GPT-5.1-Codex-Mini is a smaller and faster version of GPT-5.1-Codex
GPT-5.1-Codex-Mini is a model from OpenAI that can be accessed through OrcaRouter. It is part of the GPT-5.1 Codex series, designed for code-centric and reasoning tasks. The 'Mini' designation…
GPT-5.1-Codex-Mini supports both image and text inputs. This means you can provide images (such as diagrams, screenshots, or hand-drawn notes) alongside text prompts, and the model will interpret both modalities. The context window of 400000 tokens includes the tokens used to represent images; each image consumes a variable number of tokens depending on its size and encoding. While the exact token cost for images is not provided by OrcaRouter, it follows OpenAI's standard practice where images are tokenized based on resolution. The model can generate text-only outputs up to 128000 tokens. The multimodal capability expands its usefulness beyond pure code to tasks requiring visual understanding, such as explaining code architecture from a diagram.
Based on its Codex designation and benchmark score of 91.7 on AA Math, GPT-5.1-Codex-Mini excels at mathematical reasoning and code generation. The AA Math benchmark tests advanced arithmetic and algebraic reasoning, so the model is well-suited for generating proofs, solving equations, and performing logical steps. In coding, it can handle multiple languages, debug, refactor, and explain code. The large context window (400000 tokens) allows it to work with entire codebases or long documents, making it effective for tasks like summarizing an entire repository, performing cross-file refactoring, or generating documentation from a large codebase. The high output limit (128000 tokens) enables generation of comprehensive solutions in a single response.
While GPT-5.1-Codex-Mini is itself a cost-effective version of the Codex line, there may be even cheaper alternatives for simpler tasks. If your workload does not require the specialized code and math reasoning, a general-purpose model like GPT-4o mini might suffice at a lower price point. Additionally, if you do not need the full 400000-token context or 128000-token output, a model with smaller limits could reduce costs. For tasks that involve only text (no images) and short contexts, a smaller, cheaper model may be more appropriate. OrcaRouter offers access to many models; compare pricing and capabilities to select the most cost-effective option for your specific use case.
GPT-5.1-Codex-Mini is a 'Mini' variant, so it likely has reduced capacity compared to the full GPT-5.1-Codex. This may manifest as less nuanced reasoning in very complex multi-step problems, or slightly lower accuracy on specialized benchmarks not covered by AA Math. The model's knowledge cutoff is determined by OpenAI; while not specified, it is part of the GPT-5.1 series so it reflects relatively recent data. Being a code-optimized model, it may not perform as well on tasks requiring creative writing or open-ended dialogue compared to general-purpose models. Additionally, image input is supported but the token cost for images can be significant; users should monitor usage to avoid unexpected costs. OrcaRouter provides no additional processing beyond forwarding to OpenAI.
GPT-5.1-Codex-Mini achieved a score of 91.7 on the AA Math benchmark. AA Math (Advanced Arithmetic and Algebraic Math) evaluates a model's ability to perform complex arithmetic operations, algebraic manipulations, and multi-step reasoning. A score of 91.7 indicates that the model correctly answered 91.7% of the test problems. This is a strong result, suggesting the model is highly capable in mathematical reasoning tasks. It implies that for coding tasks involving numeric computation, algorithmic simulation, or mathematical logic, the model can be relied upon to produce accurate results. The benchmark does not cover all mathematical domains (e.g., geometry, probability in detail), but it validates its strength in core algebra and arithmetic.
OrcaRouter does not publish latency metrics for GPT-5.1-Codex-Mini. As a smaller variant within the Codex family, it is expected to be faster than a full-sized GPT-5.1-Codex model, but actual speed depends on request complexity, number of tokens processed, and server load at both OrcaRouter and OpenAI. The model supports streaming responses, which can reduce perceived latency for users. Users should test with their specific workloads to gauge performance. Factors such as image input size and output length will affect response time. Since OrcaRouter is a proxy, network latency adds a small overhead. Overall, the model's speed is likely suitable for interactive use but may vary.
The Codex lineage indicates GPT-5.1-Codex-Mini is specifically fine-tuned for code understanding and generation. While no coding benchmark scores are provided, the high AA Math score suggests strong reasoning abilities that transfer to algorithmic problem-solving. The model can handle multiple programming languages, generate syntactically correct code, explain existing code, and suggest refactorings. Its 400000-token context window allows it to retain entire code repositories, enabling tasks like cross-module dependency analysis or generating documentation for large classes. The 128000-token output limit lets it produce long code files or comprehensive explanations in one go. These features make it a strong candidate for development workflows that involve large-scale code management.
