OpenAI GPT-5.2 chat model for advanced reasoning, text & image input, 99.0 AA Math score, accessed via OrcaRouter.
openai/gpt-5.2-chat-latest is a version of OpenAI's GPT model series that focuses on chat-optimized performance with extended output capacity. It accepts text and image inputs, enabling multimodal…
The model’s standout capability is its strong performance on mathematical reasoning, as indicated by its AA Math benchmark score of 99.0. It can understand and generate complex mathematical derivations, solve equations, and reason about abstract problems. Additionally, it processes both text and image inputs, allowing it to analyze diagrams, charts, and photographs alongside text. The large output limit of 16,384 tokens enables the model to produce comprehensive explanations, multi-step solutions, or extended dialogues. It retains conversational context over long interactions, though the exact context window is not specified in provided information.
You should choose openai/gpt-5.2-chat-latest when your task demands high accuracy in reasoning, especially for math or logic-heavy domains. If your workflow involves interpreting images that contain numerical data or diagrams, this model’s multimodal capabilities add value. It is also preferable when the answer requires a long output (close to 16,384 tokens) or when you need to minimize errors in complex, multi-step processes. For simple tasks like summarization, translation, or casual conversation, a less expensive model (e.g., GPT-4o mini or Claude Haiku) may be sufficient and more cost-effective.
openai/gpt-5.2-chat-latest can accept images as input along with text. Common use cases include: extracting information from scanned documents, solving geometry problems from diagrams, interpreting graphs and plots, describing images, and performing visual question answering. The model integrates image understanding with textual reasoning, allowing it to, for example, read a chart and compute trends. However, for tasks requiring extremely high-resolution image analysis (e.g., medical imaging), specialized vision models may be more appropriate. The exact limits on image file size or resolution are not provided here.
While openai/gpt-5.2-chat-latest excels at mathematical reasoning, its performance on other benchmarks (e.g., general knowledge, coding, reasoning) is not specified in the provided facts. Users should evaluate it against their specific needs. The model does not support audio or video inputs. The output is capped at 16,384 tokens, which may be limiting for extremely long document generation. Additionally, since the context window is not disclosed, it may not be suitable for tasks requiring very long context retention. As with all language models, it can produce plausible but incorrect information, so verification is recommended.
The AA Math benchmark (Advanced Arithmetic and Algebra) assesses a model’s ability to solve mathematical problems at a high school to early college level. A score of 99.0 indicates that openai/gpt-5.2-chat-latest solved 99% of the problems correctly, placing it among the top-performing models for mathematical reasoning. This is relevant for applications where accuracy in math is critical, such as automated grading, tutoring, or scientific computation. However, this single benchmark does not reflect performance in other areas like creative writing, code generation, or common sense reasoning.
Latency for openai/gpt-5.2-chat-latest is not explicitly provided in the available facts. In general, latency depends on the input length, output length, and current load on OpenAI’s infrastructure. On OrcaRouter, requests are routed to the provider, and response times are similar to using OpenAI directly. Users should expect longer latency for larger outputs (up to 16,384 tokens) and image inputs, as processing images adds computational overhead. For real-time applications, consider using smaller models or shorter output limits to reduce wait times.
The model’s strength is its outstanding mathematical reasoning ability (99.0 AA Math). It also handles multimodal inputs and produces long outputs. However, without additional benchmark scores, we cannot compare its performance in areas like coding (e.g., HumanEval), language understanding (e.g., MMLU), or translation. It may be less capable than specialized models in those domains. Additionally, the model’s behavior on adversarial or ambiguous prompts is not documented here. Users should test the model thoroughly on their own datasets before deployment.
Provided facts only include the AA Math score of 99.0. For context, top models like o1 and GPT-4o have also shown high scores on math benchmarks, but direct comparisons are not possible without their AA Math scores. The model is likely in the top tier for math reasoning. However, models like Claude Opus may excel at creative writing, and Gemini may offer better multimodal integration. The lack of a context window figure makes it hard to compare on long-context tasks. Users should consult third-party leaderboards for a broader comparison.
Pricing is based on token usage, billed at OpenAI's provider rate with zero markup on OrcaRouter. Input tokens cost $1.75 per 1 million tokens. Output tokens cost $14.00 per 1 million tokens. Both input and output are counted separately. Image input tokens are typically calculated based on image resolution; consult OpenAI’s documentation for exact tokenization. There are no additional fees for using OrcaRouter – you pay the provider rate directly. Payments can be made via the OrcaRouter platform.
At $1.75/1M input and $14/1M output, this model is more expensive than lightweight models like GPT-4o mini ($0.15/$0.60 per 1M) but cheaper than some premium models like o1 ($15/$60). The cost trade-off depends on usage volume. For high-accuracy math tasks, the higher cost may be justified by reduced errors and rework. For simple tasks, a cheaper model saves money. Also, note that output tokens are 8x more expensive than input tokens, so optimizing output length (e.g., using max_tokens) can significantly reduce costs.
The provided facts do not mention any caching features for openai/gpt-5.2-chat-latest on OrcaRouter. However, OrcaRouter’s platform may support other cost-saving mechanisms such as usage monitoring and budget alerts. Users can also implement client-side caching of frequent responses. Since OrcaRouter passes through provider pricing without markup, the only cost savings come from selecting the right model for each request and limiting token consumption. For custom pricing or contracts, contact OrcaRouter directly.
