## Create a Message

`$client->beta->messages->create(int maxTokens, list<BetaMessageParam> messages, Model model, ?BetaCacheControlEphemeral cacheControl, ?Container container, ?BetaContextManagementConfig contextManagement, ?BetaDiagnosticsParam diagnostics, ?string fallbackCreditToken, ?list<BetaFallbackParam> fallbacks, ?string inferenceGeo, ?list<BetaRequestMCPServerURLDefinition> mcpServers, ?BetaMetadata metadata, ?BetaOutputConfig outputConfig, ?BetaJSONOutputFormat outputFormat, ?ServiceTier serviceTier, ?Speed speed, ?list<string> stopSequences, ?System system, ?float temperature, ?BetaThinkingConfigParam thinking, ?BetaToolChoice toolChoice, ?list<BetaToolUnion> tools, ?int topK, ?float topP, ?string userProfileID, ?list<AnthropicBeta> betas): BetaMessage`

**post** `/v1/messages`

Send a structured list of input messages with text and/or image content, and the model will generate the next message in the conversation.

The Messages API can be used for either single queries or stateless multi-turn conversations.

Learn more about the Messages API in our [user guide](https://docs.claude.com/en/docs/initial-setup)

### Parameters

- `maxTokens: int`

  The maximum number of tokens to generate before stopping.

  Note that our models may stop _before_ reaching this maximum. This parameter only specifies the absolute maximum number of tokens to generate.

  Set to `0` to populate the [prompt cache](https://docs.claude.com/en/docs/build-with-claude/prompt-caching#pre-warming-the-cache) without generating a response.

  Different models have different maximum values for this parameter.  See [models](https://docs.claude.com/en/docs/models-overview) for details.

- `messages: list<BetaMessageParam>`

  Input messages.

  Our models are trained to operate on alternating `user` and `assistant` conversational turns. When creating a new `Message`, you specify the prior conversational turns with the `messages` parameter, and the model then generates the next `Message` in the conversation. Consecutive `user` or `assistant` turns in your request will be combined into a single turn.

  Each input message must be an object with a `role` and `content`. You can specify a single `user`-role message, or you can include multiple `user` and `assistant` messages.

  If the final message uses the `assistant` role, the response content will continue immediately from the content in that message. This can be used to constrain part of the model's response.

  Example with a single `user` message:

  ```json
  [{"role": "user", "content": "Hello, Claude"}]
  ```

  Example with multiple conversational turns:

  ```json
  [
    {"role": "user", "content": "Hello there."},
    {"role": "assistant", "content": "Hi, I'm Claude. How can I help you?"},
    {"role": "user", "content": "Can you explain LLMs in plain English?"},
  ]
  ```

  Example with a partially-filled response from Claude:

  ```json
  [
    {"role": "user", "content": "What's the Greek name for Sun? (A) Sol (B) Helios (C) Sun"},
    {"role": "assistant", "content": "The best answer is ("},
  ]
  ```

  Each input message `content` may be either a single `string` or an array of content blocks, where each block has a specific `type`. Using a `string` for `content` is shorthand for an array of one content block of type `"text"`. The following input messages are equivalent:

  ```json
  {"role": "user", "content": "Hello, Claude"}
  ```

  ```json
  {"role": "user", "content": [{"type": "text", "text": "Hello, Claude"}]}
  ```

  See [input examples](https://docs.claude.com/en/api/messages-examples).

  Note that if you want to include a [system prompt](https://docs.claude.com/en/docs/system-prompts), you can use the top-level `system` parameter — there is no `"system"` role for input messages in the Messages API.

  There is a limit of 100,000 messages in a single request.

- `model: Model`

  The model that will complete your prompt.

  See [models](https://docs.anthropic.com/en/docs/models-overview) for additional details and options.

- `cacheControl?:optional BetaCacheControlEphemeral`

  Top-level cache control automatically applies a cache_control marker to the last cacheable block in the request.

- `container?:optional Container`

  Container identifier for reuse across requests.

- `contextManagement?:optional BetaContextManagementConfig`

  Context management configuration.

  This allows you to control how Claude manages context across multiple requests, such as whether to clear function results or not.

