HazırGPU TEE

Google: Gemma 4 31B

Model Kimliğigoogle/gemma-4-31b-it

Gemma 4 31B Instruct is Google DeepMind's 30.7B dense model. Features a 256K token context window, configurable thinking/reasoning mode, native function calling, and strong multilingual performance. Served as a text-only TEE deployment via NEAR AI.

Geliştirmeye başlayın Dokümanlar

girdi

$0.15/M

çıktı

$0.46/M

bağlam

262K

oluşturuldu

26 May 2026

Desteklenen API yapısı

girdi

text

çıktı

text

araçlar

Listelenmiyor

json modu

Listelenmiyor

Doğrulama

imza

yanıt kimliği

attestasyon

GPU TEE

sağlayıcı

Phala

Provider

Phala

GPU TEE

girdi

$0.15/M

çıktı

$0.46/M

bağlam

262K

$ pip install openai

1from openai import OpenAI
2import os
3
4MODEL = "google/gemma-4-31b-it"
5
6client = OpenAI(
7    base_url="https://api.redpill.ai/v1",
8    api_key=os.environ["REDPILL_API_KEY"],
9)
10
11response = client.chat.completions.create(
12    model=MODEL,
13    messages=[{"role": "user", "content": "Run this privately."}],
14)
15
16print(response.id)
17print(response.choices[0].message.content)

$ npm install openai

1import OpenAI from "openai"
2
3const MODEL = "google/gemma-4-31b-it"
4
5const openai = new OpenAI({
6  baseURL: "https://api.redpill.ai/v1",
7  apiKey: process.env.REDPILL_API_KEY,
8})
9
10const response = await openai.chat.completions.create({
11  model: MODEL,
12  messages: [{ role: "user", content: "Run this privately." }],
13})
14
15console.log(response.id)
16console.log(response.choices[0].message.content)

1curl https://api.redpill.ai/v1/chat/completions \
2  -H "Authorization: Bearer $REDPILL_API_KEY" \
3  -H "Content-Type: application/json" \
4  -d '{
5    "model": "google/gemma-4-31b-it",
6    "messages": [
7      { "role": "user", "content": "Run this privately." }
8    ]
9  }'

Daha fazla model

Diğer özel çıkarım rotaları.

Kataloğu görüntüle

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bağlam

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girdi

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bağlam

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girdi

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The Qwen3.5 27B native vision-language Dense model incorporates a linear attention mechanism, delivering fast response times while balancing inference speed and performance. Its overall capabilities are comparable to those of the Qwen3.5-122B-A10B.

bağlam

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girdi

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As a 30B-class SOTA model, GLM-4.7-Flash offers a new option that balances performance and efficiency. It is further optimized for agentic coding use cases, strengthening coding capabilities, long-horizon task planning, and tool collaboration, and has achieved leading performance among open-source models of the same size on several current public benchmark leaderboards.

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