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
Gemma 4 12B Starry Ocean compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| Gemma 4 12B Starry OceanGoogleThis model | DiffusionGemmaGoogleRemove | |
|---|---|---|
| Provider | ||
| Discovery tier | Open weights | Open weights |
| Added | Oct 15, 2026 | Sep 19, 2026 |
| Limits | ||
| Context window | 131.07K | 262.14KBest |
| Max output | — | 32.77K |
| Average output | 233 tokens | 266 tokens |
| Pricing, USD per 1M tokens | ||
| Input | $0.05 | $0.05 |
| Output | $0.25 | $0.15Best |
| Cache read | $0.025 | $0.025 |
| Performance | ||
| Throughput | 18.1 tok/s | — |
| Time to first token | 1.8 s | — |
| Request success (24h) | 99.3% | — |
| Benchmark indexes | ||
| Intelligence Index | — | 9.5 |
| Coding Index | — | 19.7 |
| Capabilities | ||
| Reasoning | Yes | Yes |
| Tools | Yes | No |
| Vision | Yes | No |
| Reasoning effort | none, high | none, xhigh |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- Gemma 4 12B Starry Ocean
- DiffusionGemma
Intelligence Index
- Gemma 4 12B Starry Ocean—
- DiffusionGemma9.5
Indexes
Coding Index
Composite of the coding evaluations
- Gemma 4 12B Starry Ocean: —
- DiffusionGemma: 19.7
Agentic work
GDPval-AAv2.1
Economically valuable occupational tasks, rated head to head
- Gemma 4 12B Starry Ocean: —
- DiffusionGemma: 321 Elo
Document reasoning
AA-LCRv1.1
Reasoning across long documents
- Gemma 4 12B Starry Ocean: —
- DiffusionGemma: 19.7%
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- Gemma 4 12B Starry Ocean: —
- DiffusionGemma: 10.8%
GPQA Diamond
Graduate-level science questions
- Gemma 4 12B Starry Ocean: —
- DiffusionGemma: 66.9%
IFBench
Following precise output instructions
- Gemma 4 12B Starry Ocean: —
- DiffusionGemma: 59.5%
Legacy evaluations
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- Gemma 4 12B Starry Ocean: —
- DiffusionGemma: 19.7%
Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis
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
- 18.1 tok/s
- Faster than 10% of 260 models
- Time to first token
- 1.8 s
- Quicker than 81% of 260 models
- Prompt cache hits
- 78.6%
- About $0.0303 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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