While GPT-5.1-Codex-Mini performs well on AA Math (91.7), it is not infallible. Benchmarks may not cover all edge cases; real-world performance can vary. The model may struggle with highly nuanced or domain-specific mathematical concepts not included in AA Math. In code generation, it may occasionally produce inefficient or incorrect code, especially for rare or poorly documented libraries. As a 'Mini' model, it may lack the depth of reasoning for very complex problems that a larger model could handle. There is also the potential for hallucinations or generation of plausible but incorrect answers. Users should always validate critical outputs, especially in safety-sensitive applications. OrcaRouter does not modify model behavior; these limitations are inherent to the model itself.
OrcaRouter bills GPT-5.1-Codex-Mini usage at the provider rate with zero markup. The price is $0.25 per 1 million input tokens and $2.00 per 1 million output tokens. Input tokens include the text prompt and any tokenized images. Output tokens are the text generated by the model. Billing is measured per request based on the total number of tokens processed. There are no extra fees, subscription charges, or minimum commitments. Users pay only for the tokens they consume. OrcaRouter likely requires an account and API key; the cost is deducted from the user's balance. For accurate cost management, monitor token usage via the API response, which typically includes usage statistics.
Compared to a hypothetical full GPT-5.1-Codex (if it existed), GPT-5.1-Codex-Mini is designed to be more cost-effective due to its smaller size. The known prices ($0.25/$2.00 per 1M tokens) are lower than typical full-size models from OpenAI, which can cost $10-$30 per million output tokens. This makes Codex-Mini attractive for high-volume applications or those with long contexts and large outputs. However, if performance on specific tasks is insufficient, a larger model may justify the extra cost. Users should evaluate the trade-off: for tasks where the Mini model's 91.7 AA Math score is adequate, its lower cost is advantageous. OrcaRouter does not offer caching or prompt compression that would reduce token usage.
OrcaRouter does not advertise caching for GPT-5.1-Codex-Mini. Caching is not mentioned in the provided facts about the model or API. Generally, caching of prompts and responses is not standard across proxy APIs due to data privacy considerations; each request is typically processed independently. If you require reduced costs for repeated identical inputs, consider implementing client-side caching or use batching if supported. OrcaRouter's pricing is strictly pay-per-token with no discounts for repeated context. Note that token usage for images is computed per request and cannot be reduced via caching. For cost optimization, minimize redundant token consumption by crafting precise prompts.
No hidden fees are associated with accessing GPT-5.1-Codex-Mini through OrcaRouter. The pricing is transparent: $0.25 per 1M input tokens and $2.00 per 1M output tokens, with zero markup over provider rate. There are no setup fees, monthly minimums, or charges for failed requests (though you pay for tokens used in the request/response). OrcaRouter may have an API key system but does not impose additional endpoint charges. Always check OrcaRouter's official documentation for any updates to their pricing policy. As with any API, network errors or timeouts may result in partial token consumption; if a request is interrupted, you are charged only for tokens processed up to the interruption, as determined by OpenAI.
To call GPT-5.1-Codex-Mini via OrcaRouter, use the OpenAI-compatible API at base URL https://api.orcarouter.ai/v1. Set the model parameter to "openai/gpt-5.1-codex-mini". You will need an OrcaRouter API key for authentication, which is typically passed in the Authorization header as Bearer. The API supports the same endpoints/completions as the standard OpenAI API, such as /chat/completions for chat-style interactions. Because it is OpenAI-compatible, existing code using the OpenAI Python or JavaScript SDK can be adapted by changing the base URL and API key. OrcaRouter handles routing to OpenAI and returns results in the same format. Ensure your client respects token limits (400000 input, 128000 output).
The OrcaRouter API for GPT-5.1-Codex-Mini supports standard OpenAI parameters, including model (set to "openai/gpt-5.1-codex-mini"), messages (array of message objects for chat), max_tokens (up to 128000), temperature, top_p, n, stop, stream, and others. The function calling and tool use capabilities of the model are supported as provided by OpenAI. Image input can be included in the message content using the multimodal format (e.g., content with type: "image_url"). Parameter defaults and behaviors follow OpenAI's specifications. Note that the model's context window is 400000 tokens total, so the sum of all tokens in messages (including images) must not exceed this limit. OrcaRouter does not expose any additional custom parameters beyond what OpenAI offers.
