DeepSeek alias for V4 Flash non-thinking mode — 1M context, strong instruction following and coding (legacy alias, slated for deprecation).
DeepSeek V3 is a Mixture-of-Experts text model from DeepSeek, designed for tasks that require understanding and generating over very long contexts. Its 1,048,576-token context window allows…
DeepSeek V3 excels at reasoning over long contexts due to its 1M token window. It can maintain coherence across hundreds of pages of text, making it ideal for summarizing entire documents, following complex narratives, or analyzing large code repositories. The MoE architecture allows it to delegate different parts of a task to specialized ‘expert’ subnetworks, improving efficiency. It also supports a high output limit of 384k tokens, enabling generation of lengthy reports, books, or multi-turn dialogue with extensive responses. It is particularly strong in mathematical reasoning and code generation, areas where DeepSeek has focused training.
For simple tasks like short-form Q&A, classification, or light summarization, a smaller model (e.g., Llama 3.1 8B or GPT-4o mini) may be more cost-effective and faster. DeepSeek V3 is optimized for long contexts and high output; using it for a 100-token answer wastes its capacity. If real-time latency is critical and context is short, consider a model with lower overhead. Additionally, if you need multimodal input, DeepSeek V3 is not suitable.
Best use cases include processing very long documents (e.g., legal contracts, research papers, entire books) where the entire context must be considered. It is also effective for multi-turn chat applications that maintain conversation history up to 1M tokens, such as advanced customer support or interactive storytelling. Code generation and analysis across massive codebases benefit from the large context. Additionally, tasks requiring long-form generation like report writing, article creation, or structured data generation (e.g., JSON, XML) can fully utilize the 384k output limit.
Specific benchmark scores for DeepSeek V3 are not provided in this listing. However, publicly available information from DeepSeek indicates that V3 achieves competitive results on reasoning benchmarks (e.g., MATH, GSM8K), coding benchmarks (e.g., HumanEval, MBPP), and language understanding tasks (e.g., MMLU). Its MoE architecture allows it to perform similarly to dense models with many more total parameters while using less compute per token. Users should consult the official DeepSeek paper for detailed numbers.
Latency depends on the input length, output length, and current load. Because DeepSeek V3 uses a Mixture-of-Experts architecture, it activates only a subset of parameters per token, which generally results in faster generation compared to a dense model of the same total parameter count. On OrcaRouter, latency is also influenced by network conditions and load balancing. For short contexts, the model responds quickly; for long context processing, the initial encoding time scales with input length. No specific latency figures are provided, but users can expect reasonable performance for a model of its size.
Strengths include a massive context window (1M tokens), high output limit (384k tokens), MoE efficiency leading to lower cost per token, and strong reasoning/coding performance. Limitations: text-only input (no images, audio), potential for reduced depth of knowledge compared to larger dense models, and the model may not be ideal for very short tasks where it is overkill. Additionally, its behavior on highly nuanced tasks (e.g., creative writing, emotional tone) may vary; user testing is recommended.
Pricing is $0.14 per 1 million input tokens and $0.28 per 1 million output tokens. These rates are billed at the provider’s rate with no additional markup from OrcaRouter. Input tokens include the prompt; output tokens are the model’s generated text. For example, a 500,000-token input and a 100,000-token output would cost $0.07 (input) + $0.028 (output) = $0.098. Note that tokens are counted by the provider’s tokenizer.
Given its large context window, costs can add up if you always use the full 1M tokens. However, for many use cases, the average input size is smaller. The per-token cost is competitive, especially compared to dense models of similar capability. Because it’s an MoE model, compute cost per token is lower, which OrcaRouter passes on as zero markup. If your task requires only a few hundred tokens, a cheaper model may be more economical. For long-context tasks, DeepSeek V3 often provides the best cost-performance ratio.
OrcaRouter does not separately advertise caching discounts for DeepSeek V3. Caching, if any, would follow the provider’s (DeepSeek) policies, which may or may not apply. Users should assume standard per-token billing. For cost optimization, consider reusing context windows efficiently by trimming unnecessary inputs.
