LLM Reference
Featherless

Gemma 2 9B Instruct on Featherless

Gemma 2 · Google DeepMind

ServerlessOpen Weights

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

Why use Gemma 2 9B Instruct on Featherless?

Featherless offers Gemma 2 9B Instruct with pay-as-you-go pricing at $0.43/1M input tokens. Featherless is a serverless inference provider for a large catalog of third-party open text-generation models (40,000+).

Compare Gemma 2 9B Instruct across 6 providers to find the best fit for your use case
Input / 1M
$0.431
Output / 1M
$1.12
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
google/gemma-2-9b-it

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="google/gemma-2-9b-it",
    messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)

Gotchas

  • Use provider model ID "google/gemma-2-9b-it", not the LLMReference slug "gemma-2-9b-it".
  • 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 Gemma 2 9B Instruct Across Providers

ProviderInput (per 1M)Output (per 1M)
Fireworks AI$0.20$0.20
NVIDIA NIM——
OpenRouter——
Chutes AI$0.10$0.30
Replicate API$0.10$0.10
View all 6 providers →

Pricing

TypePrice (per 1M)
Input tokens$0.43
Output tokens$1.12

Capabilities

Structured Outputs

About Gemma 2 9B Instruct

Gemma 2 9B Instruct, developed by Google, is a state-of-the-art large language model based on the advanced Gemini framework. It is a decoder-only transformer model with 9 billion parameters, offering a balance between size and performance. The model is trained on an expansive dataset comprising 8 trillion tokens, including web documents, code, and mathematical text, a notable 30% increase from its predecessor, Gemma 1.1. This allows it to adeptly handle diverse tasks such as question answering, creative writing, coding, and mathematical problem-solving.

Get Started

Model Specs

Released2024-06-27
Parameters9B
Context8k
ArchitectureDecoder Only

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