its average inference time of 10.4s per sample makes it the 16th fastest.
Field medians: Object Detection 41.5%, Counting 62.2%, Identification 84.4%, OCR 89.1%, Data Extraction 85.6%, Reasoning 76.1%.
| Task | Score | Field (0 to 100) | Rank | Cost / sample | Speed |
|---|---|---|---|---|---|
| Object Detection | 13.8% | #15 of 16 | $0.029 | 14.1s | |
| Counting | 64.9% | #7 of 16 | $0.015 | 10.0s | |
| Identification | 90.6% | #6 of 16 | $0.0085 | 4.2s | |
| OCR | 91.2% | #5 of 16 | $0.023 | 9.5s | |
| Data Extraction | 87.6% | #6 of 16 | $0.010 | 4.7s | |
| Reasoning | 82.6% | #5 of 16 | $0.012 | 5.8s |
Overall benchmark score against estimated cost per sample. Upper-left is the sweet spot: high quality at low cost.
16 models on the current benchmark · scores and efficiency pooled across all six tasks · GPT-5.5 highlighted
GPT-5.5 scores from a single evaluation run · Methodology
View all Vision Evals →GPT-5.5 costs $5.00 per 1M input tokens and $30.00 per 1M output tokens.
Pricing updated Jul 21, 2026
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License terms and commercial-use guidance for GPT-5.5.
This model is proprietary. The author retains all rights, and use of the model is governed by their specific terms of service or license agreement.
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Yes. GPT-5.5 accepts image input and handles OCR, data extraction, object counting, identification, visual reasoning, and object detection. On Roboflow's Vision Evals its strongest task is OCR at 91.2% (#5 of 16). You can test it on your own image in the demo above.
Yes, and it is one of the model's strongest vision skills: its transcriptions match the ground truth 91.2% on average (#5 of 16) on Vision Evals OCR. Pulling specific fields out of documents (data extraction) scores 87.6%.
Not its strength. On Vision Evals, GPT-5.5 scores 13.8% mAP@50 on object detection (#15 of 16) and 64.9% exact-match accuracy on object counting. For production counting or precise localization, pairing it with a specialized detector like RF-DETR or your own trained model in a Roboflow Workflow is usually more reliable: detect the objects, then count the detections.
On our benchmark's task mix, GPT-5.5 averages $0.02 per sample at $5.00 per 1M input and $30.00 per 1M output tokens (#14 of 16 on cost), with an average speed of 10.4s per sample across the benchmark. Actual cost depends on your images and prompts.
On the overall Vision Evals ranking, GPT-5.5 sits #8 of 16 at 71.8%, just behind GPT-5.6 Terra (73.5%) and just ahead of Gemini 2.5 Pro (67.9%). See the full side-by-side: GPT-5.5 vs GPT-5.6 Terra.