- `diagnostics?:optional BetaDiagnosticsParam`

  Request-level diagnostics. Currently carries the previous response
  id for prompt-cache divergence reporting.

- `fallbackCreditToken?:optional string`

  The `fallback_credit_token` from a prior refusal's `stop_details`.

  When a preceding request was refused and returned a `fallback_credit_token`,
  pass that code here on the retry to have the retry's cache-creation tokens
  for the prefix that was warm on the refused model billed at the cache-read
  rate. Must be redeemed by the same organization and workspace, with the same
  request body (optionally extended by one appended `assistant` message whose
  content is the partial text — with any trailing whitespace stripped from
  the final text block — and paired server-tool blocks streamed before the
  refusal; the appended-assistant form is not available for requests with
  `output_format` set or forced `tool_choice`), on an eligible fallback
  model, on the same platform,
  and within 5 minutes of the refusal; a mismatch is a 400. A token minted
  mid-server-tool-loop whose partial content was continuable may only be
  redeemed with the appended-assistant form — if an exact-body retry is
  rejected with a 400 saying the token must be redeemed by continuing the
  partial response, retry with the appended-assistant form instead.

  When the appended-assistant form is used on a model that otherwise disallows
  assistant-turn prefill, this token also authorizes that one prefill.

- `fallbacks?:optional list<BetaFallbackParam>`

  Opt-in server-side retry on one or more substitute models when the requested model declines for policy reasons. Tried in order: if the first entry also declines, the second is tried, and so on.

- `inferenceGeo?:optional string`

  Specifies the geographic region for inference processing. If not specified, the workspace's `default_inference_geo` is used.

- `mcpServers?:optional list<BetaRequestMCPServerURLDefinition>`

  MCP servers to be utilized in this request

- `metadata?:optional BetaMetadata`

  An object describing metadata about the request.

- `outputConfig?:optional BetaOutputConfig`

  Configuration options for the model's output, such as the output format.

- `outputFormat?:optional BetaJSONOutputFormat`

  Deprecated: Use `output_config.format` instead. See [structured outputs](https://platform.claude.com/docs/en/build-with-claude/structured-outputs)

  A schema to specify Claude's output format in responses. This parameter will be removed in a future release.

- `serviceTier?:optional ServiceTier`

  Determines whether to use priority capacity (if available) or standard capacity for this request.

  Anthropic offers different levels of service for your API requests. See [service-tiers](https://docs.claude.com/en/api/service-tiers) for details.

- `speed?:optional Speed`

  The inference speed mode for this request. `"fast"` enables high output-tokens-per-second inference.

- `stopSequences?:optional list<string>`

  Custom text sequences that will cause the model to stop generating.

  Our models will normally stop when they have naturally completed their turn, which will result in a response `stop_reason` of `"end_turn"`.

  If you want the model to stop generating when it encounters custom strings of text, you can use the `stop_sequences` parameter. If the model encounters one of the custom sequences, the response `stop_reason` value will be `"stop_sequence"` and the response `stop_sequence` value will contain the matched stop sequence.

- `stream?:optional bool`

  Whether to incrementally stream the response using server-sent events.

  See [streaming](https://docs.claude.com/en/api/messages-streaming) for details.

- `system?:optional System`

  System prompt.

  A system prompt is a way of providing context and instructions to Claude, such as specifying a particular goal or role. See our [guide to system prompts](https://docs.claude.com/en/docs/system-prompts).

- `temperature?:optional float`

  Amount of randomness injected into the response.

  Defaults to `1.0`. Ranges from `0.0` to `1.0`. Use `temperature` closer to `0.0` for analytical / multiple choice, and closer to `1.0` for creative and generative tasks.

  Note that even with `temperature` of `0.0`, the results will not be fully deterministic.

- `thinking?:optional BetaThinkingConfigParam`

  Configuration for enabling Claude's extended thinking.

  When enabled, responses include `thinking` content blocks showing Claude's thinking process before the final answer. Requires a minimum budget of 1,024 tokens and counts towards your `max_tokens` limit.

  See [extended thinking](https://docs.claude.com/en/docs/build-with-claude/extended-thinking) for details.

