DeepSeek V4 Pro flagship MoE — 1.6T total / 49B active params, 1M context, top-tier reasoning + agentic tool use.
DeepSeek V4 Pro is the flagship text-generation model offered by DeepSeek and available through OrcaRouter's API. It is designed for tasks that require processing very large amounts of token…
DeepSeek V4 Pro excels at tasks requiring comprehension of long texts, such as summarizing entire books, answering questions across thousands of tokens of context, and extracting structured data from lengthy documents. Its large output limit enables generation of verbose analyses, code, or creative writing that spans hundreds of thousands of tokens. Because it is text-only, its capabilities are confined to text-based reasoning, generation, and instruction-following. It does not support vision, audio, or other modalities. The model’s high τ²-Bench score (96.2) suggests strong performance in agentic settings where the model must use external tools or APIs to complete tasks.
While DeepSeek V4 Pro offers enormous context and output capacity, it comes with a higher per-token cost than more compact models. For tasks that require only a few hundred tokens of context and output, using this flagship model may be wasteful. Typical scenarios where a smaller, cheaper model suffices include single-turn classification, short-form summarization, or simple translation of brief passages. If your application does not benefit from the full 1M context window or the 384K output limit, you can reduce latency and cost by selecting a model from a lower tier. OrcaRouter offers a range of models; consider a non-flagship model for everyday queries.
The model is ideal for applications that involve processing entire codebases for refactoring or documentation, analyzing multi-hundred-page legal or regulatory documents, and maintaining coherent conversations over many turns where the full history is needed. It also suits generating long-form content such as detailed technical manuals, novels, or comprehensive research reviews. Its ability to output up to 384,000 tokens in a single response makes it one of the few models capable of producing very large structured outputs without chunking. For agentic workflows that require tool use across long contexts, the τ²-Bench score indicates strong reliability.
DeepSeek V4 Pro supports only text input and output. It cannot process or generate images, audio, video, or other non-text formats. If your application requires multimodal capabilities—such as analyzing a chart or transcribing speech—you will need to use a different model that supports those modalities or combine DeepSeek V4 Pro with separate external processors. Within its text-only domain, the model is designed to handle very large token counts, making it suitable for tasks where the input or output is primarily textual and lengthy.
The headline benchmark provided for DeepSeek V4 Pro is 96.2 on τ²-Bench. τ²-Bench evaluates a model’s ability to use tools and follow instructions in an agentic environment, simulating tasks that require the model to call functions, interpret results, and iterate. A score of 96.2 indicates high accuracy and reliability in such settings. No other benchmark scores (e.g., MMLU, HumanEval) are specified for this model, so direct comparisons should be limited to τ²-Bench performance. Users interested in other dimensions (reasoning, coding, etc.) may need to consult public third-party evaluations.
Latency depends on the length of the input and output, the server load, and the specific request parameters. OrcaRouter routes requests to DeepSeek’s infrastructure, and typical response times for long contexts are higher than for short ones. Because the model can output up to 384,000 tokens, generation may take minutes for very long responses. For real-time applications requiring sub-second latency, consider using a smaller model with shorter outputs. OrcaRouter does not publish standard latency benchmarks; you can estimate performance by running test requests with representative payloads.
Based on its specifications, DeepSeek V4 Pro’s primary strengths include extremely large context and output capacities, combined with strong agentic performance as measured by τ²-Bench. The 1M token context window allows the model to retain and process entire textbooks or lengthy codebases in a single pass, reducing the need for chunking or retrieval-augmented generation. The 384K output limit enables generation of very long, coherent texts without truncation. These characteristics make it particularly valuable for tasks that require breadth and depth simultaneously.
The model is text-only, so it cannot handle multimodal inputs or outputs. Its pricing per token is higher than smaller models, making it uneconomical for short-context tasks. While τ²-Bench performance is strong, no information is provided on other standard benchmarks (e.g., reasoning, multilingual, coding), so its general capabilities outside agentic tool use are not quantified here. Users should also be aware that very long outputs can incur significant costs and latency. Additionally, the model may produce incorrect or biased responses, as with all large language models.
Pricing is straightforward: $0.44 per 1 million input tokens and $0.87 per 1 million output tokens. These rates are set by DeepSeek and passed through by OrcaRouter with zero markup. Both input and output tokens are counted according to the provider’s tokenizer. There are no additional platform fees, usage tiers, or volume discounts specified. The total cost for a request equals (input_tokens * $0.44/1M) + (output_tokens * $0.87/1M). For example, a request with 100K input tokens and 50K output tokens would cost approximately $0.044 + $0.0435 = $0.0875.
No information has been provided about caching discounts or prompt caching for DeepSeek V4 Pro. OrcaRouter does not add markup, but it is unknown whether DeepSeek offers reduced rates for repeated prompt prefixes or cached context. Users should assume that every generated token is billed at the standard per-token rate. For applications with high repetition in prompts, consider evaluating whether a different provider or model with explicit caching support could lower costs. As of this writing, the pricing model is purely per-token consumption with no tiers.
The per-token cost of DeepSeek V4 Pro is higher than many smaller or older models available through OrcaRouter. For instance, a lightweight model might cost one-tenth as much per token. If your task uses only a small fraction of the context window (e.g., 4K tokens) and generates short outputs, you will pay more than necessary. The flagship model becomes cost-effective when the larger context or output size directly reduces the number of API calls or the need for external retrieval systems. For high-volume, short-context applications, a cheaper model will significantly lower your bill.