Use OrcaRouter’s OpenAI-compatible API with base URL https://api.orcarouter.ai/v1. Set the model ID to "deepseek/deepseek-chat". You can use the official OpenAI Python client or any library that supports OpenAI’s chat completions. Example in Python: ```python import openai client = openai.OpenAI(base_url="https://api.orcarouter.ai/v1", api_key="YOUR_ORCAROUTER_API_KEY") response = client.chat.completions.create(model="deepseek/deepseek-chat", messages=[{"role":"user","content":"Hello"}]) ```
The model supports standard chat completion parameters: temperature, top_p, max_tokens, stop, frequency_penalty, presence_penalty, and others. max_tokens can be set up to 384,000. The model is text-only, so no image or audio inputs. For long contexts, you can send large message arrays; ensure total tokens do not exceed 1,048,576. The tokenizer is the same as DeepSeek’s own; OrcaRouter handles token counting as per the provider.
Change the base_url to https://api.orcarouter.ai/v1 and the model ID to "deepseek/deepseek-chat". Keep your existing code structure (messages, parameters). No other changes are required if you’re using the OpenAI Python client or similar. Ensure your API key is valid for OrcaRouter. Test with a small request to verify token limits and pricing. For applications using streaming, the response format is identical to OpenAI’s streaming.
GPT-4o supports text, image, and audio input; DeepSeek V3 is text-only. GPT-4o has a 128k context window, while DeepSeek V3 supports 1M. GPT-4o’s pricing varies but is generally higher per token. DeepSeek V3’s MoE architecture may yield lower latency for long contexts. On reasoning and coding, both are strong, but GPT-4o has broader multimodal capabilities. Choose DeepSeek V3 if you need extreme context length and efficient text processing; choose GPT-4o for multimodal tasks.
Claude 3.5 Sonnet offers a 200k context window, significantly smaller than DeepSeek V3’s 1M. Claude supports text and image input; DeepSeek V3 is text-only. Claude’s pricing is higher per token (e.g., $3 per million input). DeepSeek V3 is cheaper. Claude is known for strong instruction following and safety; DeepSeek V3 excels in mathematics and coding. For long-context tasks, DeepSeek V3 is more cost-effective and offers larger capacity.
Llama 3.1 405B is a dense model with 128k context window; DeepSeek V3’s context is much larger. Llama 3.1 405B is text-only as well. Pricing for Llama 3.1 405B via hosted services is typically higher than DeepSeek V3. DeepSeek V3’s MoE architecture uses fewer active parameters, potentially faster for generation. Both are strong in reasoning; DeepSeek V3 may have an edge in long-context recall due to its extended window. Choose DeepSeek V3 for extreme context lengths; Llama 3.1 for open-weight access or specific fine-tuned variants.
Use DeepSeek V3 when your task demands processing very long contexts (e.g., entire books, large codebases) or generating long outputs (up to 384k tokens). If your task is short, a smaller model like DeepSeek V2 Lite or Llama 3.1 8B will be faster and cheaper. Also, if you need multimodal input, consider other models. The cost-benefit ratio favors DeepSeek V3 for any task where the context exceeds 128k tokens or where output length beyond typical limits is required.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1from openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key="$ORCAROUTER_API_KEY",
)
response = client.chat.completions.create(
model="deepseek/deepseek-chat",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)include_reasoninglogprobsmax_tokensreasoningresponse_formatstopstreamstream_optionstemperaturethinkingtool_choicetoolstop_logprobstop_puser_id| Input / 1M tokens | $0.147 |
| Output / 1M tokens | $0.295 |
| Cache read / 1M | $0.020 |
| Currency | USD |
Estimate based on list price
Estimate only — actual token counts depend on the provider's tokenizer.
GET /api/public/models/deepseek/deepseek-chatOpen @misc{orcarouter_deepseek_chat,
title = {DeepSeek V3 API},
author = {DeepSeek},
year = {2024},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/deepseek/deepseek-chat}
}DeepSeek. (2024). DeepSeek V3 API. OrcaRouter. https://www.orcarouter.ai/models/deepseek/deepseek-chat