- `toolChoice?:optional BetaToolChoice`

  How the model should use the provided tools. The model can use a specific tool, any available tool, decide by itself, or not use tools at all.

- `tools?:optional list<BetaToolUnion>`

  Definitions of tools that the model may use.

  If you include `tools` in your API request, the model may return `tool_use` content blocks that represent the model's use of those tools. You can then run those tools using the tool input generated by the model and then optionally return results back to the model using `tool_result` content blocks.

  There are two types of tools: **client tools** and **server tools**. The behavior described below applies to client tools. For [server tools](https://docs.claude.com/en/docs/agents-and-tools/tool-use/overview#server-tools), see their individual documentation as each has its own behavior (e.g., the [web search tool](https://docs.claude.com/en/docs/agents-and-tools/tool-use/web-search-tool)).

  Each tool definition includes:

  * `name`: Name of the tool.
  * `description`: Optional, but strongly-recommended description of the tool.
  * `input_schema`: [JSON schema](https://json-schema.org/draft/2020-12) for the tool `input` shape that the model will produce in `tool_use` output content blocks.

  For example, if you defined `tools` as:

  ```json
  [
    {
      "name": "get_stock_price",
      "description": "Get the current stock price for a given ticker symbol.",
      "input_schema": {
        "type": "object",
        "properties": {
          "ticker": {
            "type": "string",
            "description": "The stock ticker symbol, e.g. AAPL for Apple Inc."
          }
        },
        "required": ["ticker"]
      }
    }
  ]
  ```

  And then asked the model "What's the S&P 500 at today?", the model might produce `tool_use` content blocks in the response like this:

  ```json
  [
    {
      "type": "tool_use",
      "id": "toolu_01D7FLrfh4GYq7yT1ULFeyMV",
      "name": "get_stock_price",
      "input": { "ticker": "^GSPC" }
    }
  ]
  ```

  You might then run your `get_stock_price` tool with `{"ticker": "^GSPC"}` as an input, and return the following back to the model in a subsequent `user` message:

  ```json
  [
    {
      "type": "tool_result",
      "tool_use_id": "toolu_01D7FLrfh4GYq7yT1ULFeyMV",
      "content": "259.75 USD"
    }
  ]
  ```

  Tools can be used for workflows that include running client-side tools and functions, or more generally whenever you want the model to produce a particular JSON structure of output.

  See our [guide](https://docs.claude.com/en/docs/tool-use) for more details.

- `topK?:optional int`

  Only sample from the top K options for each subsequent token.

  Used to remove "long tail" low probability responses. [Learn more technical details here](https://towardsdatascience.com/how-to-sample-from-language-models-682bceb97277).

  Recommended for advanced use cases only.

- `topP?:optional float`

  Use nucleus sampling.

  In nucleus sampling, we compute the cumulative distribution over all the options for each subsequent token in decreasing probability order and cut it off once it reaches a particular probability specified by `top_p`.

  Recommended for advanced use cases only.

- `userProfileID?:optional string`

  The user profile ID to attribute this request to. Use when acting on behalf of a party other than your organization.

- `betas?:optional list<AnthropicBeta>`

  Optional header to specify the beta version(s) you want to use.

### Returns

- `BetaMessage`

  - `string id`

    Unique object identifier.

    The format and length of IDs may change over time.

  - `?BetaContainer container`

    Information about the container used in the request (for the code execution tool)

  - `list<BetaContentBlock> content`

    Content generated by the model.

    This is an array of content blocks, each of which has a `type` that determines its shape.

    Example:

    ```json
    [{"type": "text", "text": "Hi, I'm Claude."}]
    ```

    If the request input `messages` ended with an `assistant` turn, then the response `content` will continue directly from that last turn. You can use this to constrain the model's output.

    For example, if the input `messages` were:

    ```json
    [
      {"role": "user", "content": "What's the Greek name for Sun? (A) Sol (B) Helios (C) Sun"},
      {"role": "assistant", "content": "The best answer is ("}
    ]
    ```

    Then the response `content` might be:

    ```json
    [{"type": "text", "text": "B)"}]
    ```

  - `?BetaContextManagementResponse contextManagement`

    Context management response.

    Information about context management strategies applied during the request.

