OpenAI's GPT‑4 Turbo with 128K context, text/image input, accessed via OrcaRouter's API.
openai/gpt-4-turbo-2024-04-09 is a specific version of OpenAI's GPT-4 Turbo model, released on April 9, 2024. It accepts both text and image inputs and offers a context window of 128,000 tokens,…
The model can perform a wide range of natural language tasks: summarization, translation, question answering, code generation, and creative writing. With its 128K token context, it can analyze entire books or large codebases in one go. It also processes images, so it can answer questions about photographs, diagrams, or scanned documents. The model's strong performance on MATH-500 (73.7) indicates it can solve complex math problems step-by-step. For best results, provide clear instructions and use the system message to set behavior. The model is accessed via OrcaRouter's API at https://api.orcarouter.ai/v1 with model ID "openai/gpt-4-turbo-2024-04-09".
GPT-4 Turbo is powerful but relatively expensive at $10 per million input tokens and $30 per million output tokens. For tasks that don't require deep reasoning or extensive context, a lighter model like GPT-3.5 Turbo (priced around $0.5–$1.5 per million tokens) may be sufficient and far more economical. Examples include simple text classification, basic chatbots, or short-form content generation. Additionally, if your application does not need multimodal input or a very large context window, a smaller model can reduce latency and cost. OrcaRouter allows you to switch between models by changing the model ID in the API call.
Yes, GPT-4 Turbo (2024-04-09) accepts image inputs in addition to text. You can provide images as base64-encoded data or URLs within the API request. The model can interpret the content of photographs, diagrams, charts, and text in images (e.g., screenshots). This makes it suitable for vision tasks like image captioning, visual question answering, and document analysis that includes scanned pages. When using images, token cost includes the image's visual tokens—typically each image consumes tokens proportional to its resolution. The exact token calculation is defined by OpenAI; refer to their documentation for details. Through OrcaRouter, you send the same request format as the OpenAI API.
The 128K token context window (approximately 96,000 words) is ideal for tasks that require understanding of very long sequences. For example, processing an entire book or lengthy legal document in one API call, analyzing a full code repository, or maintaining a conversation history covering hundreds of turns. It also enables techniques like "chain-of-thought" prompting over long reasoning traces. However, note that the model's attention complexity can increase latency for very long inputs. For most production applications, a context window of 8K–32K is often sufficient; leverage the full 128K only when your task genuinely benefits from the extended memory.
The MATH-500 benchmark consists of 500 challenging mathematics problems spanning algebra, geometry, number theory, and more. A score of 73.7 indicates that GPT-4 Turbo correctly solved approximately 73.7% of these problems. This is a strong performance, placing it among the top models for mathematical reasoning. For context, earlier GPT-4 models scored lower on similar math benchmarks. The score suggests the model can reliably handle step-by-step reasoning, which is useful for tutoring systems, automated math checking, and complex data analysis. Be aware that performance may vary by problem domain; the model may still make mistakes on highly specialized or ambiguous questions.
Exact latency figures are not published by OpenAI for this model. Generally, GPT-4 Turbo is faster than the original GPT-4 but slower than small models like GPT-3.5 Turbo or GPT-4o Mini. Actual response time depends on input length, output length, request volume, and server load. OrcaRouter optimizes routing to OpenAI's endpoints, but does not add additional latency beyond the network hop. For latency-sensitive applications, consider using a faster model with lower total token usage. If you need immediate responses, you may test the model via OrcaRouter's API and measure performance for your specific workload.
Like all large language models, GPT-4 Turbo can sometimes generate incorrect or nonsensical information (hallucination). It may also be verbose, producing longer answers than necessary. While strong on math, it may struggle with factual accuracy on recent events (training data cutoff not specified; assume knowledge up to early 2024). The model does not support function calling in the same way as newer versions, though it accepts tool-use patterns in the request format. It also does not guarantee consistent formatting across calls. For safety-critical tasks, always validate outputs. OrcaRouter provides the model as-is with no additional filtering.
OrcaRouter passes through OpenAI's pricing with zero markup. The cost is $10.00 per 1 million input tokens and $30.00 per 1 million output tokens. Input tokens include both text and image tokens (image token count is determined by OpenAI's algorithm). Output tokens are those generated by the model. Because there is no markup, the price you see is the exact OpenAI charge. Billing is based on token usage recorded by OrcaRouter. There are no additional fees or minimums. You can monitor your usage in the OrcaRouter dashboard and set spending limits.
OrcaRouter does not offer token caching for this model; every API request is billed based on the actual token count. There are no volume discounts or committed-use discounts at this time. The pricing is strictly per-token as described. To reduce costs, you can optimize your prompts to use fewer tokens (e.g., shorter system messages, truncating unneeded context). Alternatively, for tasks that don't require the full power of GPT-4 Turbo, consider using a cheaper model available on OrcaRouter, such as GPT-3.5 Turbo or GPT-4o Mini.
