GPT-5.1-Codex is a specialized version of GPT-5.1 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-5.1-Codex is a large language model offered by OpenAI, optimized for code generation, mathematical reasoning, and text-based problem solving. It accepts both text and image inputs,…
GPT-5.1-Codex excels at code generation, mathematical reasoning, and handling long documents. Its 95.7 AA Math benchmark score indicates strong performance on advanced arithmetic and algebraic problems. The model can generate complex code in many programming languages, debug existing code, explain algorithms, and translate between languages. It is particularly effective at tasks requiring deep contextual understanding, such as refactoring a large module or writing unit tests for a whole class. The large context window allows it to maintain state across very long conversations or documents. These strengths make it suitable for professional software development, academic research, and data analysis workflows where precision and depth are required.
GPT-5.1-Codex can handle a wide range of code tasks: writing new functions, classes, or entire applications; optimizing existing code for performance or readability; generating documentation and inline comments; converting code between programming languages; and producing test suites. Its large output limit (128K tokens) enables generating very long files in a single call, such as a complete backend service or a scientific simulation script. The model also supports multi-modal inputs, so it can parse images of code snippets (e.g., screenshots) or architectural diagrams. Because it is a dedicated code variant, it tends to produce more syntactically correct and idiomatic code than general‑purpose models, though results depend on prompt clarity and complexity.
GPT-5.1-Codex is a premium model with a high cost per token. For simple tasks such as short text completion, basic question answering, or generating a few lines of code, a cheaper model (e.g., GPT-4o mini or Claude Haiku) will often be sufficient and more cost effective. Similarly, for tasks that do not require the large context window — for example, a single function with 500 tokens — the extra capacity is unnecessary. OrcaRouter offers many model options with varying price points. Use GPT-5.1-Codex when the task genuinely benefits from its large context, high output, and mathematical strength. For straightforward or low‑stakes work, downsizing to a less expensive model reduces spend without sacrificing quality.
While GPT-5.1-Codex is strong in code and mathematics, its training likely prioritizes those domains. Consequently, it may not match the performance of specialized creative writing models when generating fiction, marketing copy, or conversational dialogue. The model’s knowledge cutoff and factual accuracy are subject to OpenAI’s base model training; it may hallucinate or give outdated information, especially on fast-moving topics like current events or software library versions. Additionally, the image input modality processes images for context but the model cannot generate images or diagrams as output. For domain-specific tasks outside code and math — such as medical advice, legal interpretation, or sensitive moderation — a model specifically fine-tuned for that domain would be more appropriate.
The provided benchmark score of 95.7 on AA Math (presumably a challenging arithmetic or algebra dataset) demonstrates that GPT-5.1-Codex has very strong mathematical abilities. This suggests it can handle complex multi‑step arithmetic, equation solving, and symbolic reasoning with high accuracy. Scores in this range are typically among the best for state‑of‑the‑art language models on similar metrics. For users needing precise calculations, proof generation, or scientific computations, this model is a reliable choice. However, benchmark scores are not indicative of performance on all real‑world tasks; they reflect a specific evaluation set. Users should evaluate the model on their own representative data to validate its effectiveness.
No additional benchmark scores are provided for GPT-5.1-Codex beyond the 95.7 AA Math result. However, based on the model’s naming and positioning as a code‑focused variant, it is reasonable to expect strong performance on reasoning tasks that benefit from structured, logical thinking — such as code generation, algorithm design, and puzzle solving. On more general reasoning benchmarks (e.g., MMLU, GSM8K, HumanEval), the model likely performs competitively with other large OpenAI models, though exact numbers are not public. Users who want a fuller picture should test the model on their own tasks. OrcaRouter allows easy experimentation with its API; you can run a few sample prompts to gauge the model’s reasoning quality in your domain.
No specific latency or throughput numbers are provided for GPT-5.1-Codex. Generally, models with very large context windows (400K tokens) and high output limits (128K tokens) can have higher per‑request latency compared to smaller models, especially when processing long inputs or generating long outputs. The actual response time depends on factors such as request size, network conditions, and backend load on OrcaRouter’s infrastructure. For real‑time applications requiring subsecond responses, this model may be too slow; batch or asynchronous use is recommended. OrcaRouter supports streaming responses via the standard chat completions stream parameter, which can help reduce perceived latency by displaying partial results. Users should benchmark their typical workload to understand performance.
While the AA Math score is high, it represents performance on a specific, well‑defined problem set. Real‑world tasks often involve ambiguity, incomplete context, or domain‑specific jargon that benchmarks do not capture. Additionally, the model may exhibit biases or errors on edge cases — for example, when handling very long contexts it may lose focus on earlier content (the “lost in the middle” effect). The model’s outputs should always be reviewed by a human when correctness is critical, especially in safety‑sensitive domains. Furthermore, no benchmark reflects the model’s ability to stay current; GPT-5.1-Codex has a knowledge cutoff tied to its training data, so it may not be aware of very recent events or library releases. Consider supplementing with retrieval‑augmented generation if timeliness matters.
On OrcaRouter, GPT-5.1-Codex is billed at the provider rate with zero markup. The price is $1.25 per 1 million input tokens and $10 per 1 million output tokens. Input tokens include the prompt, system message, and any images (images are tokenized according to the model’s vision cost). Output tokens are the model’s response. There are no additional platform fees. This transparent pricing makes it easy to estimate costs: for example, a request with 10,000 input tokens and 2,000 output tokens would cost approximately $0.0125 (input) + $0.02 (output) = $0.0325. Billing is based on actual token usage as reported by the API. Users can monitor usage via the OrcaRouter dashboard.
