Google · Open weights
Gemma 4 12B Starry Ocean
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
- —
- Average output (7 days)
- 233 tokens
- Longer than 25% of 473 models
Side by side
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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.
- Throughput
- 18.3 tok/s
- Faster than 10% of 257 models
- Time to first token
- 0.9 s
- Quicker than 94% of 257 models
- Prompt cache hits
- 80.9%
- About $0.0298 per 1M input tokens with caching
- Request success (24h)
- 99.3%
- 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.
- 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.
curl https://www.noumen.xyz/api/v1/chat/completions \
-H "Authorization: Bearer $NOUMEN_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "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="gemma-4-12b-it-starry-ocean",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)Related models
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