Migrating to OrcaRouter for GPT-5.1-Codex-Mini is straightforward if you have existing code that uses an OpenAI-compatible API. Replace the base URL to https://api.orcarouter.ai/v1 and update your API key to an OrcaRouter key. Change the model parameter to "openai/gpt-5.1-codex-mini". If you were using a different model ID, adjust accordingly. OrcaRouter does not require any special SDK; any HTTP client that works with OpenAI's API will work. Test with a small request to verify connectivity and billing. Note that pricing is identical to OpenAI's provider rate, so cost migration is neutral. If you were using another provider, you may need to adjust for the model's context window (400000) and output limit (128000).
An example request using curl: curl https://api.orcarouter.ai/v1/chat/completions -H "Authorization: Bearer ORCAROUTER_API_KEY" -H "Content-Type: application/json" -d '{"model": "openai/gpt-5.1-codex-mini", "messages": [{"role": "user", "content": "Solve: 2x + 5 = 15"}], "max_tokens": 200}'. Include image by using content array: "content": [{"type": "text", "text": "Explain this diagram"}, {"type": "image_url", "image_url": {"url": "data:image/png;base64,..."}}]. The response will be a standard OpenAI chat completion object. Ensure the total token count (including tokenized image) stays under 400000. Use stream: true for streaming responses.
GPT-5.1-Codex-Mini and GPT-4o serve different primary purposes. GPT-4o is a general-purpose multimodal model, while Codex-Mini is specialized for code and math. In benchmarks, AA Math score of 91.7 provides a specific point of comparison; GPT-4o's performance on that benchmark is not provided but presumably lower. Codex-Mini has a larger context window (400000 vs. GPT-4o's 128000) and higher output limit (128000 vs. 4096 for GPT-4o in typical usage). However, GPT-4o may be more versatile for creative tasks. Pricing: Codex-Mini at $0.25/$2.00 per 1M tokens is less expensive than GPT-4o ($5/$15 per 1M tokens), making it a cost-effective choice for math and coding tasks where its strengths align.
Choose GPT-5.1-Codex-Mini when your primary needs are code generation, mathematical reasoning, or tasks requiring a very large context (up to 400000 tokens) and high output (up to 128000 tokens). It is also a good choice for cost-sensitive applications that still demand strong math and coding performance, given its lower price relative to full-size models. If your task is purely creative writing or visual art description, a general-purpose model like GPT-4o may be more suitable. If you need even lower cost for simple tasks, consider smaller models like GPT-4o mini. OrcaRouter gives you access to many models; evaluate benchmark relevance and pricing for your specific workload.
OrcaRouter provides access to GPT-5.1-Codex-Mini at the same provider pricing ($0.25/$2.00 per 1M tokens) with zero markup. There is no cost benefit to using OpenAI directly versus OrcaRouter if both charge the same. However, OrcaRouter may offer simplified billing, consolidated usage for multiple providers, or additional features like usage analytics. The API is fully compatible with OpenAI's interface, so migration is trivial. If you already have an OrcaRouter account, you can use this model without setting up an OpenAI separate account. OrcaRouter acts as a proxy; data handling is similar to using OpenAI directly (prompts sent to OpenAI). Choose OrcaRouter if you prefer a unified API gateway across multiple model providers.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key=os.environ["ORCAROUTER_API_KEY"],
)
response = client.chat.completions.create(
model="openai/gpt-5.1-codex-mini",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)include_reasoningmax_completion_tokensmax_tokensreasoningresponse_formatseedstreamstructured_outputstool_choicetools| Input / 1M tokens | $0.250 |
|---|---|
| Output / 1M tokens | $2.00 |
| Cache read / 1M | $0.025 |
| Currency | USD |
Estimate based on list price
Estimate only — actual token counts depend on the provider's tokenizer.
Compare monthly costs across models in the LLM cost calculator
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GET /api/public/models/openai/gpt-5.1-codex-miniOpen @misc{orcarouter_gpt_5_1_codex_mini,
title = {GPT-5.1-Codex-Mini API},
author = {OpenAI},
year = {2025},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/openai/gpt-5.1-codex-mini}
}OpenAI. (2025). GPT-5.1-Codex-Mini API. OrcaRouter. https://www.orcarouter.ai/models/openai/gpt-5.1-codex-mini