GPT-4.1 is a flagship large language model optimized for advanced instruction following, real-world software engineering, and long-context reasoning. It supports a 1 million token context window and outperforms GPT-4o and...
OpenAI GPT-4.1 is a large language model from OpenAI that is accessible on OrcaRouter. It offers a context window of 1,047,576 tokens and a maximum output of 32,768 tokens. The model accepts input in…
With its 1,047,576 token context, GPT-4.1 can process very long documents in a single request. It can summarize entire books, answer specific questions about dense legal texts, extract key data from lengthy reports, and analyze research papers end-to-end. The model retains details across the full context, allowing for coherent reasoning over the entire document. This capability is useful for due diligence, literature reviews, and archival analysis where completeness is critical.
Images can be included as part of the messages array in an API call, and the model will interpret them as additional context. For example, you can upload a chart and ask for a summary, or provide a photograph and request a description. File inputs extend this by allowing the model to load data from files directly—though the exact file types are not specified. The model processes these modalities together, enabling tasks like image-based question answering and document scanning.
Best use cases include long-form reasoning, such as analyzing complex legal or financial documents; codebase analysis and generation across large projects; academic research summarization; and tasks that combine text with images, like interpreting diagrams or extracting data from screenshots. The model also excels at mathematical problem solving, as indicated by its 91.3 MATH-500 score. These strengths make it suitable for high-stakes applications where accuracy and depth are paramount.
If your tasks involve short inputs, simple Q&A, or don't require multimodal support, a smaller and cheaper model may be more cost-effective. For example, GPT-4o mini offers lower token prices and sufficient context for many everyday uses. Similarly, if you rarely use the full 1M token context, you may be paying for capacity you don't need. Evaluate the trade-off between cost and capability based on the complexity and length of your typical workload.
MATH-500 is a benchmark consisting of 500 challenging mathematical problems covering topics like calculus, algebra, geometry, and logic. A score of 91.3% means GPT-4.1 correctly solves 913 out of every 1000 problems, placing it among the top-performing general-purpose models on this metric. This score suggests strong mathematical reasoning and problem-solving skills, making the model suitable for tasks that require symbolic manipulation, step-by-step derivation, or numeric computation.
GPT-4.1's primary strengths are its extremely large context window, multimodal input support, and high performance on mathematical reasoning. The large context allows processing of lengthy documents without chunking, while multimodal support enables richer interactions. The MATH-500 score of 91.3 indicates robust quantitative reasoning. Additionally, the model benefits from OpenAI's ongoing fine-tuning, safety alignment, and regular updates, which contribute to its reliability across diverse tasks.
Like all large language models, GPT-4.1 can produce hallucinations—plausible but incorrect information—especially on obscure or ambiguous topics. It may also exhibit biases present in its training data. The model requires careful prompt engineering to achieve optimal results, and its performance on tasks outside its training distribution may degrade. No latency benchmarks are provided, so response times can vary. Users should verify critical outputs, particularly in high-stakes applications.
Latency details for GPT-4.1 are not published. In practice, processing very long inputs or multimodal requests can increase response time compared to short text prompts. The underlying OpenAI infrastructure and network conditions also affect latency. OrcaRouter provides standard API endpoints with typical response times expected for the model. For applications where low latency is critical, consider testing with representative inputs to gauge performance.
On OrcaRouter, GPT-4.1 is priced at $2.00 per 1 million input tokens and $8.00 per 1 million output tokens. This is the exact provider rate with zero markup. You only pay for the tokens actually consumed; there are no additional fees, monthly charges, or minimum commitments. Pricing is transparent and based on token count as determined by OpenAI's tokenizer.
No. OrcaRouter passes through the provider rate exactly. The price you see — $2.00 per 1M input and $8.00 per 1M output — is what you pay per token. There are no setup fees, subscription costs, or hidden surcharges. This straightforward pricing model allows you to predict costs accurately based on your usage volume.
Longer prompts increase input token counts, directly raising input costs. For a prompt using the full 1,047,576 token context, the input cost alone would be approximately $2.10 (since 1M tokens cost $2.00). Output tokens are billed separately at $8.00 per million. To manage costs, you can limit input length when possible, use shorter contexts for simpler tasks, or batch multiple requests.
OrcaRouter does not currently advertise caching or volume discounts for GPT-4.1. All pricing is per-token as listed. For very high-volume usage, it may be possible to negotiate custom arrangements directly with OrcaRouter. In the meantime, the zero-markup pricing means you pay the same rate as if you were using OpenAI directly, without any intermediary surcharge.
Use the base URL https://api.orcarouter.ai/v1 and specify the model ID "openai/gpt-4.1". The API is fully compatible with OpenAI's chat completions endpoint. A typical curl call: curl https://api.orcarouter.ai/v1/chat/completions -H "Authorization: Bearer YOUR_API_KEY" -d '{"model": "openai/gpt-4.1", "messages": [{"role": "user", "content": "Hello"}]}'. You can also use any OpenAI client library by changing the base URL and model name.
Standard OpenAI parameters are supported, including temperature (0-2), top_p, max_tokens (capped at 32,768), stop sequences, frequency_penalty, presence_penalty, and stream. The model uses the chat completions format. For multimodal requests, images can be included in the message content using the supported format (URL or base64). File inputs are also accepted, though details on file format limitations are not provided.
Migration is straightforward. Replace the model ID from, say, "gpt-4" to "openai/gpt-4.1" and update the base URL from OpenAI's default to https://api.orcarouter.ai/v1. Your existing client code for chat completions will work with these two changes. No other modifications are required. It's recommended to test a few requests to ensure that the model's behavior matches your expectations before switching production traffic.
Yes. You must provide a valid API key from OrcaRouter in the Authorization header. Keys are managed through your OrcaRouter account dashboard. Without a valid key, the API returns a 401 Unauthorized error. OrcaRouter's authentication follows the same pattern as OpenAI's API, making it easy to integrate into existing workflows that already use API keys.
Smaller models, such as GPT-4o mini, typically have smaller context windows (often 128K tokens or less) and may not support multimodal inputs. They are significantly cheaper per token — often 10-20 times lower in cost. GPT-4.1 offers the advantage of a 1,047,576 token context and image input support, which is essential for tasks that require deep understanding of very long or mixed-media content. For short, simple tasks, a smaller model is more economical.
Compared to other long-context models available on OrcaRouter, GPT-4.1 offers a competitive context window size and strong mathematical reasoning performance, as shown by its 91.3 MATH-500 score. While direct benchmark comparisons with models like Claude 3 Opus or Gemini 1.5 Pro are not provided here, the MATH-500 score is a meaningful indicator of quantitative reasoning ability. The ideal choice depends on specific task requirements, cost tolerance, and the importance of multimodal input support.
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-4.1",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)frequency_penaltylogit_biaslogprobsmax_completion_tokensmax_tokensnparallel_tool_callspredictionpresence_penaltyresponse_formatseedservice_tierstopstreamstructured_outputstemperaturetool_choicetoolstop_logprobstop_pweb_search_options| Input / 1M tokens | $2.00 |
|---|---|
| Output / 1M tokens | $8.00 |
| Cache read / 1M | $0.500 |
| Currency | USD |
Estimate based on list price
Estimate only — actual token counts depend on the provider's tokenizer.
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GET /api/public/models/openai/gpt-4.1Open @misc{orcarouter_gpt_4_1,
title = {GPT-4.1 API},
author = {OpenAI},
year = {2025},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/openai/gpt-4.1}
}OpenAI. (2025). GPT-4.1 API. OrcaRouter. https://www.orcarouter.ai/models/openai/gpt-4.1