The latest GPT-4 Turbo model with vision capabilities. Vision requests can now use JSON mode and function calling. Training data: up to December 2023.
GPT-4 Turbo is a large language model released by OpenAI that processes both text and image inputs. It builds on the architecture of GPT-4 but increases the context window to 128,000 tokens — roughly…
GPT-4 Turbo demonstrates strong reasoning capabilities, particularly in mathematics (MATH-500 score of 73.7), code generation, and multi-step problem-solving. It can follow complex instructions over long conversations, maintain consistency across thousands of tokens, and generate coherent technical explanations. The model is also capable of analyzing images — such as screenshots, diagrams, and printed text — when the input includes image data. However, it is not specialized for every domain; for simple classification or extraction tasks, a smaller model like GPT-3.5 Turbo may be sufficient and more economical.
A 128,000-token context window allows you to input large documents — entire books, lengthy legal contracts, complete code repositories, or long conversations — without needing to split content into chunks. For instance, you can paste a full research paper and ask questions about any section without losing the earlier parts. This is particularly valuable for tasks like document summarization, legislative analysis, or debugging a large codebase where the model needs to see the whole structure. On OrcaRouter, this context is billed as input tokens, so feeding a 100K-token document would cost approximately $1.00 per query (100K * $10/1M).
GPT-4 Turbo accepts images as part of the input, allowing it to understand visual content such as photographs, diagrams, illustrations, and screenshots. The model can describe what is in an image, answer questions about its contents, and even perform diagram reasoning (e.g., interpreting a flowchart or a graph). Images are typically provided as URLs or base64-encoded data within the OpenAI chat completion format. The cost for image processing is included in the input token count, which is calculated based on the image resolution and detail level as per OpenAI's pricing formula.
If your use case involves high-volume, low-complexity tasks — such as simple text classification, basic Q&A on short contexts, or repetitive extraction — a smaller model like GPT-3.5 Turbo or a dedicated fine-tuned model may yield acceptable results at a fraction of the cost. GPT-4 Turbo's pricing is roughly 20 times higher than GPT-3.5 Turbo per input token and 30 times higher per output token. For applications where latency matters, GPT-3.5 Turbo also responds faster. Evaluate the trade-off between accuracy and cost; for many production pipelines, a hybrid approach using a cheap model for filtering and GPT-4 Turbo for complex cases can optimize spend.
GPT-4 Turbo achieved a score of 73.7 on the MATH-500 benchmark, which evaluates a model's ability to solve grade-school to high-school-level mathematics problems spanning topics like algebra, geometry, and calculus. This score indicates strong mathematical reasoning but is not state-of-the-art; some specialized models or larger ensembles can exceed 80. The benchmark is useful for comparing models on systematic problem-solving rather than raw language generation. On OrcaRouter, you can test this yourself by submitting a set of math problems through the API and comparing results.
Strengths include deep reasoning, handling long contexts, and competence in generating and explaining code. It also shows good performance on tasks that combine text and vision, like interpreting diagrams. Limitations include a relatively modest output cap of 4,096 tokens, which means long-form generation (e.g., writing a full chapter) requires multiple calls. The model can sometimes produce incorrect answers in fringe cases — it is not infallible. Additionally, it may not be the best choice for real-time applications due to higher latency compared to smaller models. No speed measurements are provided, but anecdotal reports suggest it is slightly slower than GPT-3.5 Turbo.
Exact latency figures for GPT-4 Turbo on OrcaRouter are not published; performance depends on the provider's infrastructure and request load. In practice, the model's inference time is longer than smaller models due to its larger parameter count and context processing. For short inputs, response times are typically a few seconds, while very large contexts (e.g., 100K tokens) can increase latency significantly because the model must process all tokens before generating output. OrcaRouter does not advertise any specific acceleration. Users needing lower latency for interactive applications may prefer a faster model, whereas batch processing of complex tasks remains viable.
Pricing is per token: $10.00 per 1 million input tokens and $30.00 per 1 million output tokens. This matches OpenAI's direct rate, with no additional markup from OrcaRouter. Input tokens include the system message, user messages, image tokens, and any conversation history. Output tokens are the model's generated response. The total cost of a request is computed as (input_tokens * $0.00001) + (output_tokens * $0.00003). There are no minimum fees or monthly commitments; you pay only for the tokens you use.
GPT-4 Turbo is significantly more expensive than smaller models like GPT-3.5 Turbo ($0.50/$1.50 per 1M tokens) but offers superior reasoning and context handling. For tasks that only need basic capabilities, using GPT-4 Turbo may lead to unnecessary costs. On the other hand, compared to the original GPT-4 ($30/$60 per 1M tokens), GPT-4 Turbo is 33% cheaper on input and 50% cheaper on output, making it a more cost-effective option for high-performance requirements. OrcaRouter's zero-markup pricing ensures you pay the same rate as when using OpenAI directly.
