Last refreshed 2026-06-15. Next refresh: weekly.
Why use Vicuna 13B on GCP Vertex AI?
GCP Vertex AI offers Vicuna 13B with competitive pricing. Vertex AI is Google Cloud's managed AI platform, offering access to Gemini models and hundreds of partner models alongside tools for fine-tuning, grounding, vector search, and end-to-end MLOps pipelines.
Compare Vicuna 13B across 2 providers to find the best fit for your use caseSetup recipe
Python + curlpip install google-cloud-aiplatformexport GOOGLE_CLOUD_PROJECT=...import os
import vertexai
from vertexai.generative_models import GenerativeModel
vertexai.init(project=os.environ["GOOGLE_CLOUD_PROJECT"], location="us-central1")vicuna-13bRequest example
import os
import vertexai
from vertexai.generative_models import GenerativeModel
# Reads GOOGLE_CLOUD_PROJECT from env; authenticates via Application Default Credentials
vertexai.init(project=os.environ["GOOGLE_CLOUD_PROJECT"], location="us-central1")
model = GenerativeModel("vicuna-13b")
response = model.generate_content("Hello")
print(response.text)Gotchas
- For Google-published models use the model name directly, e.g. "gemini-2.0-flash-001". For third-party publishers (Anthropic, Meta, etc.) use the full publisher path, e.g. "publishers/anthropic/models/claude-3-5-sonnet-v2@20241022".
- The examples expect GOOGLE_CLOUD_PROJECT; rename it only if your application config maps the new variable.
Compare Vicuna 13B Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| GCP Vertex AI | — | — |
| Replicate API | $0.10 | $0.50 |
Capabilities
About Vicuna 13B
Vicuna-13B is a finely-tuned open-source chatbot derived from the LLaMA model and developed with around 70,000 user-shared conversations from ShareGPT. Built on the robust Transformer architecture, it features a substantial 13-billion parameter scale. Early evaluations indicate it achieves over 90% of the effectiveness of models like OpenAI's ChatGPT and Google's Bard, surpassing other open-source models such as LLaMA and Stanford Alpaca in various scenarios. Training data includes user conversations initially captured in HTML and converted to markdown for quality filtering.