Google: Gemma 4 31B

2 providers live

google/gemma-4-31b-it

2 live providers
100% uptime
0.69 s first token
65 tok/s output
25% off

Gemma 4 31B Instruct is Google DeepMind's 30.7B dense multimodal model supporting text and image input with text output. Features a 256K token context window, configurable thinking/reasoning mode, native function calling, and multilingual support across 140+ languages. Strong on coding, reasoning, and document understanding tasks. Apache 2.0 license.

Context: 256K; tokens
Max output: 16K; tokens
Input: ~~$0.20~~ $0.15; per million tokens, 25% off
Output: ~~$0.40~~ $0.30; per million tokens, 25% off

Providers

Ranked by price and speed. Requests go to the best healthy provider, then fail over down the list.Benchmarks

Live providers of Gemma 4 31B, in the router's order
Who answers now	Input per 1M	First token
llm.buzz25% offpicked	~~$0.20~~ $0.15	2.98 s
DeepInfra	$0.20	0.69 s

llm.buzz answers now: lowest price.

First token is the 24-hour median. If the pick fails, the next one answers at its own price.

#	Provider	Input	Output	Cache read	Latency	Throughput	Uptime (3d)
1	llm.buzzAuto25% offNon-EU Data policy not declared	~~$0.20~~ $0.15	~~$0.40~~ $0.30	n/a	2.98 s	65 tok/s	97.2%
2	DeepInfraNon-EU Data policy not declared	$0.20	$0.40	n/a	0.69 s	26 tok/s	100%

Want to serve this model?Become a provider

Price history

No price changes since Oct 20, 2026. The history shows prices before discounts.

DeepInfra$0.20 in, $0.40 out

Performance

Last 24 hours, from llm.buzz's test requests

Throughput (tok/s)

llm.buzz65 tok/s
DeepInfra26 tok/s

Data table

Provider	From (UTC)	p50	p90	p99
llm.buzz	Oct 26, 05:00	65 tok/s	65 tok/s	65 tok/s
llm.buzz	Oct 26, 10:00	93 tok/s	93 tok/s	93 tok/s
llm.buzz	Oct 26, 20:00	22 tok/s	22 tok/s	22 tok/s
llm.buzz	Oct 27, 00:00	45 tok/s	45 tok/s	45 tok/s
llm.buzz	Oct 27, 04:00	65 tok/s	65 tok/s	65 tok/s
llm.buzz	Last 24 hours	65 tok/s	22 tok/s	22 tok/s
DeepInfra	Oct 26, 07:00	31 tok/s	31 tok/s	31 tok/s
DeepInfra	Oct 26, 12:00	37 tok/s	37 tok/s	37 tok/s
DeepInfra	Oct 26, 16:00	26 tok/s	26 tok/s	26 tok/s
DeepInfra	Oct 26, 20:00	18 tok/s	18 tok/s	18 tok/s
DeepInfra	Oct 27, 01:00	15 tok/s	15 tok/s	15 tok/s
DeepInfra	Last 24 hours	26 tok/s	15 tok/s	15 tok/s

Latency (time to first token)

llm.buzz2.98 s
DeepInfra0.69 s

Data table

Provider	From (UTC)	p50	p90	p99
llm.buzz	Oct 26, 05:00	2.98 s	2.98 s	2.98 s
llm.buzz	Oct 26, 10:00	7.41 s	7.41 s	7.41 s
llm.buzz	Oct 26, 20:00	5.14 s	5.14 s	5.14 s
llm.buzz	Oct 27, 00:00	2.07 s	2.07 s	2.07 s
llm.buzz	Oct 27, 04:00	2.07 s	2.07 s	2.07 s
llm.buzz	Last 24 hours	2.98 s	7.41 s	7.41 s
DeepInfra	Oct 26, 07:00	0.58 s	0.58 s	0.58 s
DeepInfra	Oct 26, 12:00	0.58 s	0.58 s	0.58 s
DeepInfra	Oct 26, 16:00	0.69 s	0.69 s	0.69 s
DeepInfra	Oct 26, 20:00	0.69 s	0.69 s	0.69 s
DeepInfra	Oct 27, 01:00	0.69 s	0.69 s	0.69 s
DeepInfra	Last 24 hours	0.69 s	0.69 s	0.69 s

Uptime

Last 72 hours, by hour

Best provider 100%

This model

llm.buzz

DeepInfra

Up: 95% or more served
Degraded: 85% to 95%
Down: below 85%
No data
From llm.buzz's test requests

Questions

How much does Google: Gemma 4 31B cost?

Google: Gemma 4 31B costs $0.15 per million input tokens and $0.30 per million output tokens at llm.buzz, the provider requests go to first. These prices are 25% off until 2026-11-06 00:00 UTC. Each provider sets its own price, and you pay the price of the provider that answered.

Do providers train on my prompts?

Each provider declares it, and the table above shows every declaration. You can limit a request to providers that keep nothing, or to providers in the EU.

Can I serve Google: Gemma 4 31B myself?

Yes. Apply as a provider, add your endpoint and list this model at your price. It goes live once the automated check passes.

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Call it

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.llm.buzz/api/v1",
    api_key=os.environ["LLM_BUZZ_KEY"],
)
reply = client.chat.completions.create(
    model="google/gemma-4-31b-it",
    messages=[{"role": "user", "content": "Hello"}],
)

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.llm.buzz/api/v1",
  apiKey: process.env.LLM_BUZZ_KEY,
});
const reply = await client.chat.completions.create({
  model: "google/gemma-4-31b-it",
  messages: [{ role: "user", content: "Hello" }],
});

curl https://api.llm.buzz/api/v1/chat/completions \
  -H "Authorization: Bearer $LLM_BUZZ_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "google/gemma-4-31b-it",
       "messages": [{"role": "user", "content": "Hello"}]}'

Endpoints

POST /api/v1/chat/completionsPOST /api/v1/responsesPOST /api/v1/messages

Authentication

Send your llm.buzz key as a Bearer token: Authorization: Bearer $LLM_BUZZ_KEY. Create keys in the console. The same key calls every model; each request pays the price of the provider that answered it.

More in Authentication and the API reference.

Call Google: Gemma 4 31B with one keyEach request pays the price of the provider that answered it.

Get API key