GPT-5.2-Codex is an upgraded version of GPT-5.1-Codex optimized for software engineering and coding workflows. It is designed for both interactive development sessions and long, independent execution of complex engineering tasks....
OpenAI GPT-5.2-Codex is a variant of the GPT-5.2 model that has been fine-tuned for code-centric tasks. It supports text and image inputs, processes up to 400,000 tokens of context, and can generate…
GPT-5.2-Codex can generate code in dozens of programming languages, write unit tests, refactor existing code, translate between languages, explain code intent, and suggest bug fixes. Its 400K token context allows it to consider entire files, modules, or even an entire codebase in a single request. The model can also process image inputs, such as architecture diagrams or handwritten logic, and combine those with textual prompts to produce code that matches visual designs. For tasks requiring very long outputs, it can emit up to 128,000 tokens, enough for multi-file codebases or exhaustive documentation.
If your task does not require the extended context or code-specific tuning, a general-purpose model may be more economical. For simple text generation, summarization, or classification tasks, the specialized code focus of GPT-5.2-Codex offers no benefit, and the provider rate of $14.00 per 1M output tokens may be unnecessarily high. Additionally, if you need faster response times for real-time applications, a smaller model with lower latency might be preferable, as GPT-5.2-Codex is a large model optimized for accuracy rather than speed.
Yes, GPT-5.2-Codex accepts both text and image inputs. This allows you to include screenshots of code editors, error messages, whiteboard diagrams, or UI mockups as part of your prompt. The model will interpret the visual content and generate code or textual responses that align with the given imagery. This multimodal capability is especially useful for generating code from wireframes or debugging issues shown in screenshots. Note that image processing counts against the context window; each image consumes tokens proportional to its size, reducing the available capacity for other content.
τ²-Bench is a benchmark designed to measure a model's ability to generate code that is correct, efficient, and well-structured across a variety of programming tasks. A score of 92.1 indicates that GPT-5.2-Codex performs at a high level of competence, particularly in tasks that require understanding specifications, handling edge cases, and producing executable code. This benchmark evaluates both one-shot code generation and iterative debugging scenarios. While the exact methodology of τ²-Bench is not publicly detailed, the score serves as a comparative reference point for code-focused models.
Explicit latency figures for GPT-5.2-Codex are not provided in the given facts. However, as a large model with a 400K-token context and 128K-token output, inference time will be longer than smaller models, especially when processing near-maximum context lengths. Users should expect higher latency for complex prompts that fully utilize the context window. For interactive coding assistance, it may be beneficial to limit context size or use streaming responses to improve perceived speed. OrcaRouter's API supports streaming to return tokens as they are generated.
Based on the provided benchmark score of 92.1 on τ²-Bench, GPT-5.2-Codex demonstrates strong performance in code generation and debugging tasks. Its large context window allows it to retain and reason about long code fragments, which is critical for tasks like refactoring multi-file projects or understanding complex dependencies. The ability to accept image inputs further extends its usefulness in workflows that combine visual and textual information. These strengths make it suitable for professional software development where accuracy and context length are priorities.
Like all large language models, GPT-5.2-Codex can produce incorrect or insecure code, hallucinate nonexistent library functions, and be sensitive to prompt phrasing. It may also struggle with tasks that require real-time knowledge or proprietary APIs not present in its training data. The 400K context window, while large, is still finite; extremely large codebases may not fit entirely, requiring chunking or summarization strategies. Additionally, the model's cost per output token is high compared to smaller models, making it less economical for simple or repetitive code tasks.
Pricing is billed at the provider rate with zero markup. Input tokens cost $1.75 per 1 million tokens, and output tokens cost $14.00 per 1 million tokens. There are no additional fees from OrcaRouter. For a typical request with 10,000 input tokens and 2,000 output tokens, the cost would be $0.0175 for input and $0.028 for output, totaling about $0.0455 per request. Pricing does not vary by usage tier or region – it is a flat per-token rate. Caching may reduce input costs if the same prompt is reused; check OrcaRouter's documentation for caching policies.
Because output tokens are eight times more expensive than input tokens ($14.00 vs $1.75), short outputs are relatively more cost-efficient. To manage costs, limit the number of output tokens by setting the `max_tokens` parameter appropriately. For tasks that require long outputs (e.g., generating entire codebases), consider breaking the work into smaller pieces to avoid hitting the 128K max output limit and to keep costs predictable. Using image inputs also incurs token charges based on the image size, which can add to the overall bill.
