GPT-5.3-Codex is OpenAI’s most advanced agentic coding model, combining the frontier software engineering performance of GPT-5.2-Codex with the broader reasoning and professional knowledge capabilities of GPT-5.2. It achieves state-of-the-art results...
GPT-5.3-Codex is a large language model from OpenAI, accessible through OrcaRouter's OpenAI-compatible API. It is specifically named "Codex," indicating a focus on code generation and understanding,…
GPT-5.3-Codex achieved a score of 91.5 on the GPQA Diamond benchmark, which tests graduate-level reasoning across physics, chemistry, biology, and other sciences. This indicates strong performance on complex, multi-step reasoning problems. The model can answer questions that require deep understanding, synthesis of multiple facts, and logical deduction. It is not limited to code; it can handle abstract reasoning, mathematical calculations, and scientific analysis. However, as with all large language models, it may occasionally produce incorrect or inconsistent outputs, especially on highly specialized or ambiguous questions.
As a Codex model, GPT-5.3-Codex is designed for code understanding and generation. It can generate code in multiple programming languages based on natural language descriptions or context from files. The 400K context allows it to consider large codebases for tasks like refactoring, bug fixing, or documentation generation. It can also process images of code or diagrams, such as architecture flowcharts, to inform its reasoning. While it can handle algorithmic challenges and library usage, users should verify outputs for correctness, especially in production environments.
The pricing for GPT-5.3-Codex is $1.75 per 1M input tokens and $14.00 per 1M output tokens. If your task does not require a large context window (e.g., short prompts, single-turn queries) or does not need the high reasoning capability indicated by the GPQA Diamond score, a less expensive model may be more cost-effective. For example, models with smaller context windows (8K–128K) and lower output token costs are available through OrcaRouter. Consider the trade-off: use GPT-5.3-Codex only when you regularly process large documents or need the best possible reasoning for complex questions.
Yes, GPT-5.3-Codex accepts text, image, and file inputs within the same context window. You can provide an image of a chart, a text description, and a PDF file all in one request, and the model will process them together. This is useful for tasks like analyzing a research paper where the paper is a file, the figures are images, and you ask a question in text. The model treats all modalities as part of the input, constrained by the total 400K token limit. Note that images count toward the token limit; the actual number of images that fit depends on their resolution and encoding.
GPQA Diamond is a benchmark consisting of multiple-choice questions that require graduate-level reasoning across science disciplines. It is designed to be challenging for both humans and AI models, often requiring deep knowledge and multi-step reasoning. A score of 91.5 means GPT-5.3-Codex answered 91.5% of the questions correctly, which is a high score. This indicates the model can handle complex, expert-level questions. However, benchmark scores do not guarantee real-world performance on all tasks; they measure specific capabilities under controlled conditions.
A high GPQA Diamond score suggests GPT-5.3-Codex excels at tasks that require careful reasoning, such as answering technical questions, debugging code, or providing explanations for scientific concepts. It can process subtle distinctions and avoid common pitfalls. In practice, this means users can expect better accuracy on complex prompts compared to models with lower scores. However, the score is an average; the model may still fail on certain question types or edge cases. It is advisable to test the model on representative tasks from your domain.
Latency depends on request size, output length, and current load on OpenAI's infrastructure. OrcaRouter passes requests directly to OpenAI without additional processing, so latency is determined by the provider. The 400K context window and 128K output mean that very long inputs or outputs can take longer to process. For real-time applications, consider using shorter contexts or a smaller model. There is no specific latency figure provided; users should benchmark with their typical inputs. OrcaRouter does not impose any extra delay beyond OpenAI's response time.
Despite its high GPQA Diamond score, GPT-5.3-Codex has limitations. It may produce confident but incorrect answers (hallucination), especially on obscure topics. Its knowledge cut-off date is not specified; it may lack recent information. The model's performance on non-scientific reasoning, such as creative writing or common sense, may be less remarkable than on graduate-level science. It is sensitive to prompt phrasing, and its outputs can be verbose. Additionally, the 400K context window is a maximum; performance can degrade when the context is very full, especially in the middle. Users should validate outputs for critical tasks.
Pricing is $1.75 per 1 million input tokens and $14.00 per 1 million output tokens. These are the provider rates from OpenAI; OrcaRouter adds zero markup. Input tokens include all text, image tokens (based on resolution), and file tokens. Output tokens are the generated text. For context, a request with 100K input tokens and 5K output tokens would cost roughly $0.175 for input and $0.07 for output, total $0.245. Costs scale linearly with usage. There is no additional per-request fee. Users are billed based on actual token consumption.
