GPT-5-Codex is a specialized version of GPT-5 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 Codex is a large language model from OpenAI that specializes in code generation and mathematical reasoning. It is an evolution of the Codex family, designed to handle complex programming…
GPT-5 Codex excels at generating, explaining, debugging, and translating code across many programming languages. Its large context window allows it to understand project-level dependencies and produce coherent multi-file solutions. The model can also generate documentation, create unit tests, and refactor existing code. Because it supports image input, it can interpret screenshots of code, whiteboard architectures, or flow diagrams. For tasks like code review, it can analyze pull requests in their entirety. However, for simple one-liner completions or trivial scripts, a smaller and cheaper model would be more cost-effective.
Yes, GPT-5 Codex accepts images as input alongside text. This allows the model to understand visual content such as diagrams, charts, screenshots of code, handwritten notes, and scanned documents. For example, a user can upload a photo of a whiteboard architecture and ask the model to generate the corresponding code. The image input is processed as part of the context, counting toward the 400K token limit (images are tokenized based on resolution). This multimodal capability extends the model’s utility to scenarios where visual context is essential, such as reverse-engineering GUI layouts or interpreting hand-drawn schematics.
GPT-5 Codex can generate up to 128,000 tokens in a single completion. This enables the model to produce very long outputs, such as a full software application, a lengthy technical report, or a detailed mathematical proof. The combined input and output must fit within the 400K token context window. When using images, note that they consume tokens from the input pool. The high output limit reduces the need to chain multiple requests for long-form content, though it may increase costs if not managed carefully. For shorter outputs, setting a lower max_tokens parameter can help control expenses.
GPT-5 Codex is a premium model with correspondingly higher per-token costs ($1.25 input, $10 output per 1M tokens). For routine code completions, simple queries, or tasks that do not require advanced reasoning, a smaller model like GPT-4 Turbo or even open-source alternatives may be more cost-effective. If your codebase is small or your math problems are elementary, the extra context and accuracy of GPT-5 Codex may not justify the expense. OrcaRouter offers access to many models; you can compare prices and capabilities to select the best fit. Use GPT-5 Codex when the complexity or stakes of the task demand its strengths.
GPT-5 Codex achieved a score of 98.7 on the AA Math benchmark, which measures advanced algebraic reasoning. This indicates near-perfect performance on problems that often challenge other models. While the exact methodology of AA Math is not detailed here, a score above 95 is generally considered state-of-the-art. The model’s training likely included extensive mathematical corpora and code that reinforces logical reasoning. Users should note that benchmark scores do not guarantee real-world performance, but they provide a useful reference for relative capability. The model was not evaluated on other standard benchmarks in the provided facts.
No specific latency figures were provided for GPT-5 Codex. As a large model with a 400K token context window and 128K output limit, inference time will generally be higher than smaller models. Latency depends on the length of both input and output, as well as the underlying hardware used by OpenAI. OrcaRouter routes requests to the provider, so network factors also play a role. For real-time applications, consider testing with partial prompts or caching common responses. Users who need low-latency responses for high-frequency calls may prefer smaller models available on OrcaRouter.
The principal strength of GPT-5 Codex is its combination of a large context window (400K tokens), high output limit (128K tokens), multimodal input (text and image), and exceptional performance on the AA Math benchmark (98.7). These features make it suitable for complex coding tasks, advanced mathematics, and scenarios where visual context is essential. The model is also likely to exhibit strong reasoning due to its training on both code and math. Its zero-markup pricing through OrcaRouter ensures that users pay only the provider rate. These strengths are most valuable in high-stakes, large-scale projects.
GPT-5 Codex’s limitations include its higher per-token cost compared to smaller models, which can add up for high-volume usage. Its large context window may also lead to slower response times, especially when processing many tokens. The model’s training data cutoff date is not provided, so it may lack knowledge of very recent events or code libraries. Additionally, while it scores highly on AA Math, performance on other benchmarks is not specified. Users should verify that the model’s behavior aligns with their specific use case. Finally, as a closed-source model, fine-tuning is not available on OrcaRouter.
GPT-5 Codex is priced at $1.25 per 1 million input tokens and $10.00 per 1 million output tokens. This is the provider rate set by OpenAI, and OrcaRouter charges zero markup, so users pay exactly that amount. Input tokens include both text and image tokens; images are tokenized based on their size and resolution. The high output price reflects the model’s ability to generate long, high-quality completions. For cost-sensitive projects, it is important to monitor token usage and set appropriate max_tokens values. There are no additional fees for API access beyond the per-token costs.
Based on the provided facts, GPT-5 Codex is priced significantly higher than most other OpenAI models. For example, GPT-4 Turbo and GPT-3.5 Turbo have lower per-token rates. However, no specific comparative prices are given here. The premium pricing reflects the larger context window, higher output limit, multimodal input, and benchmark scores. Users should evaluate whether the incremental gains in accuracy and context size justify the additional cost for their workload. OrcaRouter lists prices for each model, enabling direct comparison. For projects with tight budgets, alternatives like GPT-4 Turbo or open-source models may be more economical.
OrcaRouter’s documentation was not provided with details on caching. Generally, caching can reduce costs by storing frequent responses, but it is not confirmed for GPT-5 Codex. Users may implement their own caching layer at the application level. Another cost-saving strategy is to use shorter prompts, limit output length, and batch requests. OrcaRouter does not add markup, so the listed rates are final. For massive workloads, contact OrcaRouter about potential volume discounts. Additionally, consider whether the 400K context window is fully utilized; trimming unnecessary tokens can reduce input costs.
