docs: add Meta Model API (muse-spark-1.1) provider page

docs/providers/meta.md

@@ -0,0 +1,109 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# Meta Model API
+https://ai.meta.com/blog/introducing-muse-spark-meta-model-api/
+
+Meta's Model API is an OpenAI-compatible endpoint (`https://api.meta.ai/v1`) serving `muse-spark-1.1`, Meta Superintelligence Labs' multimodal reasoning model for agentic and coding workloads. It exposes both the Chat Completions and Responses endpoints, so you can use it with LiteLLM by setting `meta/` as the model prefix.
+
+This is a distinct provider from `meta_llama` (the Llama API); use `meta/` for the Meta Model API.
+
+## API Key
+```python
+import os
+os.environ['META_API_KEY'] = ""       # your Meta Model API key from https://dev.meta.ai
+# optional: override the base url
+os.environ['META_API_BASE'] = ""      # defaults to https://api.meta.ai/v1
+```
+
+## Sample Usage
+```python
+from litellm import completion
+import os
+
+os.environ['META_API_KEY'] = ""
+response = completion(
+    model="meta/muse-spark-1.1",
+    messages=[{"role": "user", "content": "hello from litellm"}],
+)
+print(response)
+```
+
+## Sample Usage - Streaming
+```python
+from litellm import completion
+import os
+
+os.environ['META_API_KEY'] = ""
+response = completion(
+    model="meta/muse-spark-1.1",
+    messages=[{"role": "user", "content": "hello from litellm"}],
+    stream=True,
+)
+for chunk in response:
+    print(chunk)
+```
+
+## Supported Models
+
+| Model Name | Function Call |
+|------------|---------------|
+| muse-spark-1.1 | `completion(model="meta/muse-spark-1.1", messages)` |
+
+`muse-spark-1.1` has a 1M-token context window and supports multimodal input (image, video, PDF), tool calling and parallel tool calling, structured output, prompt caching, and web search grounding via the Responses API.
+
+## Reasoning
+
+`muse-spark-1.1` is a reasoning model. Control how much it thinks with `reasoning_effort`, which accepts `minimal`, `moderate`, `high`, and `xhigh`. The thinking tokens are returned as `usage.completion_tokens_details.reasoning_tokens` and are billed at the output rate, so matching effort to the task is the main lever on cost.
+
+<Tabs>
+<TabItem value="sdk" label="SDK">
+
+```python
+from litellm import completion
+import os
+
+os.environ['META_API_KEY'] = ""
+resp = completion(
+    model="meta/muse-spark-1.1",
+    messages=[{"role": "user", "content": "Diagnose this failing test and propose a fix."}],
+    reasoning_effort="high",
+)
+print(resp.choices[0].message.content)
+print(resp.usage.completion_tokens_details.reasoning_tokens)
+```
+
+</TabItem>
+<TabItem value="proxy" label="PROXY">
+
+1. Setup config.yaml
+
+```yaml
+model_list:
+  - model_name: muse-spark-1.1
+    litellm_params:
+        model: meta/muse-spark-1.1
+        api_key: os.environ/META_API_KEY
+```
+
+2. Run proxy
+
+```bash
+litellm --config config.yaml
+```
+
+3. Test it!
+
+```bash
+curl -L -X POST 'http://0.0.0.0:4000/v1/chat/completions' \
+-H 'Content-Type: application/json' \
+-H 'Authorization: Bearer sk-1234' \
+-d '{
+    "model": "muse-spark-1.1",
+    "messages": [{"role": "user", "content": "Say hello in exactly 3 words."}],
+    "reasoning_effort": "minimal"
+}'
+```
+
+</TabItem>
+</Tabs>

sidebars.js

@@ -1101,6 +1101,7 @@ const sidebars = {
         "providers/llamagate",
         "providers/lm_studio",
         "providers/manus",
+        "providers/meta",
         "providers/meta_llama",
         "providers/milvus_vector_stores",
         "providers/mistral",