Retrieve Message Batch results
Streams the results of a Message Batch as a .jsonl file.
Each line in the file is a JSON object containing the result of a single request in the Message Batch. Results are not guaranteed to be in the same order as requests. Use the custom_id field to match results to requests.
Learn more about the Message Batches API in our user guide
Path ParametersExpand Collapse
message_batch_id: string
ID of the Message Batch.
Header ParametersExpand Collapse
Optional header to specify the beta version(s) you want to use.
UnionMember1 = "message-batches-2024-09-24" or "prompt-caching-2024-07-31" or "computer-use-2024-10-22" or 16 more
ReturnsExpand Collapse
BetaMessageBatchIndividualResponse = object { custom_id, result }
This is a single line in the response .jsonl file and does not represent the response as a whole.
custom_id: string
Developer-provided ID created for each request in a Message Batch. Useful for matching results to requests, as results may be given out of request order.
Must be unique for each request within the Message Batch.
Processing result for this request.
Contains a Message output if processing was successful, an error response if processing failed, or the reason why processing was not attempted, such as cancellation or expiration.
BetaMessageBatchSucceededResult = object { message, type }
id: string
Unique object identifier.
The format and length of IDs may change over time.
Information about the container used in the request (for the code execution tool)
id: string
Identifier for the container used in this request
expires_at: string
The time at which the container will expire.
Skills loaded in the container
skill_id: string
Skill ID
type: "anthropic" or "custom"
Type of skill - either 'anthropic' (built-in) or 'custom' (user-defined)
version: string
Skill version or 'latest' for most recent version
Content generated by the model.
This is an array of content blocks, each of which has a type that determines its shape.
Example:
[{"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:
[
{"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:
[{"type": "text", "text": "B)"}]
BetaTextBlock = object { citations, text, type }
Citations supporting the text block.
The type of citation returned will depend on the type of document being cited. Citing a PDF results in page_location, plain text results in char_location, and content document results in content_block_location.
BetaCitationCharLocation = object { cited_text, document_index, document_title, 4 more }
type: "char_location"
BetaCitationPageLocation = object { cited_text, document_index, document_title, 4 more }
type: "page_location"
BetaCitationContentBlockLocation = object { cited_text, document_index, document_title, 4 more }
type: "content_block_location"
BetaCitationsWebSearchResultLocation = object { cited_text, encrypted_index, title, 2 more }
type: "web_search_result_location"
BetaCitationSearchResultLocation = object { cited_text, end_block_index, search_result_index, 4 more }
type: "search_result_location"
type: "text"
BetaThinkingBlock = object { signature, thinking, type }
type: "thinking"
BetaRedactedThinkingBlock = object { data, type }
type: "redacted_thinking"
BetaToolUseBlock = object { id, input, name, 2 more }
type: "tool_use"
Tool invocation directly from the model.
BetaDirectCaller = object { type }
Tool invocation directly from the model.
type: "direct"
BetaServerToolCaller = object { tool_id, type }
Tool invocation generated by a server-side tool.
type: "code_execution_20250825"
BetaServerToolUseBlock = object { id, caller, input, 2 more }
Tool invocation directly from the model.
BetaDirectCaller = object { type }
Tool invocation directly from the model.
type: "direct"
BetaServerToolCaller = object { tool_id, type }
Tool invocation generated by a server-side tool.
type: "code_execution_20250825"
name: "web_search" or "web_fetch" or "code_execution" or 4 more
type: "server_tool_use"
BetaWebSearchToolResultBlock = object { content, tool_use_id, type }
BetaWebSearchToolResultError = object { error_code, type }
type: "web_search_tool_result_error"
type: "web_search_result"
type: "web_search_tool_result"
BetaWebFetchToolResultBlock = object { content, tool_use_id, type }
content: BetaWebFetchToolResultErrorBlock { error_code, type } or BetaWebFetchBlock { content, retrieved_at, type, url }
BetaWebFetchToolResultErrorBlock = object { error_code, type }
type: "web_fetch_tool_result_error"
BetaWebFetchBlock = object { content, retrieved_at, type, url }
Citation configuration for the document
source: BetaBase64PDFSource { data, media_type, type } or BetaPlainTextSource { data, media_type, type }
BetaBase64PDFSource = object { data, media_type, type }
media_type: "application/pdf"
type: "base64"
BetaPlainTextSource = object { data, media_type, type }
media_type: "text/plain"
type: "text"
title: string
The title of the document
type: "document"
retrieved_at: string
ISO 8601 timestamp when the content was retrieved
type: "web_fetch_result"
url: string
Fetched content URL
type: "web_fetch_tool_result"
BetaCodeExecutionToolResultBlock = object { content, tool_use_id, type }
BetaCodeExecutionToolResultError = object { error_code, type }
type: "code_execution_tool_result_error"
