[Infra] Reduce llm_translation_testing parallelism and tolerate worker restarts

.circleci/config.yml

@@ -682,7 +682,7 @@ jobs:
             for dir in "${IGNORE_DIRS[@]}"; do
               IGNORE_ARGS="$IGNORE_ARGS --ignore=$dir"
             done
-            uv run --no-sync python -m pytest -v tests/llm_translation $IGNORE_ARGS --junitxml=test-results/junit.xml --durations=20 -n 8 --timeout=120 --timeout_method=thread --retries 2 --retry-delay 5
+            uv run --no-sync python -m pytest -v tests/llm_translation $IGNORE_ARGS --junitxml=test-results/junit.xml --durations=20 -n 4 --timeout=120 --timeout_method=thread --retries 2 --retry-delay 5 --max-worker-restart=5
           no_output_timeout: 15m
 
       # Store test results

docs/my-website/blog/claude_opus_4_7/index.md

@@ -0,0 +1,366 @@
+---
+slug: claude_opus_4_7
+title: "Day 0 Support: Claude Opus 4.7"
+date: 2026-04-16T10:00:00
+authors:
+  - sameer
+  - ishaan-alt
+  - krrish
+description: "Day 0 support for Claude Opus 4.7 on LiteLLM AI Gateway - use across Anthropic, Azure, Vertex AI, and Bedrock."
+tags: [anthropic, claude, opus 4.7]
+hide_table_of_contents: false
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+LiteLLM now supports [Claude Opus 4.7](https://www.anthropic.com/news/claude-opus-4-7) on Day 0. Use it across Anthropic, Azure, Vertex AI, and Bedrock through the LiteLLM AI Gateway.
+
+{/* truncate */}
+
+## Docker Image
+
+```bash
+docker pull ghcr.io/berriai/litellm:litellm_stable_release_branch-v1.83.3-stable.opus-4.7
+```
+
+## Usage - Anthropic
+
+<Tabs>
+<TabItem value="proxy" label="LiteLLM Proxy">
+
+**1. Setup config.yaml**
+
+```yaml
+model_list:
+  - model_name: claude-opus-4-7
+    litellm_params:
+      model: anthropic/claude-opus-4-7
+      api_key: os.environ/ANTHROPIC_API_KEY
+```
+
+**2. Start the proxy**
+
+```bash
+docker run -d \
+  -p 4000:4000 \
+  -e ANTHROPIC_API_KEY=$ANTHROPIC_API_KEY \
+  -v $(pwd)/config.yaml:/app/config.yaml \
+  ghcr.io/berriai/litellm:litellm_stable_release_branch-v1.83.3-stable.opus-4.7 \
+  --config /app/config.yaml
+```
+
+**3. Test it!**
+
+```bash
+curl --location 'http://0.0.0.0:4000/chat/completions' \
+--header 'Content-Type: application/json' \
+--header 'Authorization: Bearer $LITELLM_KEY' \
+--data '{
+  "model": "claude-opus-4-7",
+  "messages": [
+    {
+      "role": "user",
+      "content": "what llm are you"
+    }
+  ]
+}'
+```
+
+</TabItem>
+</Tabs>
+
+## Usage - Azure
+
+<Tabs>
+<TabItem value="proxy" label="LiteLLM Proxy">
+
+**1. Setup config.yaml**
+
+```yaml
+model_list:
+  - model_name: claude-opus-4-7
+    litellm_params:
+      model: azure_ai/claude-opus-4-7
+      api_key: os.environ/AZURE_AI_API_KEY
+      api_base: os.environ/AZURE_AI_API_BASE  # https://<resource>.services.ai.azure.com
+```
+
+**2. Start the proxy**
+
+```bash
+docker run -d \
+  -p 4000:4000 \
+  -e AZURE_AI_API_KEY=$AZURE_AI_API_KEY \
+  -e AZURE_AI_API_BASE=$AZURE_AI_API_BASE \
+  -v $(pwd)/config.yaml:/app/config.yaml \
+  ghcr.io/berriai/litellm:litellm_stable_release_branch-v1.83.3-stable.opus-4.7 \
+  --config /app/config.yaml
+```
+
+**3. Test it!**
+
+```bash
+curl --location 'http://0.0.0.0:4000/chat/completions' \
