feat(google-vertex): update model YAMLs [bot]

[harshiv-26]

Auto-generated by poc-agent for provider google-vertex.

---

Note

Low Risk
Low risk config-only change updating cost metadata; main impact is on any billing/estimation that reads these YAML values.

Overview
Updates the anthropic/claude-opus-4-7 Google Vertex model YAML to add region-specific cost entries for us and eu alongside the existing global pricing.

Also removes the extra Claude models documentation link from sources, leaving the Google Vertex docs and Claude pricing references.

^{Reviewed by Cursor Bugbot for commit 94803e5. Bugbot is set up for automated code reviews on this repo. Configure here.}

[github-actions]

/test-models

[harshiv-26]

Gateway test results

• Total: 6
• Passed: 4
• Failed: 0
• Validation failed: 2
• Errored: 0
• Skipped: 0
• Success rate: 66.67%

Provider	Model	Scenarios
google-vertex	anthropic/claude-opus-4-7	success: params, params:stream, tool-call, tool-call:stream validation_failure: reasoning, reasoning:stream

Failures (2)

google-vertex/anthropic/claude-opus-4-7 — reasoning (validation_failure)

Error:

Traceback (most recent call last):
  File "/tmp/tmpj66_3i4d/snippet.py", line 43, in <module>
    raise Exception("VALIDATION FAILED: reasoning - no reasoning information in response")
Exception: VALIDATION FAILED: reasoning - no reasoning information in response

Code snippet

from openai import OpenAI

client = OpenAI(api_key="***", base_url="https://internal.devtest.truefoundry.tech/api/llm")

response = client.chat.completions.create(
    model="test-v2-vertex/anthropic-claude-opus-4-7",
    messages=[
        {"role": "system", "content": "You are a helpful assistant. You MUST think step by step and show your reasoning. Never skip reasoning steps."},
        {"role": "user", "content": "Hi"},
        {"role": "assistant", "content": "Hi, how can I help you"},
        {"role": "user", "content": "How to calculate 3^3^3^3? Think step by step and show all reasoning."},
    ],
    reasoning_effort="medium",
    stream=False,
)

_usage = getattr(response, "usage", None)
_reasoning_detected = False

_choices = getattr(response, "choices", None)
if _choices and len(_choices) > 0:
    _message = getattr(_choices[0], "message", None)
else:
    _message = None

if _message and getattr(_message, "content", None) is not None:
    print(_message.content)

if _usage is not None:
    _output_token_details = getattr(_usage, "completion_tokens_details", None)
    if _output_token_details and getattr(_output_token_details, "reasoning_tokens", 0) > 0:
        _reasoning_detected = True
    elif getattr(_usage, "reasoning", None) is not None:
        _reasoning_detected = True

if getattr(_message, "reasoning_content", None) is not None:
    _reasoning_detected = True
elif getattr(_message, "reasoning", None) is not None:
    _reasoning_detected = True

if not _reasoning_detected:
    print("Response: ", response)
    raise Exception("VALIDATION FAILED: reasoning - no reasoning information in response")
print("VALIDATION: reasoning SUCCESS")

google-vertex/anthropic/claude-opus-4-7 — reasoning:stream (validation_failure)

Error:

Traceback (most recent call last):
  File "/tmp/tmp2qhama1q/snippet.py", line 35, in <module>
    raise Exception("VALIDATION FAILED: reasoning stream - no reasoning information in stream")
Exception: VALIDATION FAILED: reasoning stream - no reasoning information in stream

Code snippet

from openai import OpenAI

client = OpenAI(api_key="***", base_url="https://internal.devtest.truefoundry.tech/api/llm")

response = client.chat.completions.create(
    model="test-v2-vertex/anthropic-claude-opus-4-7",
    messages=[
        {"role": "system", "content": "You are a helpful assistant. You MUST think step by step and show your reasoning. Never skip reasoning steps."},
        {"role": "user", "content": "Hi"},
        {"role": "assistant", "content": "Hi, how can I help you"},
        {"role": "user", "content": "How to calculate 3^3^3^3? Think step by step and show all reasoning."},
    ],
    reasoning_effort="medium",
    stream=True,
)

_reasoning_detected = False
for chunk in response:
    if chunk.choices and len(chunk.choices) > 0:
        delta = chunk.choices[0].delta
        if delta.content is not None:
            print(delta.content, end="", flush=True)
        if getattr(delta, "reasoning_content", None) is not None:
            _reasoning_detected = True
        if getattr(delta, "reasoning", None) is not None:
            _reasoning_detected = True

    _usage = getattr(chunk, "usage", None)
    if _usage is not None:
        _details = getattr(_usage, "completion_tokens_details", None)
        if _details and getattr(_details, "reasoning_tokens", 0) > 0:
            _reasoning_detected = True

if not _reasoning_detected:
    raise Exception("VALIDATION FAILED: reasoning stream - no reasoning information in stream")
print("\nVALIDATION: reasoning stream SUCCESS")

[cursor]

Cursor Bugbot has reviewed your changes and found 1 potential issue.

^{❌ Bugbot Autofix is OFF. To automatically fix reported issues with cloud agents, enable autofix in the Cursor dashboard.}

^{Reviewed by Cursor Bugbot for commit 94803e5. Configure here.}

[cursor]

Inconsistent region identifiers compared to all other models

High Severity

The region values us and eu are inconsistent with every other model YAML in this directory, which all use specific GCP region names like us-east5 and europe-west1 (or global). If the consuming system matches costs by region identifier, these abbreviated names won't match, leading to incorrect or missing cost calculations for this model.

Additional Locations (1)

• providers/google-vertex/anthropic/claude-opus-4-7.yaml#L21-L22

 

^{Reviewed by Cursor Bugbot for commit 94803e5. Configure here.}