> ## Documentation Index
> Fetch the complete documentation index at: https://docs.bytespike.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# claude-sonnet-4-6

> Anthropic's production workhorse mid-tier. Vision, tool use, prompt caching, extended thinking, and web search — together at Sonnet pricing. Available through the Anthropic Messages protocol and OpenAI Chat Completions.

`claude-sonnet-4-6` is the production workhorse of the Claude family — the model most ByteSpike customers default to when they need a model that does **everything** at a mid-tier price. Vision, tools, prompt caching, extended thinking, and web search are all available at the same 200K context as Opus; Opus reserves the top of the quality envelope for hard reasoning.

**Pricing:** $3.00 / 1M input, $15.00 / 1M output, \$0.30 / 1M cache read — see the [rate card](/pricing).

## Protocols

| Protocol                | Path                                                |
| ----------------------- | --------------------------------------------------- |
| Anthropic Messages      | `POST https://llm.bytespike.ai/v1/messages`         |
| OpenAI Chat Completions | `POST https://llm.bytespike.ai/v1/chat/completions` |

The two protocols produce equivalent responses for the same input; pick whichever your client already speaks.

## Quickstart

<CodeGroup>
  ```bash cURL theme={null}
  curl https://llm.bytespike.ai/v1/messages \
    -H "x-api-key: $BYTESPIKE_API_KEY" \
    -H "anthropic-version: 2023-06-01" \
    -H "content-type: application/json" \
    -d '{
      "model": "claude-sonnet-4-6",
      "max_tokens": 1024,
      "messages": [
        { "role": "user", "content": "Hello, ByteSpike." }
      ]
    }'
  ```

  ```python Python (anthropic SDK) theme={null}
  import anthropic

  client = anthropic.Anthropic(
      base_url="https://llm.bytespike.ai",
      api_key=os.environ["BYTESPIKE_API_KEY"],
  )

  resp = client.messages.create(
      model="claude-sonnet-4-6",
      max_tokens=1024,
      messages=[{"role": "user", "content": "Hello, ByteSpike."}],
  )

  print(resp.content[0].text)
  ```

  ```python Python (openai SDK) theme={null}
  from openai import OpenAI

  client = OpenAI(
      base_url="https://llm.bytespike.ai/v1",
      api_key=os.environ["BYTESPIKE_API_KEY"],
  )

  resp = client.chat.completions.create(
      model="claude-sonnet-4-6",
      messages=[{"role": "user", "content": "Hello, ByteSpike."}],
  )

  print(resp.choices[0].message.content)
  ```
</CodeGroup>

## Capabilities

| Capability                      | Supported   |
| ------------------------------- | ----------- |
| Chat completions                | ✅           |
| Streaming (SSE)                 | ✅           |
| Vision (image input)            | ✅           |
| Tool use (function calling)     | ✅ parallel  |
| Prompt caching (cache\_control) | ✅           |
| Extended thinking               | ✅           |
| Web search (web\_search tool)   | ✅           |
| JSON / structured output        | ✅           |
| Context window                  | 200K tokens |

## When to use

* **Default mid-tier.** When you need a model that does everything — vision + tools + thinking + web search — at Sonnet pricing.
* **Agents.** Use the Anthropic Messages endpoint; the `tool_use` and `thinking` blocks pass through transparently.
* **RAG.** `cache_control` on a long system prompt amortizes the cost across many user turns. The first request pays the cache-write tier; subsequent reads from the same prefix pay \$0.30/1M (10% of input).
* **Fresh-fact tasks.** Add `web_search` to the tools array.

When **not** to use:

* Hardest reasoning — go to [`claude-opus-4-8`](/models/claude-opus-4-8), the Claude flagship.
* Cheapest-possible classification or routing — use [`claude-haiku-4-5`](/models/claude-haiku-4-5) at one-quarter the price.

## Next

* [claude-opus-4-8](/models/claude-opus-4-8) — flagship, 200K context
* [claude-haiku-4-5](/models/claude-haiku-4-5) — small, fast, cheap