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DeepSeek Model Family

DeepSeek develops advanced foundation models optimized for computational efficiency and strong generalization across diverse tasks. The architecture incorporates recent advances in transformer-based systems, delivering robust performance in both zero-shot and fine-tuned scenarios. Models are pretrained on rigorously filtered multilingual corpora with specialized optimizations for mathematical reasoning and algorithmic tasks. The inference stack achieves competitive throughput while maintaining low latency, making it suitable for production deployment. Researchers and engineers can leverage these models for tasks ranging from natural language processing to complex analytical problem-solving.

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Featured Model: deepseek-ai/DeepSeek-V4.1-Flash-1017

DeepSeek-V4.1-Flash-1017 is a minor update to DeepSeek-V4.1-Flash, delivering substantial improvements across reasoning, coding, agentic tasks, and visual benchmarks. Alongside these gains, the model reduces task execution time and output length, enabling users to complete tasks faster and at lower cost and advancing the performance–cost Pareto frontier across multiple agent harnesses.

Price per 1M input tokens

$0.098

$0.20

51% off

Price per 1M cached input tokens

$0.0029

$0.006

Price per 1M output tokens

$0.294

$0.60

Release Date

10/17/2026

Context Size

1,048,576

Quantization

fp8

# Assume openai>=1.0.0
import os

from openai import OpenAI

# Create an OpenAI client with your deepinfra token and endpoint
openai = OpenAI(
    api_key=os.environ["DEEPINFRA_API_KEY"],
    base_url="https://api.deepinfra.com/v1/openai",
)

chat_completion = openai.chat.completions.create(
    model="deepseek-ai/DeepSeek-V4.1-Flash-1017",
    messages=[{"role": "user", "content": "Hello"}],
)

print(chat_completion.choices[0].message.content)
print(chat_completion.usage.prompt_tokens, chat_completion.usage.completion_tokens)

# Hello! It's nice to meet you. Is there something I can help you with, or would you like to chat?
# 11 25
copy

Featured Model: deepseek-ai/DeepSeek-V4-Pro-0813

DeepSeek-V4-Pro-0813 is the official release of DeepSeek-V4-Pro, superseding the preview version, with greatly enhanced agentic capabilities and performance improvements that are especially pronounced in production environments. It is built on the DeepSeek-V4-Pro (Preview) model structure, with a DSpark speculative decoding module attached.

Price per 1M input tokens

$1.30

Price per 1M cached input tokens

$0.10

Price per 1M output tokens

$2.60

Release Date

08/14/2026

Context Size

1,048,576

Quantization

fp8

# Assume openai>=1.0.0
import os

from openai import OpenAI

# Create an OpenAI client with your deepinfra token and endpoint
openai = OpenAI(
    api_key=os.environ["DEEPINFRA_API_KEY"],
    base_url="https://api.deepinfra.com/v1/openai",
)

chat_completion = openai.chat.completions.create(
    model="deepseek-ai/DeepSeek-V4-Pro-0813",
    messages=[{"role": "user", "content": "Hello"}],
)

print(chat_completion.choices[0].message.content)
print(chat_completion.usage.prompt_tokens, chat_completion.usage.completion_tokens)

# Hello! It's nice to meet you. Is there something I can help you with, or would you like to chat?
# 11 25
copy

Featured Model: deepseek-ai/DeepSeek-V4.1-Flash

Introduce DeepSeek-V4.1-Flash, a multimodal Mixture-of-Experts (MoE) model with 552B backbone parameters and support for contexts of up to one million tokens.

