
Google: Gemma 4 26B A4B
3 providers live- 3 live providers
- 100% uptime
- 0.23 s first token
- 232 tok/s output
Gemma 4 26B A4B IT is an instruction-tuned Mixture-of-Experts (MoE) model from Google DeepMind. Despite 25.2B total parameters, only 3.8B activate per token during inference — delivering near-31B quality at a fraction of the compute cost. Supports multimodal input including text, images, and video (up to 60s at 1fps). Features a 256K token context window, native function calling, configurable thinking/reasoning mode, and structured output support. Released under Apache 2.0.
- Context
- 256K
- tokens
- Max output
- 16K
- tokens
- Input
- $0.07
- per million tokens
- Output
- $0.34
- per million tokens
Providers
Ranked by price and speed. Requests go to the best healthy provider, then fail over down the list.BenchmarksPrice history
Input price since Oct 20, 2026, in USD per million tokens.
- DeepInfra
- Novita
- Scaleway
Data table
| Provider | From | Input | Output |
|---|---|---|---|
| DeepInfra | Oct 20, 2026 | $0.07 | $0.34 |
| Novita | Oct 21, 2026 | $0.13 | $0.40 |
| Scaleway | Oct 21, 2026 | $0.2888 | $0.5776 |
| Scaleway | Oct 22, 2026 | $0.2893 | $0.5786 |
| Scaleway | Oct 22, 2026 | $0.2886 | $0.5771 |
| Scaleway | Oct 23, 2026 | $0.2883 | $0.5765 |
| Scaleway | Oct 26, 2026 | $0.2881 | $0.5762 |
Performance
Last 24 hours, from llm.buzz's test requestsThroughput (tok/s)
- DeepInfra93 tok/s
- Scaleway232 tok/s
- Novita54 tok/s
Data table
| Provider | From (UTC) | p50 | p90 | p99 |
|---|---|---|---|---|
| DeepInfra | Oct 26, 07:00 | 93 tok/s | 93 tok/s | 93 tok/s |
| DeepInfra | Oct 26, 12:00 | 15 tok/s | 15 tok/s | 15 tok/s |
| DeepInfra | Oct 26, 16:00 | 134 tok/s | 134 tok/s | 134 tok/s |
| DeepInfra | Oct 26, 21:00 | 134 tok/s | 134 tok/s | 134 tok/s |
| DeepInfra | Oct 27, 01:00 | 65 tok/s | 65 tok/s | 65 tok/s |
| DeepInfra | Last 24 hours | 93 tok/s | 15 tok/s | 15 tok/s |
| Scaleway | Oct 26, 05:00 | 232 tok/s | 232 tok/s | 232 tok/s |
| Scaleway | Oct 26, 10:00 | 232 tok/s | 232 tok/s | 232 tok/s |
| Scaleway | Oct 26, 14:00 | 161 tok/s | 161 tok/s | 161 tok/s |
| Scaleway | Oct 26, 19:00 | 161 tok/s | 161 tok/s | 161 tok/s |
| Scaleway | Oct 26, 23:00 | 232 tok/s | 232 tok/s | 232 tok/s |
| Scaleway | Oct 27, 03:00 | 161 tok/s | 161 tok/s | 161 tok/s |
| Scaleway | Last 24 hours | 232 tok/s | 161 tok/s | 161 tok/s |
| Novita | Oct 26, 07:00 | 26 tok/s | 26 tok/s | 26 tok/s |
| Novita | Oct 26, 11:00 | 193 tok/s | 193 tok/s | 193 tok/s |
| Novita | Oct 26, 15:00 | 112 tok/s | 112 tok/s | 112 tok/s |
| Novita | Oct 26, 20:00 | 37 tok/s | 37 tok/s | 37 tok/s |
| Novita | Oct 27, 00:00 | 54 tok/s | 54 tok/s | 54 tok/s |
| Novita | Last 24 hours | 54 tok/s | 26 tok/s | 26 tok/s |
Latency (time to first token)
- DeepInfra0.69 s
- Scaleway0.23 s
- Novita0.83 s
Data table
| Provider | From (UTC) | p50 | p90 | p99 |
|---|---|---|---|---|
| DeepInfra | Oct 26, 07:00 | 0.69 s | 0.69 s | 0.69 s |
| DeepInfra | Oct 26, 12:00 | 0.83 s | 0.83 s | 0.83 s |
| DeepInfra | Oct 26, 16:00 | 0.58 s | 0.58 s | 0.58 s |
| DeepInfra | Oct 26, 21:00 | 0.69 s | 0.69 s | 0.69 s |
| DeepInfra | Oct 27, 01:00 | 1.00 s | 1.00 s | 1.00 s |
| DeepInfra | Last 24 hours | 0.69 s | 1.00 s | 1.00 s |
| Scaleway | Oct 26, 05:00 | 0.23 s | 0.23 s | 0.23 s |
| Scaleway | Oct 26, 10:00 | 0.28 s | 0.28 s | 0.28 s |
| Scaleway | Oct 26, 14:00 | 0.23 s | 0.23 s | 0.23 s |
| Scaleway | Oct 26, 19:00 | 0.28 s | 0.28 s | 0.28 s |
| Scaleway | Oct 26, 23:00 | 0.23 s | 0.23 s | 0.23 s |
| Scaleway | Oct 27, 03:00 | 0.23 s | 0.23 s | 0.23 s |
| Scaleway | Last 24 hours | 0.23 s | 0.28 s | 0.28 s |
| Novita | Oct 26, 07:00 | 0.83 s | 0.83 s | 0.83 s |
| Novita | Oct 26, 11:00 | 1.00 s | 1.00 s | 1.00 s |
| Novita | Oct 26, 15:00 | 0.58 s | 0.58 s | 0.58 s |
| Novita | Oct 26, 20:00 | 0.83 s | 0.83 s | 0.83 s |
| Novita | Oct 27, 00:00 | 0.69 s | 0.69 s | 0.69 s |
| Novita | Last 24 hours | 0.83 s | 1.00 s | 1.00 s |
Uptime
Last 72 hours, by hour- 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 26B A4B cost?
Google: Gemma 4 26B A4B costs $0.07 per million input tokens and $0.34 per million output tokens at DeepInfra, the provider requests go to first. 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 26B A4B myself?
Yes. Apply as a provider, add your endpoint and list this model at your price. It goes live once the automated check passes.
Similar models
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-4-26b-a4b-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-4-26b-a4b-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-4-26b-a4b-it",
"messages": [{"role": "user", "content": "Hello"}]}'Endpoints
POST /api/v1/chat/completionsPOST /api/v1/responsesPOST /api/v1/messagesAuthentication
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.
README
Gemma 4 26B A4B IT is an instruction-tuned Mixture-of-Experts (MoE) model from Google DeepMind. Despite 25.2B total parameters, only 3.8B activate per token during inference — delivering near-31B quality at a fraction of the compute cost. Supports multimodal input including text, images, and video (up to 60s at 1fps). Features a 256K token context window, native function calling, configurable thinking/reasoning mode, and structured output support. Released under Apache 2.0.
- Model ID
- google/gemma-4-26b-a4b-it
- Takes
- Text, image
- Answers
- Text
- Context
- 256K tokens
- Max output
- 16K tokens
- Capabilities
- Tools, Structured outputs
- Parameters
- max_tokens, response_format, structured_outputs, temperature, tool_choice, tools
- Weights
- Open weights
- On llm.buzz since
- Oct 20, 2026