+--header 'Content-Type: application/json' \
+--header 'Authorization: Bearer $LITELLM_KEY' \
+--data '{
+  "model": "claude-opus-4-7",
+  "messages": [
+    {
+      "role": "user",
+      "content": "what llm are you"
+    }
+  ]
+}'
+```
+
+</TabItem>
+</Tabs>
+
+## Usage - Vertex AI
+
+<Tabs>
+<TabItem value="proxy" label="LiteLLM Proxy">
+
+**1. Setup config.yaml**
+
+```yaml
+model_list:
+  - model_name: claude-opus-4-7
+    litellm_params:
+      model: vertex_ai/claude-opus-4-7
+      vertex_project: os.environ/VERTEX_PROJECT
+      vertex_location: us-east5
+```
+
+**2. Start the proxy**
+
+```bash
+docker run -d \
+  -p 4000:4000 \
+  -e VERTEX_PROJECT=$VERTEX_PROJECT \
+  -e GOOGLE_APPLICATION_CREDENTIALS=/app/credentials.json \
+  -v $(pwd)/config.yaml:/app/config.yaml \
+  -v $(pwd)/credentials.json:/app/credentials.json \
+  ghcr.io/berriai/litellm:litellm_stable_release_branch-v1.83.3-stable.opus-4.7 \
+  --config /app/config.yaml
+```
+
+**3. Test it!**
+
+```bash
+curl --location 'http://0.0.0.0:4000/chat/completions' \
+--header 'Content-Type: application/json' \
+--header 'Authorization: Bearer $LITELLM_KEY' \
+--data '{
+  "model": "claude-opus-4-7",
+  "messages": [
+    {
+      "role": "user",
+      "content": "what llm are you"
+    }
+  ]
+}'
+```
+
+</TabItem>
+</Tabs>
+
+## Usage - Bedrock
+
+<Tabs>
+<TabItem value="proxy" label="LiteLLM Proxy">
+
+**1. Setup config.yaml**
+
+```yaml
+model_list:
+  - model_name: claude-opus-4-7
+    litellm_params:
+      model: bedrock/anthropic.claude-opus-4-7
+      aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID
+      aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY
+      aws_region_name: us-east-1
+```
+
+**2. Start the proxy**
+
+```bash
+docker run -d \
+  -p 4000:4000 \
+  -e AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \
+  -e AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY \
+  -v $(pwd)/config.yaml:/app/config.yaml \
+  ghcr.io/berriai/litellm:litellm_stable_release_branch-v1.83.3-stable.opus-4.7 \
+  --config /app/config.yaml
+```
+
+**3. Test it!**
+
+```bash
+curl --location 'http://0.0.0.0:4000/chat/completions' \
+--header 'Content-Type: application/json' \
+--header 'Authorization: Bearer $LITELLM_KEY' \
+--data '{
+  "model": "claude-opus-4-7",
+  "messages": [
+    {
+      "role": "user",
+      "content": "what llm are you"
+    }
+  ]
+}'
+```
+
+</TabItem>
+</Tabs>
+
+## Advanced Features
+
+### Adaptive Thinking
+
+:::note
+When using `reasoning_effort` with Claude Opus 4.7, all values (`low`, `medium`, `high`, `xhigh`) are mapped to `thinking: {type: "adaptive"}`. To use explicit thinking budgets with `type: "enabled"`, pass the native `thinking` parameter directly.
+:::
+
+<Tabs>
+<TabItem value="completions" label="/chat/completions">
+
+LiteLLM supports adaptive thinking through the `reasoning_effort` parameter:
+
+```bash
+curl --location 'http://0.0.0.0:4000/chat/completions' \
+--header 'Content-Type: application/json' \
+--header 'Authorization: Bearer $LITELLM_KEY' \
+--data '{
+  "model": "claude-opus-4-7",
+  "messages": [
+    {
+      "role": "user",
