Google · Open weights

Gemma 4 12B Starry Ocean

Added Oct 15, 2026Open weights

Gemma 4 12B Starry Ocean is an open-weight merge for storytelling, roleplay, and creative writing. It supports image understanding, tool calling, optional reasoning, and a 131,072-token context window.

Reasoning
Tools
Vision

Input: Text, Images
Output: Text
Reasoning effort: none · high
Discovery tier: Open weights

Pricing

USD per 1M tokens

Input: $0.05
Output: $0.25
Cache read: $0.025

Reference prices as published by the model provider. API example

Specifications

Context window: 131.1K
Max output: —

Side by side

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TryNemotron 3.5 Content Safety DeepSeek V4 Flash 0731 (Thinking)Qwen3.5 9B All 3

Public evaluations

Benchmarks

There are no public benchmark results for Gemma 4 12B Starry Ocean yet.

Live routing data

Performance and reliability

Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.

Prompt cache hits: 41.0%; About $0.0398 per 1M input tokens with caching
Request success (24h): 99.4%; Reliable

Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.

Hourly request success · last 72 hoursLowest hour 88.8%

Healthy (98% or more)
Degraded (90–98%)
Down (under 90%)
No requests

Developers

Use it through the API

Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.

Read the quickstart

curl https://www.noumen.xyz/api/v1/chat/completions \
  -H "Authorization: Bearer $NOUMEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "google/gemma-4-12b-it-starry-ocean",
    "reasoning_effort": "none",
    "messages": [{ "role": "user", "content": "Explain this stack trace." }]
  }'

import os
from openai import OpenAI

client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
    model="google/gemma-4-12b-it-starry-ocean",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Google

Other Google models, starting with the same family.