The main trade-off is between the model’s high accuracy and context capacity versus its cost per token. For tasks where errors are costly—such as generating production code or solving high-stakes mathematical problems—the premium price may be justified. Conversely, for exploratory work, prototyping, or simple completions, the expense may outweigh the benefits. The zero-markup pricing through OrcaRouter ensures you are not paying more than the provider rate, but the absolute cost remains high. Plan your token budgets carefully. A best practice is to first test with a smaller model and then scale up if GPT-5 Codex’s capabilities are needed.
To use GPT-5 Codex, send requests to OrcaRouter’s OpenAI-compatible API endpoint at base_url https://api.orcarouter.ai/v1. Your API call should include the model ID "openai/gpt-5-codex". The request format is identical to the standard OpenAI API, so existing codebases can be migrated by changing the base URL and model name. Authentication requires your OrcaRouter API key, which can be obtained from your OrcaRouter dashboard. Example request using curl: curl https://api.orcarouter.ai/v1/chat/completions -H "Authorization: Bearer YOUR_API_KEY" -d '{"model": "openai/gpt-5-codex", "messages": [...]}'.
All standard OpenAI chat completions parameters are supported when using OrcaRouter’s API. These include "temperature" (0–2, default 1), "max_tokens", "top_p", "frequency_penalty", "presence_penalty", and "stop". Because GPT-5 Codex has a 128K output limit, you can set max_tokens up to that value. For image input, messages can contain image_url content parts. Parameter behavior is the same as documented by OpenAI. Refer to OrcaRouter’s documentation for any provider-specific notes. There are no additional custom parameters required for GPT-5 Codex beyond the model id.
Migration is straightforward. Replace your OpenAI API base URL with https://api.orcarouter.ai/v1 and update the model name to "openai/gpt-5-codex". Replace your OpenAI API key with your OrcaRouter API key. The request and response schemas are identical, so no code logic changes are needed. This allows you to access GPT-5 Codex at the same provider rates with zero markup. For example, if you previously used "model": "gpt-4-turbo", now use "model": "openai/gpt-5-codex". Test with a small request to verify token pricing and response quality.
Authentication requires an API key from OrcaRouter. You can generate a key from your account dashboard. The key is passed in the Authorization header as "Bearer YOUR_API_KEY". OrcaRouter uses the same authentication scheme as OpenAI. Keep the key secure; do not expose it in client-side code. For high-traffic applications, consider environment variables. OrcaRouter may also support other authentication methods—check the official documentation. There is no additional authentication needed beyond the API key. If you encounter errors, verify the key and that you are using the correct base URL and model ID.
While exact benchmark comparisons are not provided, GPT-5 Codex offers a larger context window (400K vs. 128K tokens), higher max output (128K vs. 4K-16K), and image input support. It also scores 98.7 on AA Math, which likely exceeds GPT-4 Turbo’s math performance. Pricing is higher: GPT-5 Codex costs $1.25/$10 per 1M tokens compared to GPT-4 Turbo’s lower rates. For tasks that require extreme context length, multistep reasoning, or image understanding, GPT-5 Codex is the stronger choice. GPT-4 Turbo remains a cost-effective alternative for moderate workloads.
Anthropic’s Claude 3 family (Opus, Sonnet, Haiku) also offers large context windows (up to 200K tokens) and multimodal input. However, GPT-5 Codex’s 400K context is twice as large, and its 128K output limit is unique. On the AA Math benchmark, GPT-5 Codex’s 98.7 is notably high; Claude 3 Opus has strong math but specific scores are not provided. Pricing structures differ—Claude models have their own per-token rates. Through OrcaRouter, you can compare both. Choose GPT-5 Codex for tasks demanding maximum context and math precision; Claude 3 may be preferred for nuanced text conversations.
Llama 3 models (e.g., 70B) are open-source and free to self-host, but they have smaller context windows (usually up to 128K tokens) and lower output limits. They do not natively support multimodal input (unless extended). Benchmark performance varies: Llama 3 70B scores well on many tasks but does not have a specific AA Math score. GPT-5 Codex is proprietary and accessed via paid API, but it offers superior context size and math accuracy. For users who need maximum capabilities and are willing to pay, GPT-5 Codex is the clear winner. For budget-constrained or privacy-sensitive projects, open-source models may be more appropriate.
OrcaRouter provides access to many models, but GPT-5 Codex stands out for its 400K context window, 128K output token limit, multimodal input, and 98.7 AA Math benchmark score. If your use case involves very long code or documents, advanced algebra, or interpreting images alongside text, this model is the most capable choice among those listed. Its zero-markup pricing means you pay only OpenAI’s rates. However, for shorter tasks or lower budgets, OrcaRouter’s other models like GPT-4 Turbo or Claude 3 may offer better value. Evaluate your workload’s requirements against the model’s strengths 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-codex",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)include_reasoningmax_completion_tokensmax_tokensreasoningresponse_formatseedstreamstructured_outputstool_choicetools| Input / 1M tokens | $1.25 |
| Output / 1M tokens | $10.00 |
| Cache read / 1M | $0.125 |
| 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-codexOpen @misc{orcarouter_gpt_5_codex,
title = {GPT-5 Codex API},
author = {OpenAI},
year = {2025},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/openai/gpt-5-codex}
}OpenAI. (2025). GPT-5 Codex API. OrcaRouter. https://www.orcarouter.ai/models/openai/gpt-5-codex