The input price of $1.75/M tokens is moderate compared to some premium models, while the output price of $14/M is relatively high. Output tokens are expensive because generation is computationally intensive. If your workflow involves generating very long responses (e.g., 50K tokens), the cost can be substantial. Consider whether a cheaper model with smaller context can meet your needs for simpler tasks. Also, note that the model's large context may encourage users to include more input tokens, increasing cost. Plan to optimize prompts to keep both input and output tokens lean.
OrcaRouter passes through the provider rate without markup. There is no mention of caching or discount programs specific to this model. Users are billed per token as consumed. If you have high-volume needs, you may contact OrcaRouter for potential volume pricing, but no specific details are provided. For cost management, monitor token usage and set limits. Caching of responses is not standard; each request is processed independently. Because the model is accessed via OpenAI, any caching or prompt caching features from OpenAI would apply, but OrcaRouter does not add its own caching layer.
You call the model through OrcaRouter's OpenAI-compatible API at the base URL https://api.orcarouter.ai/v1. Use the model id "openai/gpt-5.3-codex". The API is compatible with the OpenAI Python client, curl, and other tools. A sample Python call: import openai client = openai.OpenAI(base_url="https://api.orcarouter.ai/v1", api_key="your_key") response = client.chat.completions.create( model="openai/gpt-5.3-codex", messages=[{"role": "user", "content": "Hello"}] ) For multimodal inputs, use the appropriate format with image_url or file attachments as per OpenAI's multimodal API.
The API supports standard OpenAI parameters: messages (list of message objects), temperature, top_p, n, max_tokens, presence_penalty, frequency_penalty, stop, stream, and others. For multimodality, messages can include content parts with type text, image_url (with detail parameter), or file objects. The max_tokens parameter should not exceed the model's maximum output of 128,000. The context window of 400,000 tokens is the total input tokens across all messages and modalities. There is no parameter for context length; the model automatically uses the full provided context. Ensure your total input tokens are within the limit.
Migration is straightforward: change the model field in your API call from your previous model id (e.g., "openai/gpt-4o") to "openai/gpt-5.3-codex". Ensure your API key is for OrcaRouter and you are using the base URL https://api.orcarouter.ai/v1. Be aware of the different context and output limits. If your previous model had a smaller context, your prompts may now fit entirely. You may also need to adjust multimodal formatting if not previously used. Test with sample requests to verify behavior and costs. The API response format is identical to OpenAI's.
OrcaRouter uses API key authentication. You must include your key in the Authorization header as Bearer token. Obtain an API key from your OrcaRouter account. The key is used to track usage and billing. There is no additional authentication layer. The base URL is https://api.orcarouter.ai/v1. Ensure your client library supports custom base URLs. For security, keep your API key secret and rotate it regularly. OrcaRouter does not provide OAuth or other auth methods for this endpoint.
Compared to models like GPT-4o-mini or GPT-3.5, GPT-5.3-Codex offers a much larger context window (400K vs typically 8K–128K) and higher output limit (128K vs 4K–16K). Its GPQA Diamond score of 91.5 indicates superior reasoning. However, it is more expensive per token, especially on output. Smaller models are better for high-throughput, low-cost applications where complex reasoning is unnecessary. Choose GPT-5.3-Codex when you need to process large amounts of data or require the best possible answer quality.
Many multimodal models accept text and images, but GPT-5.3-Codex additionally supports file inputs. Its context window of 400K tokens is large relative to most multimodal models (often 128K). The 91.5 GPQA Diamond score is high in absolute terms, though direct comparisons depend on the benchmark. Models from other providers may offer different strengths, such as specialized vision or lower latency. GPT-5.3-Codex is a generalist model with strong reasoning; for tasks purely based on image recognition, a dedicated vision model might be more efficient.
If your task does not require the 400K context or the 128K output, a smaller model will be more cost-effective. If your primary need is speed, consider models with lower latency. If you only need text input, a text-only model may be cheaper. For tasks outside the scientific reasoning domain (e.g., creative writing), models tuned for chat may perform better. Also, if you need to run the model on specific hardware or have data residency requirements, consider models deployed in those regions. Evaluate based on your actual workload.
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-5.3-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-5.3-codexOpen @misc{orcarouter_gpt_5_3_codex,
title = {GPT-5.3-Codex API},
author = {OpenAI},
year = {2026},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/openai/gpt-5.3-codex}
}OpenAI. (2026). GPT-5.3-Codex API. OrcaRouter. https://www.orcarouter.ai/models/openai/gpt-5.3-codex