  - `?BetaDiagnostics diagnostics`

    Response envelope for request-level diagnostics. Present (possibly
    null) whenever the caller supplied `diagnostics` on the request.

  - `Model model`

    The model that will complete your prompt.

    See [models](https://docs.anthropic.com/en/docs/models-overview) for additional details and options.

  - `"assistant" role`

    Conversational role of the generated message.

    This will always be `"assistant"`.

  - `?BetaRefusalStopDetails stopDetails`

    Structured information about a refusal.

  - `?BetaStopReason stopReason`

    The reason that we stopped.

    This may be one the following values:

    * `"end_turn"`: the model reached a natural stopping point
    * `"max_tokens"`: we exceeded the requested `max_tokens` or the model's maximum
    * `"stop_sequence"`: one of your provided custom `stop_sequences` was generated
    * `"tool_use"`: the model invoked one or more tools
    * `"pause_turn"`: we paused a long-running turn. You may provide the response back as-is in a subsequent request to let the model continue.
    * `"refusal"`: when streaming classifiers intervene to handle potential policy violations

    In non-streaming mode this value is always non-null. In streaming mode, it is null in the `message_start` event and non-null otherwise.

  - `?string stopSequence`

    Which custom stop sequence was generated, if any.

    This value will be a non-null string if one of your custom stop sequences was generated.

  - `"message" type`

    Object type.

    For Messages, this is always `"message"`.

  - `BetaUsage usage`

    Billing and rate-limit usage.

    Anthropic's API bills and rate-limits by token counts, as tokens represent the underlying cost to our systems.

    Under the hood, the API transforms requests into a format suitable for the model. The model's output then goes through a parsing stage before becoming an API response. As a result, the token counts in `usage` will not match one-to-one with the exact visible content of an API request or response.

    For example, `output_tokens` will be non-zero, even for an empty string response from Claude.

    Total input tokens in a request is the summation of `input_tokens`, `cache_creation_input_tokens`, and `cache_read_input_tokens`.

### Example

```php
<?php

require_once dirname(__DIR__) . '/vendor/autoload.php';

$client = new Client(apiKey: 'my-anthropic-api-key');

$betaMessage = $client->beta->messages->create(
  maxTokens: 1024,
  messages: [['content' => 'Hello, world', 'role' => 'user']],
  model: 'claude-opus-4-6',
  cacheControl: ['type' => 'ephemeral', 'ttl' => '5m'],
  container: [
    'id' => 'id',
    'skills' => [
      ['skillID' => 'pdf', 'type' => 'anthropic', 'version' => 'latest']
    ],
  ],
  contextManagement: [
    'edits' => [
      [
        'type' => 'clear_tool_uses_20250919',
        'clearAtLeast' => ['type' => 'input_tokens', 'value' => 0],
        'clearToolInputs' => true,
        'excludeTools' => ['string'],
        'keep' => ['type' => 'tool_uses', 'value' => 0],
        'trigger' => ['type' => 'input_tokens', 'value' => 1],
      ],
    ],
  ],
  diagnostics: ['previousMessageID' => 'previous_message_id'],
  fallbackCreditToken: 'x',
  fallbacks: [
    [
      'model' => 'claude-fable-5',
      'maxTokens' => 0,
      'outputConfig' => [
        'effort' => 'low',
        'format' => ['schema' => ['foo' => 'bar'], 'type' => 'json_schema'],
        'taskBudget' => ['total' => 1024, 'type' => 'tokens', 'remaining' => 0],
      ],
      'speed' => 'standard',
      'thinking' => [
        'budgetTokens' => 1024, 'type' => 'enabled', 'display' => 'summarized'
      ],
    ],
  ],
  inferenceGeo: 'inference_geo',
  mcpServers: [
    [
      'name' => 'name',
      'type' => 'url',
      'url' => 'url',
      'authorizationToken' => 'authorization_token',
      'toolConfiguration' => ['allowedTools' => ['string'], 'enabled' => true],
    ],
  ],
  metadata: ['userID' => '13803d75-b4b5-4c3e-b2a2-6f21399b021b'],
  outputConfig: [
    'effort' => 'low',
    'format' => ['schema' => ['foo' => 'bar'], 'type' => 'json_schema'],
    'taskBudget' => ['total' => 1024, 'type' => 'tokens', 'remaining' => 0],
  ],
  outputFormat: ['schema' => ['foo' => 'bar'], 'type' => 'json_schema'],
  serviceTier: 'auto',
  speed: 'standard',
  stopSequences: ['string'],
  system: [
    [
      'text' => 'Today\'s date is 2024-06-01.',
      'type' => 'text',
      'cacheControl' => ['type' => 'ephemeral', 'ttl' => '5m'],
      'citations' => [
        [
          'citedText' => 'cited_text',
          'documentIndex' => 0,
          'documentTitle' => 'x',
          'endCharIndex' => 0,
          'startCharIndex' => 0,
          'type' => 'char_location',
        ],
      ],
    ],
  ],
  temperature: 1,
  thinking: ['type' => 'adaptive', 'display' => 'summarized'],
  toolChoice: ['type' => 'auto', 'disableParallelToolUse' => true],
  tools: [
    [
      'inputSchema' => [
        'type' => 'object',
        'properties' => ['location' => 'bar', 'unit' => 'bar'],
        'required' => ['location'],
      ],
      'name' => 'name',
      'allowedCallers' => ['direct'],
      'cacheControl' => ['type' => 'ephemeral', 'ttl' => '5m'],
      'deferLoading' => true,
      'description' => 'Get the current weather in a given location',
      'eagerInputStreaming' => true,
      'inputExamples' => [['foo' => 'bar']],
      'strict' => true,
      'type' => 'custom',
    ],
  ],
  topK: 5,
  topP: 0.7,
  userProfileID: 'user_profile_id',
  betas: ['message-batches-2024-09-24'],
);

var_dump($betaMessage);
```

