LLM Reference
Featherless

Qwen2.5-32B-Instruct on Featherless

Qwen2.5 · Alibaba

ServerlessOpen Source

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

Why use Qwen2.5-32B-Instruct on Featherless?

Featherless offers Qwen2.5-32B-Instruct with pay-as-you-go pricing at $0.68/1M input tokens. Featherless is a serverless inference provider for a large catalog of third-party open text-generation models (40,000+).

Compare Qwen2.5-32B-Instruct across 4 providers to find the best fit for your use case
Input / 1M
$0.68
Output / 1M
$1.20
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
Qwen/Qwen2.5-32B-Instruct

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="Qwen/Qwen2.5-32B-Instruct",
    messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)

Gotchas

  • Use provider model ID "Qwen/Qwen2.5-32B-Instruct", not the LLMReference slug "qwen2.5-32b-instruct".
  • 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 Qwen2.5-32B-Instruct Across Providers

ProviderInput (per 1M)Output (per 1M)
Fireworks AI$0.90$0.90
SiliconFlow$0.15$0.15
Replicate API$0.60$0.60
Featherless$0.68$1.20

Pricing

TypePrice (per 1M)
Input tokens$0.68
Output tokens$1.20

Capabilities

Structured Outputs

About Qwen2.5-32B-Instruct

Instruction-tuned 32B variant for advanced content creation, analytics, and vision-language pipelines on multi-GPU infrastructure.

Get Started

Model Specs

Released2024-06-07
Parameters32.5B
Context128k
ArchitectureDecoder Only

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