OrcaRouter states that DeepSeek V4 Pro is billed at the provider rate with zero markup. This means the price you pay per token is exactly what OrcaRouter pays DeepSeek, with no added margin. This policy applies to all models listed on the platform. The transparency allows you to compare costs directly against other providers without worrying about hidden fees. However, rates may change if DeepSeek updates its pricing; OrcaRouter is expected to pass those changes through without alteration.
Use the OpenAI-compatible chat completions endpoint: POST https://api.orcarouter.ai/v1/chat/completions. Set the 'model' parameter to 'deepseek/deepseek-v4-pro'. Include your API key in the Authorization header as 'Bearer YOUR_API_KEY'. Standard parameters such as 'messages', 'temperature', 'max_tokens', 'top_p', 'stop', and 'frequency_penalty' are supported. For example, setting 'max_tokens' to 384000 will allow the model to generate up to that many tokens. Refer to OrcaRouter’s documentation for any additional supported parameters. The response follows the same format as the OpenAI API.
All standard chat completion parameters are available: 'messages' (required array of message objects with 'role' and 'content'), 'temperature' (0-2, default likely 1), 'top_p' (0-1), 'max_tokens' (up to 384000), 'stop' (string or array of strings), 'frequency_penalty' (-2 to 2), 'presence_penalty' (-2 to 2), 'seed' (integer for deterministic sampling), and 'stream' (boolean). Note that 'max_tokens' cannot exceed the model’s maximum output of 384000 tokens; sending a higher value will be clipped or return an error. The model identifier must be exactly 'deepseek/deepseek-v4-pro'. No additional provider-specific parameters have been disclosed.
If you are using the OpenAI Python client library, migration requires only two changes: set the base URL to 'https://api.orcarouter.ai/v1' and update the model name to 'deepseek/deepseek-v4-pro'. Your existing code that uses the 'openai.ChatCompletion.create()' or the newer client API should work with these modifications. Ensure you have an OrcaRouter API key. The request and response schemas are identical to OpenAI’s, so no other changes are needed. For other programming languages (JavaScript, Java, curl), update the endpoint URL and model field accordingly.
The base URL for all API requests is https://api.orcarouter.ai/v1. The exact model ID to use in the 'model' field is 'deepseek/deepseek-v4-pro'. This ID is case-sensitive and must be provided exactly as shown. Requests to any other endpoint or using an incorrect model ID will result in an error. OrcaRouter’s API supports both streaming and non-streaming modes. For streaming, set 'stream': true in the request body, and you will receive SSE events in the same format as OpenAI’s streaming.
Compared to other flagship models available through OrcaRouter, DeepSeek V4 Pro offers one of the largest context windows (1M tokens) and output limits (384K tokens). Its τ²-Bench score of 96.2 is a direct point of comparison. However, without benchmark data for other models on the same metric, direct ranking is not possible. Many other flagship models support multimodal inputs, which DeepSeek V4 Pro does not. Cost per token varies; some competitors may have lower per-token prices but smaller context windows. The choice depends on whether you need the extremely large context and output capacities or multimodal capabilities.
If your application requires vision (image understanding) or audio processing, you must choose a multimodal model. Similarly, if your tasks are typically short (<10K tokens) and do not require agentic tool use, a cheaper general-purpose flagship may be more cost-effective. Some competitors may offer faster inference for short contexts or lower latency. DeepSeek V4 Pro’s strength lies in scenarios where the long context and output are essential. If your use case involves processing many separate short documents, a model with a smaller context window but lower per-token price could be more economical.
DeepSeek offers several models. DeepSeek V4 Pro is the flagship, with the largest context and highest cost. Smaller DeepSeek models may have context windows of 32K or 128K tokens and lower prices, making them more suitable for typical workloads. If you are already using a DeepSeek model and need more context capacity or better agentic performance, upgrading to V4 Pro is the logical step. However, for most tasks that do not require the max context, a lower-tier DeepSeek model will provide similar quality at reduced cost. Check OrcaRouter’s catalog for available DeepSeek models.
τ²-Bench measures a model’s ability to use tools in an agentic setting. A score of 96.2 suggests DeepSeek V4 Pro is highly reliable at correctly calling functions, parsing results, and following multi-step instructions. When comparing with other models, if they have a τ²-Bench score, you can directly compare. If not, you may need to evaluate based on other benchmarks or qualitative testing. For applications that do not involve tool use, the τ²-Bench score is less relevant. In those cases, consider other metrics like reasoning, coding, or language understanding if available.
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="deepseek/deepseek-v4-pro",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)include_reasoninglogprobsmax_tokensreasoningresponse_formatstopstreamstream_optionstemperaturethinkingtool_choicetoolstop_logprobstop_puser_id| Input / 1M tokens · Off-peak | $0.442 |
| Output / 1M tokens · Off-peak | $0.884 |
| Cache read / 1M · Off-peak | $0.060 |
| Peak hours | 01:00–04:00, 06:00–10:00 ×2 (UTC) |
| Input / 1M tokens · ×2 | $0.884 |
| Output / 1M tokens · ×2 | $1.768 |
| Cache read / 1M · ×2 | $0.120 |
| Currency | USD |
Estimate based on list price
Estimate only — actual token counts depend on the provider's tokenizer.
What developers are saying this week
GET /api/public/models/deepseek/deepseek-v4-proOpen @misc{orcarouter_deepseek_v4_pro,
title = {DeepSeek V4 Pro API},
author = {DeepSeek},
year = {2026},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/deepseek/deepseek-v4-pro}
}DeepSeek. (2026). DeepSeek V4 Pro API. OrcaRouter. https://www.orcarouter.ai/models/deepseek/deepseek-v4-pro