Last refreshed 2026-07-26. Next refresh: weekly.
Why use Claude Opus 5 on GCP Vertex AI?
GCP Vertex AI offers Claude Opus 5 with pay-as-you-go pricing at $5.00/1M input tokens. 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 Claude Opus 5 across 7 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")claude-opus-5Request 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("claude-opus-5")
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 Claude Opus 5 Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Anthropic | $5.00 | $25.00 |
| AWS Bedrock | — | — |
| GCP Vertex AI | $5.00 | $25.00 |
| Microsoft Foundry | — | — |
| OpenRouter | $5.00 | $25.00 |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $5.00 |
| Output tokens | $25.00 |
| Image input | $1.00 |
Capabilities
About Claude Opus 5
Claude Opus 5 is Anthropic's July 2026 flagship Opus model for complex agentic coding, enterprise work, long-horizon reasoning, computer use, and professional analysis. It accepts text and image input and returns text, with adaptive thinking enabled by default and five request-level effort settings from low through max that control thinking depth and token use rather than visible response length. It also supports tool use, structured outputs, prompt caching, Batch API, a 1M-token context window, and up to 128K synchronous output tokens.