Roboflow
OpenAI

OpenAI: GPT-5.2

GPT-5.2 Overview

GPT-5.2 is OpenAI’s latest flagship large language model, released in December 2025. It is a proprietary, multimodal system supporting text and vision inputs, along with tool use, and features a 400,000-token context window designed for working with long documents, extended conversations, and complex workflows.

Relative to GPT-5.1, GPT-5.2 is positioned by OpenAI as offering improved long-context reasoning, more capable tool use, and stronger performance on professional tasks such as writing, coding, spreadsheet work, and image interpretation. The model is available in multiple variants (including Instant, Thinking, and Pro) that balance speed, cost, and depth of reasoning, making GPT-5.2 a general-purpose model aimed at reliability and workflow robustness rather than minimal latency or lowest cost.

GPT-5.2 Interactive Demo

Model settings

Thinking level

Max output tokens

Default 65,536 · max 65,536

Sign in to adjust thinking and output length per run.

Results appear here. Add an image or pick an example to run GPT-5.2.

GPT-5.2 Details & Performance

Details

Resources

—

Vision Tasks

CaptioningChart Question AnsweringClassificationDocument Question AnsweringImage TaggingMulti-Label ClassificationOCRObject DetectionVision LanguageVisual Question Answering

Features

Foundation VisionLLMs with Vision CapabilitiesMultimodal Vision

Usage

Past 30 Days

Performance

Avg. Latency

Arena Rankings

GPT-5.2 Pricing

GPT-5.2 costs $1.75 per 1M input tokens and $14.00 per 1M output tokens.

Input$1.75 / 1M tokens
Output$14.00 / 1M tokens
Cached input$0.175 / 1M tokens

Pricing updated Aug 26, 2026

Alternatives to GPT-5.2

Other models worth comparing for similar use cases.

Anthropic
Claude Opus 4.8
Claude Opus 4.8 is Anthropic's most capable generally available large language model, released on May 28, 2026 as an incremental upgrade to Claude Opus 4.7. The model accepts text and image inputs and produces text outputs, with a 1 million token context window on the Claude API, Amazon Bedrock, and Google Cloud Vertex AI (200k tokens on Microsoft Foundry) and up to 128k max output tokens. It uses adaptive thinking and supports adjustable effort tiers — high by default, with extra and max tiers available for more demanding tasks. A fast mode operates at approximately 2.5x standard speed. The model is described by Anthropic as a hybrid reasoning model designed for advanced coding, agentic workflows, long-context reasoning, and professional knowledge work.Key behavioral improvements over Opus 4.7 include substantially reduced rates of unreported code flaws, improved honesty in self-assessment, and better tool-calling reliability. On Anthropic's Super-Agent benchmark, Opus 4.8 completes every case end-to-end, and it scores 84% on Online-Mind2Web for computer-use and browser-agent tasks. It achieves 88.6% on SWE-bench Verified and 69.2% on SWE-bench Pro. Alongside the model, Anthropic launched Dynamic Workflows in Claude Code (research preview), which enables Claude to orchestrate hundreds of parallel subagents for codebase-scale tasks such as large migrations. The Messages API was also updated to accept mid-task system messages without breaking prompt caching, improving support for long-running agentic pipelines.
Google
Gemini 3.1 Pro
Gemini 3.1 Pro is a proprietary multimodal model from Google’s Gemini 3 series, released in early 2026 and designed for advanced reasoning across large multimodal datasets. It accepts text, images, audio, video, and documents, supporting up to a 1-million-token input context with up to 64k output tokens. Compared with Gemini 3 Pro, it improves long-context synthesis and multi-step reasoning, enabling more reliable analysis of large documents, datasets, and software codebases.The model also advances visual understanding and grounding, allowing it to interpret UI screenshots, diagrams, and real-world scenes while referencing specific regions within images or video. These capabilities make Gemini 3.1 Pro well suited for multimodal workflows involving document processing, interface analysis, robotics research, and complex visual reasoning.
Qwen
Qwen3.5 397B A17B
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.
Grok
Grok 4.5
Grok 4.5 is a proprietary reasoning model from SpaceXAI (xAI) that accepts interleaved text and image input and returns text, with a 500,000 token context window. xAI positions it as a model for coding, agentic software work, and knowledge tasks, and states it was trained in the company's Memphis data centers on datasets spanning science, engineering, and mathematics. Its reinforcement learning stage covers hundreds of thousands of multi step software engineering tasks scored by automated checks and model based grading, and training is reported to have run on tens of thousands of NVIDIA GB300 GPUs using an asynchronous scheme in which multi hour agentic rollouts continue while learning proceeds in parallel, targeting long horizon autonomous operation rather than single turn inference.For vision, the model consumes JPEG and PNG images in any order relative to text prompts, covering visual question answering, description of chart and document imagery, and reading text rendered inside a scene. Reasoning effort is configurable, and the model supports function calling and structured outputs, so image inputs can be interleaved with tool calls inside agent loops. xAI has not published a technical report, architecture details, or parameter count, and reported mixture of experts sizing figures come from secondary coverage rather than official documentation.

Other OpenAI GPT models

Other versions in the same family as GPT-5.2.