Price per 1M input tokens

$0.14

$0.20

30% off

Price per 1M cached input tokens

$0.0042

$0.006

Price per 1M output tokens

$0.42

$0.60

Release Date

10/6/2026

Context Size

1,048,576

Quantization

fp8

# Assume openai>=1.0.0
import os

from openai import OpenAI

# Create an OpenAI client with your deepinfra token and endpoint
openai = OpenAI(
    api_key=os.environ["DEEPINFRA_API_KEY"],
    base_url="https://api.deepinfra.com/v1/openai",
)

chat_completion = openai.chat.completions.create(
    model="deepseek-ai/DeepSeek-V4.1-Flash",
    messages=[{"role": "user", "content": "Hello"}],
)

print(chat_completion.choices[0].message.content)
print(chat_completion.usage.prompt_tokens, chat_completion.usage.completion_tokens)

# Hello! It's nice to meet you. Is there something I can help you with, or would you like to chat?
# 11 25
copy

Featured Model: deepseek-ai/DeepSeek-V4-Flash-Vision-Exp

DeepSeek-V4-Flash-Vision-Exp is DeepSeek's experimental multimodal model in the V4-Flash family, adding visual understanding to the V4-Flash architecture. It serves a 1M-token (1,048,576) context window and supports image input with visual grounding, tool calling, structured/JSON output, and configurable reasoning effort (low/high/max, or disabled).

Price per 1M input tokens

$0.2156

$0.44

51% off

Price per 1M cached input tokens

$0.0069

$0.014

Price per 1M output tokens

$0.6468

$1.32

Release Date

09/3/2026

Context Size

1,048,576

Quantization

fp8

# Assume openai>=1.0.0
import os

from openai import OpenAI

# Create an OpenAI client with your deepinfra token and endpoint
openai = OpenAI(
    api_key=os.environ["DEEPINFRA_API_KEY"],
    base_url="https://api.deepinfra.com/v1/openai",
)

chat_completion = openai.chat.completions.create(
    model="deepseek-ai/DeepSeek-V4-Flash-Vision-Exp",
    messages=[{"role": "user", "content": "Hello"}],
)

print(chat_completion.choices[0].message.content)
print(chat_completion.usage.prompt_tokens, chat_completion.usage.completion_tokens)

# Hello! It's nice to meet you. Is there something I can help you with, or would you like to chat?
# 11 25
copy

Available DeepSeek Models

DeepSeek's models are a suite of advanced AI systems that prioritize efficiency, scalability, and real-world applicability.

Model	Context	$ per 1M input tokens	$ per 1M output tokens	Actions
DeepSeek-V4-Flash-0731	1024k	$0.06 / $0.015 cached	$0.18	View more
DeepSeek-V4-Pro	1024k	$1.30 / $0.10 cached	$2.60	View more
DeepSeek-V4-Flash	1024k	$0.09 / $0.018 cached	$0.18	View more
DeepSeek-V3.2	160k	$0.26 / $0.13 cached	$0.38	View more
DeepSeek-V3.1	160k	$0.25 / $0.13 cached	$0.95	View more
DeepSeek-V3	160k	$0.32	$0.89	View more
DeepSeek-R1-0528	160k	$0.50 / $0.35 cached	$2.15	View more

FAQ

What is DeepSeek?

DeepSeek is a family of high-performance, open-source language models developed by DeepSeek AI. These models, including DeepSeek-R1 and DeepSeek-V3, are optimized for reasoning, coding, and multi-modal tasks. DeepInfra hosts these models with scalable, low-latency inference infrastructure and OpenAI-compatible APIs—so you can use them immediately without managing your own GPUs.

How do DeepSeek models compare to OpenAI or Claude models?

DeepSeek-R1 achieves performance comparable to OpenAI’s GPT-4 and Claude 3 on math, reasoning, and coding tasks. DeepSeek-V3, a 671B-parameter MoE model, rivals top-tier closed-source LLMs while remaining fully open-source. DeepInfra provides low-latency access and predictable pricing that’s often more affordable.

Are the DeepSeek models open source?

Yes. All DeepSeek models are MIT-licensed, with open weights and training details publicly released. This ensures transparency, customizability, and legal flexibility for commercial use.

How do I integrate DeepSeek models into my application?

You can integrate DeepSeek models seamlessly using DeepInfra’s OpenAI-compatible API. Just replace your existing base URL with DeepInfra’s endpoint and use your DeepInfra API key—no infrastructure setup required. DeepInfra also supports integration through libraries like openai, litellm, and other SDKs, making it easy to switch or scale your workloads instantly.