Image inputs are converted into tokens by OpenAI’s API. The cost depends on the image resolution and detail level. Standard details: a 512x512 image incurs 85 tokens per image (plus 170 tokens for text if using low-res). High-resolution images are first scaled to 2048x2048 and then split into 512x512 tiles, each costing 170 tokens. Actual costs vary. On OrcaRouter, these tokens are billed at the same input rate of $1.75 per 1M tokens. Always refer to OpenAI’s documentation for the exact token calculation formula to estimate costs accurately.
Use the OpenAI-compatible client library (e.g., Python openai package) and set the base URL to https://api.orcarouter.ai/v1. Set the model parameter to "openai/gpt-5.2-chat-latest". Authenticate with your OrcaRouter API key. Example in Python: client = OpenAI(base_url="https://api.orcarouter.ai/v1", api_key="YOUR_KEY") response = client.chat.completions.create(model="openai/gpt-5.2-chat-latest", messages=[{"role": "user", "content": "What is the derivative of x^2?"}]) The response format matches OpenAI’s standard – a completion object with choices. You can also include image content in the messages array using OpenAI’s format for multimodal content.
All standard OpenAI chat completion parameters are supported: model, messages, max_tokens, temperature, top_p, frequency_penalty, presence_penalty, stop, etc. For this model, max_tokens can be up to 16384. temperature is recommended between 0 and 2 for creative tasks; lower values (0-0.3) for deterministic math. For image inputs, include a message with "role": "user" and content as an array of text and image parts. Refer to OpenAI’s documentation for detailed parameter descriptions. OrcaRouter passes these parameters to OpenAI unchanged.
Migration involves changing the base URL and API key. Instead of https://api.openai.com/v1, use https://api.orcarouter.ai/v1. Replace your OpenAI API key with an OrcaRouter API key. Keep the model ID as "openai/gpt-5.2-chat-latest" (note the provider prefix). No code changes are needed for the request body or response processing, as OrcaRouter uses the identical format. Test with a small request first. OrcaRouter may also offer additional features like usage analytics and cost tracking in its dashboard.
Rate limits for openai/gpt-5.2-chat-latest on OrcaRouter are not specified in the provided facts. They likely depend on your OrcaRouter plan and OpenAI’s capacity. Common HTTP status codes: 200 (success), 400 (bad request), 401 (authentication error), 429 (rate exceeded), 500 (server error). For error handling, implement retries with exponential backoff for transient errors (429, 500). Monitor token usage to avoid unexpected costs. OrcaRouter’s API may return detailed error messages in the response body for debugging.
GPT-4o is a strong multimodal model with broad capabilities, but the provided facts do not include its AA Math score for direct comparison. GPT-4o’s pricing is $5.00/1M input and $15.00/1M output, making openai/gpt-5.2-chat-latest cheaper for input ($1.75) and similar for output ($14 vs $15). GPT-4o supports up to 128K context, while this model’s context window is not specified. For math-specific tasks, the 99.0 AA Math score suggests better performance than GPT-4o’s typical math results, but broader evaluation is needed.
o1 is a reasoning-focused model with deliberate step-by-step thinking. Its pricing is much higher: $15/1M input and $60/1M output. o1 also scores highly on math (e.g., AIME 2024 at 74% for o1-preview, but no AA Math score given). openai/gpt-5.2-chat-latest is likely faster and cheaper, but o1 may offer better performance on extremely hard reasoning problems due to its internal chain-of-thought. For typical math problems, this model may be sufficient at a fraction of the cost.
Claude 3.5 Sonnet is a strong all-rounder with pricing $3.00/1M input and $15.00/1M output. It has a 200K context window. Claude’s math performance is good but not benchmarked here. openai/gpt-5.2-chat-latest has a higher output token limit (16,384 vs 8,192 for Sonnet? Actually Sonnet outputs up to 8,192). For multimodal reasoning, both accept images. The choice may depend on specific test performance and ecosystem preferences. Claude is known for safety and creative writing, while this model emphasizes mathematical accuracy.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1from openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key="$ORCAROUTER_API_KEY",
)
response = client.chat.completions.create(
model="openai/gpt-5.2-chat-latest",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)frequency_penaltylogit_biaslogprobsmax_tokensparallel_tool_callspresence_penaltypredictionresponse_formatseedservice_tierstopstreamstructured_outputstemperaturetool_choicetoolstop_logprobstop_p| Input / 1M tokens | $1.75 |
| Output / 1M tokens | $14.00 |
| Cache read / 1M | $0.175 |
| Currency | USD |
Estimate based on list price
Estimate only — actual token counts depend on the provider's tokenizer.
GET /api/public/models/openai/gpt-5.2-chat-latestOpen @misc{orcarouter_gpt_5_2_chat_latest,
title = {openai/gpt-5.2-chat-latest API},
author = {openai},
year = {n.d.},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/openai/gpt-5.2-chat-latest}
}openai. (n.d.). openai/gpt-5.2-chat-latest API. OrcaRouter. https://www.orcarouter.ai/models/openai/gpt-5.2-chat-latest