Output tokens are three times more expensive than input tokens ($30 vs. $10 per million). Therefore, generating long responses significantly increases the total cost. For cost-sensitive applications, consider limiting the max_tokens parameter to the minimum necessary length. Also, note that prompts with many images can consume a large number of input tokens (each image may consume hundreds of tokens). Always estimate token usage before scaling. A single 128K token input would cost $1.28 just for input, plus $3.84 for 128K output (if generated). In practice, typical requests use far fewer tokens.
You can use OpenAI's tokenizer or OrcaRouter's integrated token counting. For text, 1 token ≈ 0.75 words in English. For images, token consumption depends on image size and detail level; OpenAI's documentation provides formulas. You can also send a small sample request and inspect the usage field in the API response (which includes prompt_tokens, completion_tokens, and total_tokens). Multiply by the per-token prices to get the cost. OrcaRouter also shows per-request costs in the logs. Keep in mind that the maximum output is 4,096 tokens, so the output cost per request is capped at $0.12288 (4,096 * $30/1,000,000).
Use OrcaRouter's OpenAI-compatible API endpoint: https://api.orcarouter.ai/v1. Set the model parameter to "openai/gpt-4-turbo-2024-04-09". Include your OrcaRouter API key in the Authorization header (Bearer your_key). The request format is identical to OpenAI's Chat Completions API. Example using Python with openai library: set openai.base_url = "https://api.orcarouter.ai/v1" and openai.api_key = "orcarouter_key". Then call openai.chat.completions.create(model="openai/gpt-4-turbo-2024-04-09", messages=[...]). You can pass standard parameters like temperature, top_p, max_tokens (max 4096).
All standard OpenAI Chat Completions parameters are supported, including: temperature (0-2, default 1), top_p (0-1, default 1), max_tokens (up to 4096), n (number of completions), stop sequences, frequency_penalty, presence_penalty, and logit_bias. For multimodal requests, include a content array with type "text" and "image_url". OrcaRouter passes these parameters directly to OpenAI's API. Note that some advanced features like function calling may work but are not officially documented for this model version; test to confirm. You can also stream responses by setting stream=True, which returns server-sent events.
Migrating is straightforward: change the base URL from https://api.openai.com/v1 to https://api.orcarouter.ai/v1 and replace your API key with your OrcaRouter key. Update the model name to "openai/gpt-4-turbo-2024-04-09". All message formatting, system prompts, tool definitions, etc., remain the same. OrcaRouter acts as a transparent gateway, so the responses are identical to what OpenAI would return (as long as the model and parameters are the same). You can test the migration by sending a few requests and comparing outputs. No changes to your prompt engineering are required.
Compared to the original GPT-4 (released March 2023), GPT-4 Turbo offers several improvements: a larger context window (128K vs. 8K/32K), lower pricing ($10/$30 vs. ~$30/$60 per million tokens), and faster response times. The MATH-500 score of 73.7 is significantly higher than earlier GPT-4 scores on similar benchmarks. However, some users report that GPT-4 Turbo can be slightly less consistent in following format instructions compared to GPT-4. For most tasks, GPT-4 Turbo is the recommended choice unless you specifically need GPT-4's behavior. Through OrcaRouter, you can access both models and compare outputs directly.
GPT-4o (OpenAI's later multimodal model) offers native multimodal capabilities, faster speeds, and improved vision understanding. It also has a 128K context window. GPT-4o is generally cheaper than GPT-4 Turbo ($5/$15 per million tokens). On MATH-500, GPT-4o typically scores higher. GPT-4 Turbo remains a strong choice if you need the original GPT-4 Turbo's specific behavior, or if you've already optimized your prompts for it. On OrcaRouter, you can switch between these models by changing the model ID. We recommend testing both on your use case to determine which yields better accuracy/cost balance.
Choose openai/gpt-4-turbo-2024-04-09 if you need a reliable, high-performing model with a very large context window and you are willing to pay a premium for superior reasoning. It is especially strong for math tasks (MATH-500 73.7). If your application requires multimodal input, both GPT-4 Turbo and GPT-4o support it, but GPT-4o may be faster and cheaper. For simple text tasks, consider GPT-3.5 Turbo or GPT-4o Mini. OrcaRouter provides a wide catalog; evaluate cost, latency, and quality on your specific data before committing.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1from openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key="$ORCAROUTER_API_KEY",
)
response = client.chat.completions.create(
model="openai/gpt-4-turbo-2024-04-09",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)frequency_penaltylogit_biaslogprobsmax_tokenspresence_penaltyresponse_formatseedstopstreamtemperaturetool_choicetoolstop_logprobstop_p| Input / 1M tokens | $10.00 |
| Output / 1M tokens | $30.00 |
| Currency | USD |
Estimate based on list price
Estimate only — actual token counts depend on the provider's tokenizer.
GET /api/public/models/openai/gpt-4-turbo-2024-04-09Open @misc{orcarouter_gpt_4_turbo_2024_04_09,
title = {openai/gpt-4-turbo-2024-04-09 API},
author = {openai},
year = {n.d.},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/openai/gpt-4-turbo-2024-04-09}
}openai. (n.d.). openai/gpt-4-turbo-2024-04-09 API. OrcaRouter. https://www.orcarouter.ai/models/openai/gpt-4-turbo-2024-04-09