#### Response

```json
{
  "id": "msg_013Zva2CMHLNnXjNJJKqJ2EF",
  "container": {
    "id": "id",
    "expires_at": "2019-12-27T18:11:19.117Z",
    "skills": [
      {
        "skill_id": "pdf",
        "type": "anthropic",
        "version": "latest"
      }
    ]
  },
  "content": [
    {
      "citations": [
        {
          "cited_text": "cited_text",
          "document_index": 0,
          "document_title": "document_title",
          "end_char_index": 0,
          "file_id": "file_id",
          "start_char_index": 0,
          "type": "char_location"
        }
      ],
      "text": "Hi! My name is Claude.",
      "type": "text"
    }
  ],
  "context_management": {
    "applied_edits": [
      {
        "cleared_input_tokens": 0,
        "cleared_tool_uses": 0,
        "type": "clear_tool_uses_20250919"
      }
    ]
  },
  "diagnostics": {
    "cache_miss_reason": {
      "cache_missed_input_tokens": 0,
      "type": "model_changed"
    }
  },
  "model": "claude-opus-4-6",
  "role": "assistant",
  "stop_details": {
    "category": "cyber",
    "explanation": "explanation",
    "fallback_credit_token": "fallback_credit_token",
    "fallback_has_prefill_claim": true,
    "recommended_model": "recommended_model",
    "type": "refusal"
  },
  "stop_reason": "end_turn",
  "stop_sequence": null,
  "type": "message",
  "usage": {
    "cache_creation": {
      "ephemeral_1h_input_tokens": 0,
      "ephemeral_5m_input_tokens": 0
    },
    "cache_creation_input_tokens": 2051,
    "cache_read_input_tokens": 2051,
    "inference_geo": "inference_geo",
    "input_tokens": 2095,
    "iterations": [
      {
        "cache_creation": {
          "ephemeral_1h_input_tokens": 0,
          "ephemeral_5m_input_tokens": 0
        },
        "cache_creation_input_tokens": 0,
        "cache_read_input_tokens": 0,
        "input_tokens": 0,
        "model": "claude-fable-5",
        "output_tokens": 0,
        "type": "message"
      }
    ],
    "output_tokens": 503,
    "output_tokens_details": {
      "thinking_tokens": 0
    },
    "server_tool_use": {
      "web_fetch_requests": 2,
      "web_search_requests": 0
    },
    "service_tier": "standard",
    "speed": "standard"
  }
}
```