+      "content": "Solve this complex problem: What is the optimal strategy for..."
+    }
+  ],
+  "reasoning_effort": "high"
+}'
+```
+
+</TabItem>
+<TabItem value="messages" label="/v1/messages">
+
+Use the `thinking` parameter with `type: "adaptive"` to enable adaptive thinking mode:
+
+```bash
+curl --location 'http://0.0.0.0:4000/v1/messages' \
+--header 'x-api-key: sk-12345' \
+--header 'content-type: application/json' \
+--data '{
+    "model": "claude-opus-4-7",
+    "max_tokens": 16000,
+    "thinking": {
+        "type": "adaptive"
+    },
+    "messages": [
+        {
+            "role": "user",
+            "content": "Explain why the sum of two even numbers is always even."
+        }
+    ]
+}'
+```
+
+</TabItem>
+</Tabs>
+
+### Effort Levels
+
+Claude Opus 4.7 supports four effort levels: `low`, `medium`, `high` (default), and `xhigh`. These give you finer-grained control over how much reasoning the model applies to a task. Pass the effort level via the `output_config` parameter.
+
+`xhigh` is a new effort level introduced with Opus 4.7 that sits above `high`. The `max` effort level is Claude Opus 4.6 only and is not available on 4.7.
+
+<Tabs>
+<TabItem value="completions" label="/chat/completions">
+
+```bash
+curl --location 'http://0.0.0.0:4000/chat/completions' \
+--header 'Content-Type: application/json' \
+--header 'Authorization: Bearer $LITELLM_KEY' \
+--data '{
+  "model": "claude-opus-4-7",
+  "messages": [
+    {
+      "role": "user",
+      "content": "Explain quantum computing"
+    }
+  ],
+  "output_config": {
+    "effort": "xhigh"
+  }
+}'
+```
+
+**Using OpenAI SDK:**
+
+```python
+import openai
+
+client = openai.OpenAI(
+    api_key="your-litellm-key",
+    base_url="http://0.0.0.0:4000"
+)
+
+response = client.chat.completions.create(
+    model="claude-opus-4-7",
+    messages=[{"role": "user", "content": "Explain quantum computing"}],
+    extra_body={"output_config": {"effort": "xhigh"}}
+)
+```
+
+**Using LiteLLM SDK:**
+
+```python
+from litellm import completion
+
+response = completion(
+    model="anthropic/claude-opus-4-7",
+    messages=[{"role": "user", "content": "Explain quantum computing"}],
+    output_config={"effort": "xhigh"},
+)
+```
+
+You can combine `reasoning_effort` with `output_config` for even more fine-grained control over the model's behavior.
+
+</TabItem>
+<TabItem value="messages" label="/v1/messages">
+
+```bash
+curl --location 'http://0.0.0.0:4000/v1/messages' \
+--header 'x-api-key: sk-12345' \
+--header 'content-type: application/json' \
+--data '{
+    "model": "claude-opus-4-7",
+    "max_tokens": 4096,
+    "messages": [
+        {
+            "role": "user",
+            "content": "Explain quantum computing"
+        }
+    ],
+    "output_config": {
+        "effort": "xhigh"
+    }
+}'
+```
+
+</TabItem>
+</Tabs>
+
+**Effort level guide:**
+
+| Effort | When to use |
+|--------|-------------|
+| `low` | Short, fast responses — simple lookups, formatting, classification |
+| `medium` | Balanced tradeoff for everyday Q&A and light reasoning |
+| `high` (default) | Complex reasoning, code generation, analysis |
+| `xhigh` | Hardest problems — multi-step math, deep research, agentic planning |
+