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+# anthropic/opus-4-8-coding (Claude Opus 4.8 Advanced Coding)
+
+This example exercises Claude Opus 4.8 on hard coding tasks using the `xhigh` effort level with adaptive thinking.
+
+You can run this example with:
+
+```bash
+npx promptfoo@latest init --example anthropic/opus-4-8-coding
+cd opus-4-8-coding
+```
+
+## What This Tests
+
+Claude Opus 4.8 is Anthropic's most capable model for complex reasoning and agentic coding. This example evaluates:
+
+- **Bug diagnosis** across multiple system boundaries
+- **Production-quality code generation** with proper error handling
+- **Code review** with nuanced, prioritized feedback
+
+## Working with Opus 4.8
+
+- **Builds on Opus 4.7.** Opus 4.8 supports the same feature set as 4.7 (no breaking API changes) and improves capability on complex reasoning and long-horizon agentic coding.
+- **Adaptive thinking is opt-in.** Set `thinking: { type: adaptive }` (as this example does) to let the model decide when and how much to reason per request. Without an explicit `thinking` block the model runs **without** extended thinking, even at high effort.
+- **`effort` defaults to `high`; `xhigh` is available.** Setting `effort: high` behaves the same as omitting it. Start with `xhigh` for coding and agentic work, and pair high effort with a large `max_tokens`.
+- **Sampling controls are managed for you.** Opus 4.8 rejects `temperature`, `top_p`, and `top_k` at the model level; promptfoo omits them automatically (don't set them in config).
+
+## Running the Example
+
+```bash
+# Set your API key
+export ANTHROPIC_API_KEY=your_api_key_here
+
+# Run the evaluation
+npx promptfoo@latest eval
+
+# View results
+npx promptfoo@latest view
+```
+
+## Other providers
+
+Opus 4.8 is also reachable through:
+
+- AWS Bedrock — `bedrock:us.anthropic.claude-opus-4-8` (or `bedrock:converse:us.anthropic.claude-opus-4-8`)
+- Google Vertex — `vertex:claude-opus-4-8` with `config.region: global`
+- Azure AI Foundry — point `anthropic:messages:claude-opus-4-8` at `https://<resource>.services.ai.azure.com/anthropic` via `apiBaseUrl`
+
+Across all four providers, promptfoo automatically omits the unsupported sampling parameters (`temperature`, `top_p`, `top_k`) for Opus 4.8. The Anthropic Messages provider also logs a one-time warning if you set them explicitly; the Bedrock, Vertex, and Azure paths omit them silently.
+
+## Learn More
+
+- [Claude Opus 4.8 announcement](https://www.anthropic.com/news/claude-opus-4-8)
+- [Anthropic documentation](https://docs.anthropic.com)
+- [Promptfoo Anthropic provider docs](https://promptfoo.dev/docs/providers/anthropic)
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+# yaml-language-server: $schema=https://promptfoo.dev/config-schema.json
+description: Claude Opus 4.8 advanced coding with xhigh effort and adaptive thinking
+
+prompts:
+  - |
+    {{task}}
+
+providers:
+  - id: anthropic:messages:claude-opus-4-8
+    config:
+      # Opus 4.8 deprecates manual sampling controls (temperature/top_p/top_k) at
+      # the model level — promptfoo omits them automatically, so don't set them here.
+      #
+      # Adaptive thinking is opt-in: without an explicit `thinking` block the model
+      # runs WITHOUT extended thinking even at high effort. Set it to let the model
+      # decide when and how much to reason per request.
+      thinking:
+        type: adaptive
+      effort: xhigh # Recommended starting point for coding/agentic work (between high and max)
+      max_tokens: 8000
+
+tests:
+  # Complex bug diagnosis across multiple systems
+  - vars:
+      task: |
+        You're debugging a production issue where users can't log in. Here's what you know:
+
+        1. The frontend shows "Authentication failed" after username/password submission
+        2. Backend logs show successful JWT generation
+        3. Redis cache is returning stale session data
+        4. Database shows correct user credentials
+        5. The issue only affects 10% of login attempts
+        6. It started after deploying a load balancer configuration change
+
+        Diagnose the root cause and propose a fix. Explain your reasoning about what's causing the intermittent nature of the bug.
+    assert:
+      - type: contains-any
+        value: ['load balancer', 'session', 'sticky', 'affinity', 'routing']
+        reason: Should identify load balancer session routing as the issue
+      - type: llm-rubric
+        value: |
+          The response should:
+          1. Identify the root cause (likely session affinity/sticky sessions issue with load balancer)
+          2. Explain why it's intermittent (different backend servers, inconsistent session state)
+          3. Propose concrete fixes (enable sticky sessions, shared session store, stateless tokens)
+          4. Show reasoning about the tradeoffs of different solutions
+
+  # Production-quality code generation with error handling
+  - vars:
+      task: |
+        Write a Python function that:
+        1. Fetches user data from a REST API (may timeout or return errors)
+        2. Caches results in Redis with 5-minute TTL
+        3. Falls back to database if cache miss
+        4. Returns user object or raises appropriate exception
+
+        Include proper error handling, typing, and comments explaining design decisions.
+    assert:
+      - type: contains
+        value: 'def'
+        reason: Should include Python function definition
+      - type: contains-any
+        value: ['try', 'except', 'raise', 'error']
+        reason: Should include error handling
+      - type: contains-any
+        value: ['cache', 'redis', 'ttl']
+        reason: Should implement caching logic
+      - type: llm-rubric
+        value: |
+          The code should:
+          1. Include proper type hints (from typing import ...)
+          2. Handle network timeouts and API errors gracefully
+          3. Implement cache-aside pattern correctly
+          4. Include docstrings and comments explaining design decisions
+          5. Use appropriate exception types
+          6. Be production-ready (not a toy example)
+
+  # Code review with nuanced feedback
+  - vars:
+      task: |
+        Review this React component and provide feedback:
+
+        ```jsx
+        function UserList() {
+          const [users, setUsers] = useState([]);
+
+          useEffect(() => {
+            fetch('/api/users')
+              .then(res => res.json())
+              .then(data => setUsers(data));
+          }, []);
+
+          return (
+            <div>
+              {users.map(user => (
+                <div key={user.id}>
+                  <h3>{user.name}</h3>
+                  <p>{user.email}</p>
+                </div>
+              ))}
+            </div>
+          );
+        }
+        ```
+
+        Identify issues, suggest improvements, and explain the reasoning behind each suggestion.
+    assert:
+      - type: contains-any
+        value: ['error', 'loading', 'state', 'async']
+        reason: Should identify missing error and loading states
+      - type: llm-rubric
+        value: |
+          The review should identify multiple issues:
+          1. No error handling for failed fetch
+          2. No loading state
+          3. No cleanup for fetch in useEffect
+          4. Missing dependencies might cause issues in strict mode
+          5. No null/empty checks for users array
+
+          For each issue, it should:
+          - Explain why it's a problem
+          - Suggest specific improvements
+          - Provide example code where helpful
+          - Prioritize issues by severity