Output tokens are eight times more expensive than input tokens ($10 vs $1.25 per million). This asymmetry means that for tasks requiring very long outputs, the cost is dominated by generation. For example, generating a 100K‑token output would cost $1.00 just for the output — far exceeding typical input costs. Conversely, tasks with a very long prompt but short response — such as analyzing a large codebase and giving a short summary — are relatively cheap on the output side. Users should structure prompts to minimize unnecessary generation length when possible. The large context window encourages including abundant context, but that context comes at a cost. Using caching (if supported) or reducing token count can help manage expenses. Always balance context size and output length against budget.
The provided facts do not mention any caching or discount programs for GPT-5.1-Codex on OrcaRouter. Typically, API pricing is pay‑as‑you‑go per token with no volume discounts or pre‑purchase plans mentioned. OrcaRouter may offer developer tiers or enterprise agreements, but those are not described here. To minimize costs, consider the following strategies: keep contexts as short as needed, reuse previous responses when appropriate, and batch requests during development. For production use, monitor token consumption and set spending limits. If you require extremely high volumes, contact OrcaRouter support to inquire about potential custom pricing — but no such information is currently provided.
GPT-5.1-Codex is accessed through OrcaRouter’s OpenAI‑compatible API. Use the base URL https://api.orcarouter.ai/v1 in your OpenAI client library. Set the model parameter to the exact ID “openai/gpt-5.1-codex”. The API supports chat completions with the standard messages array, including system, user, and assistant roles. For image inputs, include an image_url content block in the user message. Example request body: { "model": "openai/gpt-5.1-codex", "messages": [ {"role": "user", "content": "Write a Python function to calculate Fibonacci numbers."} ], "max_tokens": 500, "temperature": 0.2 } Authentication is via an API key (either from OrcaRouter or an OpenAI‑compatible key, depending on your setup). Refer to OrcaRouter’s documentation for details.
When using OrcaRouter’s API with GPT-5.1-Codex, you can set all standard OpenAI parameters: model (required), messages (required), max_tokens, temperature, top_p, frequency_penalty, presence_penalty, stop sequences, stream (boolean), and response_format (e.g., json_object). Additional OrcaRouter‑specific parameters may exist but are not documented here. The max_tokens parameter controls the maximum length of the generated response, up to the model’s 128K token limit. The temperature parameter (range 0–2) controls randomness; lower values produce more deterministic outputs, suitable for code generation. The stream parameter enables streaming of partial tokens. Note that the context window of 400K tokens includes both the messages and the response; ensure the total token count (system + user + assistant) plus max_tokens does not exceed 400K.
Migrating to GPT-5.1-Codex is straightforward because OrcaRouter’s API is OpenAI‑compatible. If you already use the OpenAI Python library (or any SDK that supports the OpenAI interface), simply change the base URL to https://api.orcarouter.ai/v1 and the model name to “openai/gpt-5.1-codex”. Your existing message structures, parameters, and authentication method (API key) remain largely the same. However, note that the model has specific context and output limits: adjust max_tokens to make full use of the 128K output capacity if needed, and ensure your prompt plus response fits within 400K total tokens. Review your prompts to leverage the model’s code and math strengths. Test with a subset of your workload before full migration.
Authentication for OrcaRouter’s OpenAI‑compatible API uses an API key sent in the Authorization header as "Bearer <your_api_key>". You can obtain an API key from the OrcaRouter dashboard. The key is used identically to OpenAI’s key format. All requests must include this header; otherwise, a 401 error is returned. OrcaRouter does not support other authentication methods (e.g., OAuth) through this endpoint. For security, keep your API key secret and rotate it periodically. If you already use the OpenAI Python library, you can set the api_key parameter or the environment variable OPENAI_API_KEY. The base URL remains https://api.orcarouter.ai/v1. No additional authentication fields are required.
GPT-5.1-Codex is a specialized code variant with a 400K context window and 128K max output, while GPT-4o (a general‑purpose model) typically has a smaller context window (e.g., 128K) and lower output limits. The Codex variant is likely optimized for programming and mathematical reasoning, whereas GPT-4o is built for broad conversational and multimodal tasks. Benchmark scores for mathematical reasoning (the provided 95.7 AA Math) are not directly available for GPT-4o. On OrcaRouter, pricing for GPT-5.1-Codex is $1.25/$10 per M tokens (input/output), which is higher than GPT-4o’s typical rates. For code‑intensive workloads requiring deep context, GPT-5.1-Codex may deliver superior accuracy, but for general chat, GPT-4o remains a more cost‑effective choice.
Other code‑specific models include OpenAI’s earlier Codex versions, GPT-4o mini, and specialized models like DeepSeek Coder. GPT-5.1-Codex distinguishes itself with a larger context window (400K) and very high output limit (128K). The provided AA Math benchmark (95.7) suggests superior mathematical reasoning, which may be important for scientific computing. Earlier Codex models typically had smaller contexts (e.g., 16K) and lower scores. DeepSeek Coder models offer competitive coding performance but often have smaller context windows (e.g., 32K–128K) and lower pricing. On OrcaRouter, GPT-5.1-Codex’s pricing is relatively high, so it is best used when the large context and high output are necessary. For typical code completion or short‑response coding, a cheaper code‑specialist is more economical.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1from openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key="$ORCAROUTER_API_KEY",
)
response = client.chat.completions.create(
model="openai/gpt-5.1-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-5.1-codexOpen @misc{orcarouter_gpt_5_1_codex,
title = {GPT-5.1-Codex API},
author = {OpenAI},
year = {2025},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/openai/gpt-5.1-codex}
}OpenAI. (2025). GPT-5.1-Codex API. OrcaRouter. https://www.orcarouter.ai/models/openai/gpt-5.1-codex