While the provided facts do not specify OrcaRouter's caching policies, many API gateways implement caching for repeated prompts to reduce input token charges. You should consult OrcaRouter's documentation or support to determine whether prompt caching is available for model ID "openai/gpt-5.2-codex". If caching is supported, identical prompt prefixes may be billed at a lower rate, significantly lowering costs for applications that reuse system messages or large context blocks.
You access the model through OrcaRouter's OpenAI-compatible API at base URL `https://api.orcarouter.ai/v1`. Use the model ID `"openai/gpt-5.2-codex"` in your requests. The API follows the standard chat completions format. You can pass `model: "openai/gpt-5.2-codex"` in your request body. All parameters supported by OpenAI's chat completions endpoint are available, including `messages`, `max_tokens`, `temperature`, `top_p`, `stream`, and `stop`. For image inputs, use the `content` array with `type: "image_url"` as specified in the OpenAI vision API.
You can set `max_tokens` up to 128,000 tokens. Use `temperature` (0.0–2.0) to control randomness; for code generation, values around 0.2–0.4 are common. `top_p` provides nucleus sampling. `frequency_penalty` and `presence_penalty` modify token selection. The `stop` parameter accepts up to 4 sequences. Streaming via `stream: true` is supported to receive tokens incrementally. For reproducible outputs, set `seed` to an integer. Note that large context prompts may increase processing time; consider reducing context or using streaming for better user experience.
To migrate, change your base URL from `https://api.openai.com/v1` to `https://api.orcarouter.ai/v1` and use model ID `"openai/gpt-5.2-codex"` instead of an OpenAI-specific model name. Your existing OpenAI client library code will work with minimal changes. OrcaRouter passes through requests transparently and does not alter the API contract. Ensure your authentication uses an OrcaRouter API key. For non-code tasks, update your model IDs accordingly. If you were using a different OpenAI model, you can still access it through OrcaRouter by using the appropriate model ID.
Yes, the API accepts any chat completions request, so you can use it for general-purpose tasks. However, because the model is fine-tuned for code, it may not perform as well as a general model on creative writing or casual conversation. It can still produce helpful text summaries, especially of technical content. For non-code tasks, you may be paying a premium for capabilities you do not need. Consider using a cheaper general-purpose model available on OrcaRouter for such use cases.
Without published benchmarks for GPT-4o-Code on τ²-Bench, a direct numeric comparison is not possible. However, GPT-5.2-Codex offers a larger context window (400K vs. GPT-4o's typical 128K) and higher maximum output (128K vs. 16K for GPT-4o). The score of 92.1 on τ²-Bench suggests strong code generation abilities, but GPT-4o-Code may have different strengths. In practice, the trade-off often comes down to context size requirements and cost: GPT-5.2-Codex is more expensive per output token, but may provide better results for complex, context-heavy tasks.
Claude Codex by Anthropic also targets code generation, but its specific benchmark scores on τ²-Bench are not provided for comparison. Context window sizes for Claude models vary; as of known public information, Claude 3 Opus supports 200K tokens. GPT-5.2-Codex's 400K context is larger, which can be advantageous for very long codebases. Pricing for Claude Codex may differ; GPT-5.2-Codex's provider rate of $14 per 1M output tokens is competitive with high-end code models. Users should evaluate based on actual task performance and required context length.
For simpler code tasks that do not need the full 400K context or the specialized tuning of GPT-5.2-Codex, smaller models like GPT-4o mini or Llama 3 8B may be adequate and significantly cheaper. OrcaRouter offers many such models at varying price points. The τ²-Bench score of 92.1 indicates high accuracy, but for routine snippet generation or syntax completion, a lower-cost model may meet your needs at a fraction of the price. Always evaluate the complexity of your code task against the model's strength and cost.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1from openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key="$ORCAROUTER_API_KEY",
)
response = client.chat.completions.create(
model="openai/gpt-5.2-codex",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)include_reasoningmax_completion_tokensmax_tokensreasoningresponse_formatseedstreamstructured_outputstool_choicetools| 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-codexOpen @misc{orcarouter_gpt_5_2_codex,
title = {GPT-5.2-Codex API},
author = {OpenAI},
year = {2026},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/openai/gpt-5.2-codex}
}OpenAI. (2026). GPT-5.2-Codex API. OrcaRouter. https://www.orcarouter.ai/models/openai/gpt-5.2-codex