LLM Reference
Featherless

DeepSeek V4 Pro on Featherless

DeepSeek V4 · DeepSeek

ServerlessOpen Source

Last refreshed 2026-09-24. Next refresh: weekly.

Why use DeepSeek V4 Pro on Featherless?

Featherless offers DeepSeek V4 Pro with pay-as-you-go pricing at $1.60/1M input tokens. Featherless is a serverless inference provider for a large catalog of third-party open text-generation models (40,000+).

Compare DeepSeek V4 Pro across 6 providers to find the best fit for your use case
Input / 1M
$1.60
Output / 1M
$3.20
Cache
Not sourced
Batch
Not sourced

Setup recipe

Python + curl
Install
pip install openai
Auth
export FEATHERLESS_API_KEY=...
Call
import os
from openai import OpenAI
client = OpenAI(
    base_url="https://api.featherless.ai/v1",
Model ID
deepseek-ai/DeepSeek-V4-Pro

Request example

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.featherless.ai/v1",
    api_key=os.environ["FEATHERLESS_API_KEY"],
)
response = client.chat.completions.create(
    model="deepseek-ai/DeepSeek-V4-Pro",
    messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)

Gotchas

  • Use provider model ID "deepseek-ai/DeepSeek-V4-Pro", not the LLMReference slug "deepseek-v4-pro".
  • Use exact Featherless catalog id in modelProvider.providerModelId (HF-style org/name). Not interchangeable with LLM Reference slugs.
  • The examples expect FEATHERLESS_API_KEY; rename it only if your application config maps the new variable.

Compare DeepSeek V4 Pro Across Providers

ProviderInput (per 1M)Output (per 1M)
DeepSeek Platform$0.43$0.87
Fireworks AI$1.74$3.48
OpenRouter$0.44$0.87
Vercel AI Gateway$0.43$0.87
Novita AI$1.60$3.20
View all 6 providers →

Pricing

TypePrice (per 1M)
Input tokens$1.60
Output tokens$3.20

Capabilities

ReasoningJSON / Tool useStructured OutputsPrompt Caching

About DeepSeek V4 Pro

DeepSeek V4 Pro is DeepSeek's flagship open-weights model, released April 24 2026 under the MIT license. Architecture: 1.6T total / 49B active parameters, MoE with Compressed Sparse Attention (CSA) + Heavily Compressed Attention (HCA) hybrid — requiring only 27% of inference FLOPs vs standard 1M-context transformers — plus Manifold-Constrained Hyper-Connections (mHC) and Muon Optimizer. Context window: 1,000,000 tokens; max output: 384,000 tokens (Think Max mode requires >=384K context). Text-only (no vision/image input). Supports three reasoning modes: Non-Think, Think High, Think Max. Function calling, tool use, and structured outputs supported.

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