OrcaRouter does not offer any specific discounts, volume pricing, or response caching for GPT-4 Turbo beyond the stated per-token rates. The pricing is straightforward: you pay exactly what OpenAI charges, with no added fees. There is no commitment discount, pre-purchase of tokens, or tiered pricing. Caching of prompts or responses is not advertised, so every request is billed at the standard rate. If you anticipate very high volumes, you may wish to contact OrcaRouter or consider using a dedicated API endpoint with your own caching layer to reduce repeated costs for identical inputs.
Output tokens cost three times more than input tokens ($30 vs. $10 per 1M). Therefore, long generations can quickly increase the bill. For example, generating a 2,000-token response would cost $0.06, while a 4,000-token response costs $0.12. To control costs, consider setting a lower max_tokens parameter, using shorter prompts, or employing iterative refinement where the model produces shorter outputs and then expands them in a separate call only when needed. For tasks such as summarization, a model with a smaller output token cost might be preferable if the context is not critical.
You can call GPT-4 Turbo via OrcaRouter's OpenAI-compatible API. First, obtain an API key from OrcaRouter. Then set the base URL to https://api.orcarouter.ai/v1 and use the model ID "openai/gpt-4-turbo" in your chat completion requests. For example, with the OpenAI Python SDK, you would create a client with base_url pointing to OrcaRouter and model="openai/gpt-4-turbo". The request and response formats are identical to those of OpenAI's native API, so minimal code changes are required to switch from direct OpenAI usage.
The API supports standard OpenAI chat completion parameters: messages (array with role and content), temperature (0-2), top_p, n, stream, stop, max_tokens (capped at 4096), presence_penalty, frequency_penalty, logit_bias, user, and function calling/tools. For image inputs, you can include a content part with type "image_url" and url. The model does not support additional parameters beyond OpenAI's specification. All parameters work exactly as documented for OpenAI's GPT-4 Turbo. Note that the max_tokens parameter cannot exceed 4096, which is the model's output limit.
Migration is straightforward: replace your OpenAI base URL with OrcaRouter's endpoint https://api.orcarouter.ai/v1 and change the model name to "openai/gpt-4-turbo". Use your OrcaRouter API key instead of your OpenAI API key. The rest of your code — message formatting, streaming, error handling — remains unchanged because the API is fully compatible. If you were using the OpenAI Python library, you can simply set openai.api_base (or the equivalent in newer versions) to the OrcaRouter URL. This allows you to test GPT-4 Turbo via OrcaRouter without rewriting any logic.
GPT-4 Turbo is an evolution of GPT-4 with a larger context window (128K vs. up to 32K in earlier GPT-4 variants) and cheaper per-token pricing: $10/$30 per 1M tokens vs. $30/$60 per 1M tokens for GPT-4. Both models support vision, but GPT-4 Turbo also improves upon efficiency and minor reasoning quality. In benchmarks like MATH-500, GPT-4 Turbo scores 73.7, while the original GPT-4 (with context 8K) scored around 52.9 on a smaller MATH test set — the numbers are not directly comparable due to different test versions, but the improvement is indicative. On OrcaRouter, the older GPT-4 models are also available at their respective pricing.
GPT-3.5 Turbo is far cheaper ($0.50/$1.50 per 1M tokens) and faster, making it suitable for high-volume, low-latency applications. However, it has a smaller context window (16K or 4K depending on variant) and significantly weaker reasoning, code generation, and vision capabilities. For tasks that require multi-step reasoning or large context, GPT-4 Turbo clearly outperforms. In a direct comparison on mathematical reasoning, GPT-3.5 Turbo typically scores below 30 on MATH-500, whereas GPT-4 Turbo achieves 73.7. Choose GPT-3.5 Turbo for simple tasks and cost savings, but rely on GPT-4 Turbo when accuracy or context length is critical.
A direct head-to-head comparison is not provided, but general public knowledge shows that Anthropic Claude 3 and Google Gemini 1.5 offer similar capabilities. Claude 3 Opus has a 200K context window and comparable reasoning, while Gemini 1.5 Pro can handle up to 1M tokens. However, each model has different pricing and performance profiles. On OrcaRouter, you can also access other providers' models for comparison. GPT-4 Turbo remains competitive for its balance of price, reasoning quality, and developer ecosystem (OpenAI SDK). For vision tasks, both Claude and Gemini also support image input.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1from openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key="$ORCAROUTER_API_KEY",
)
response = client.chat.completions.create(
model="openai/gpt-4-turbo",
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-turboOpen @misc{orcarouter_gpt_4_turbo,
title = {GPT-4 Turbo API},
author = {OpenAI},
year = {2024},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/openai/gpt-4-turbo}
}OpenAI. (2024). GPT-4 Turbo API. OrcaRouter. https://www.orcarouter.ai/models/openai/gpt-4-turbo