OpenAIGPT-5.6 SolOpenAIGPT-5.5OpenAIGPT-5.4OpenAIGPT-5.1OpenAIGPT-5

Deploy GPT-5.2 with an API

GPT-5.2 runs as a hosted REST endpoint through Roboflow Workflows. Pick a task, then hand the prompt to your coding agent or copy the code. Deploying the workflow into a free Roboflow workspace replaces the your-workspace and YOUR_API_KEY placeholders with your own.

Connect your agent to Roboflow (once)

Add the Roboflow MCP server

claude mcp add --transport http roboflow https://mcp.roboflow.com/mcp

Run /mcp and authorize Roboflow in your browser when the OAuth flow opens.

Start a new Claude Code session so the MCP loads, then paste the prompt below (it works the same in any agent).

Deploy this workflow to your Roboflow workspace to use it.

Integrate the Roboflow "GPT-5.2" workflow into my app.

- Endpoint: POST https://serverless.roboflow.com/<your-workspace>/workflows/gpt-5-2-captioning
- Auth: send my Roboflow API key as `api_key` in the request body, read from the ROBOFLOW_API_KEY env var (never hardcode).
- Body: { "api_key": ..., "inputs": { `image`: { type: "url" | "base64", value } } }.
- Billing: this workflow needs no provider API key — inference runs on my Roboflow credits. A BYO provider key can be added to the model step in the Roboflow workflow editor later.

With the Roboflow MCP connected, call `workflows_get` on "gpt-5-2-captioning" to read the exact input schema (the source of truth), then `workflows_run` on a sample image to confirm the output shape before writing code (the MCP is authenticated, so this needs no key). Without the MCP, use the contract above.

Before running the app, set up these keys so it does not error at runtime:
- `ROBOFLOW_API_KEY` (sent as `api_key`) from https://app.roboflow.com/settings/api
Create a .gitignore'd .env with these variables, using placeholder values for any I haven't given you. Then pause and tell me directly, in your reply: the full path to the .env file, exactly which keys I need to paste in, and the link to get each one. Wait for me to confirm I've added them before you run anything. Do not run the app until I confirm.

Then add the integration to my codebase: match my project's language, framework, and conventions; read every key from environment variables (never hardcode); add basic error handling; and include a small runnable example. If you can't tell what language my project uses, ask me.
Installpip install inference-sdk

Deploy this workflow to your Roboflow workspace to use it.

# Inference runs on your Roboflow credits — no provider API key needed. To bill your own provider account instead, add an api_key to the model step in the Roboflow workflow editor.
# 1. Import the library
from inference_sdk import InferenceHTTPClient

# 2. Connect to your workflow
client = InferenceHTTPClient(
  api_url="https://serverless.roboflow.com",
  api_key="YOUR_API_KEY"
)

# 3. Run your workflow on an image
result = client.run_workflow(
  workspace_name="your-workspace",
  workflow_id="gpt-5-2-captioning",
  images={
    "image": "YOUR_IMAGE.jpg"  # Path to your image file
  },
  use_cache=True  # cache workflow definition for 15 minutes
)

# 4. Get your results
print(result)

Deploy this workflow to your Roboflow workspace to use it.

// Inference runs on your Roboflow credits — no provider API key needed. To bill your own provider account instead, add an api_key to the model step in the Roboflow workflow editor.
const response = await fetch('https://serverless.roboflow.com/your-workspace/workflows/gpt-5-2-captioning', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    api_key: 'YOUR_API_KEY',
    inputs: {
      "image": {"type": "url", "value": "IMAGE_URL"}
    }
  })
});

const result = await response.json();
console.log(result);

Deploy this workflow to your Roboflow workspace to use it.

# Inference runs on your Roboflow credits — no provider API key needed. To bill your own provider account instead, add an api_key to the model step in the Roboflow workflow editor.
curl --location 'https://serverless.roboflow.com/your-workspace/workflows/gpt-5-2-captioning' \
--header 'Content-Type: application/json' \
--data '{
  "api_key": "YOUR_API_KEY",
  "inputs": {
    "image": {"type": "url", "value": "IMAGE_URL"}
  }
}'

GPT-5.2 License

Proprietary

GPT-5.2 is proprietary: the weights are not distributed, and the GPT-5.2 license is the vendor's commercial terms of service that you accept when you call the API.

Commercial use
Permitted under the vendor terms, typically metered per token or per request, with the vendor usage policy applying to your inputs and outputs.
Modification
Not available. GPT-5.2 weights are closed, so you can configure prompts and use vendor-hosted fine-tuning where it is offered, but you cannot modify the model itself.
Redistribution
Not permitted. You cannot self-host or resell the model; you build on the hosted API instead.

Vendor terms govern data retention, whether your inputs can be trained on, rate limits, and regional availability, and they can change with notice. Review them if you handle regulated or customer data.

Do I need a commercial license for GPT-5.2?

Proprietary terms are set by the vendor rather than negotiated per project, and no open-source obligation attaches to your code. If you would rather deploy a model whose commercial license is included in your plan — on Roboflow Managed Cloud or a Self-Hosted Inference Server — Roboflow's licensing page lists the supported alternatives to GPT-5.2.

Do not hesitate to reach out with questions for your commercial project — our team will help you start solving business problems on the first call. See Roboflow commercial licensing for the models included in each plan.

Talk to sales

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.

Commercial use depends on the terms set by the model author. Most proprietary commercial models require a paid subscription, API key, or per-call billing. Check the provider’s pricing and terms-of-service for details.

License information is provided as a guide and is not legal advice.