BetaCodeExecutionResultBlock = object { content, return_code, stderr, 2 more }
type: "code_execution_output"
type: "code_execution_result"
type: "code_execution_tool_result"
BetaBashCodeExecutionToolResultBlock = object { content, tool_use_id, type }
content: BetaBashCodeExecutionToolResultError { error_code, type } or BetaBashCodeExecutionResultBlock { content, return_code, stderr, 2 more }
BetaBashCodeExecutionToolResultError = object { error_code, type }
error_code: "invalid_tool_input" or "unavailable" or "too_many_requests" or 2 more
type: "bash_code_execution_tool_result_error"
BetaBashCodeExecutionResultBlock = object { content, return_code, stderr, 2 more }
type: "bash_code_execution_output"
type: "bash_code_execution_result"
type: "bash_code_execution_tool_result"
BetaTextEditorCodeExecutionToolResultBlock = object { content, tool_use_id, type }
content: BetaTextEditorCodeExecutionToolResultError { error_code, error_message, type } or BetaTextEditorCodeExecutionViewResultBlock { content, file_type, num_lines, 3 more } or BetaTextEditorCodeExecutionCreateResultBlock { is_file_update, type } or BetaTextEditorCodeExecutionStrReplaceResultBlock { lines, new_lines, new_start, 3 more }
BetaTextEditorCodeExecutionToolResultError = object { error_code, error_message, type }
error_code: "invalid_tool_input" or "unavailable" or "too_many_requests" or 2 more
type: "text_editor_code_execution_tool_result_error"
BetaTextEditorCodeExecutionViewResultBlock = object { content, file_type, num_lines, 3 more }
file_type: "text" or "image" or "pdf"
type: "text_editor_code_execution_view_result"
BetaTextEditorCodeExecutionCreateResultBlock = object { is_file_update, type }
type: "text_editor_code_execution_create_result"
BetaTextEditorCodeExecutionStrReplaceResultBlock = object { lines, new_lines, new_start, 3 more }
type: "text_editor_code_execution_str_replace_result"
type: "text_editor_code_execution_tool_result"
BetaToolSearchToolResultBlock = object { content, tool_use_id, type }
content: BetaToolSearchToolResultError { error_code, error_message, type } or BetaToolSearchToolSearchResultBlock { tool_references, type }
BetaToolSearchToolResultError = object { error_code, error_message, type }
error_code: "invalid_tool_input" or "unavailable" or "too_many_requests" or "execution_time_exceeded"
type: "tool_search_tool_result_error"
BetaToolSearchToolSearchResultBlock = object { tool_references, type }
type: "tool_reference"
type: "tool_search_tool_search_result"
type: "tool_search_tool_result"
BetaMCPToolUseBlock = object { id, input, name, 2 more }
name: string
The name of the MCP tool
server_name: string
The name of the MCP server
type: "mcp_tool_use"
BetaMCPToolResultBlock = object { content, is_error, tool_use_id, type }
Citations supporting the text block.
The type of citation returned will depend on the type of document being cited. Citing a PDF results in page_location, plain text results in char_location, and content document results in content_block_location.
BetaCitationCharLocation = object { cited_text, document_index, document_title, 4 more }
type: "char_location"
BetaCitationPageLocation = object { cited_text, document_index, document_title, 4 more }
type: "page_location"
BetaCitationContentBlockLocation = object { cited_text, document_index, document_title, 4 more }
type: "content_block_location"
BetaCitationsWebSearchResultLocation = object { cited_text, encrypted_index, title, 2 more }
type: "web_search_result_location"
BetaCitationSearchResultLocation = object { cited_text, end_block_index, search_result_index, 4 more }
type: "search_result_location"
type: "text"
type: "mcp_tool_result"
BetaContainerUploadBlock = object { file_id, type }
Response model for a file uploaded to the container.
type: "container_upload"
Context management response.
Information about context management strategies applied during the request.
applied_edits: array of BetaClearToolUses20250919EditResponse { cleared_input_tokens, cleared_tool_uses, type } or BetaClearThinking20251015EditResponse { cleared_input_tokens, cleared_thinking_turns, type }
List of context management edits that were applied.
BetaClearToolUses20250919EditResponse = object { cleared_input_tokens, cleared_tool_uses, type }
cleared_input_tokens: number
Number of input tokens cleared by this edit.
cleared_tool_uses: number
Number of tool uses that were cleared.
type: "clear_tool_uses_20250919"
The type of context management edit applied.
BetaClearThinking20251015EditResponse = object { cleared_input_tokens, cleared_thinking_turns, type }
cleared_input_tokens: number
Number of input tokens cleared by this edit.
cleared_thinking_turns: number
Number of thinking turns that were cleared.
type: "clear_thinking_20251015"
The type of context management edit applied.
The model that will complete your prompt.
See models for additional details and options.
UnionMember0 = "claude-opus-4-5-20251101" or "claude-opus-4-5" or "claude-3-7-sonnet-latest" or 17 more
The model that will complete your prompt.
See models for additional details and options.
"claude-opus-4-5-20251101"
Premium model combining maximum intelligence with practical performance
"claude-opus-4-5"
Premium model combining maximum intelligence with practical performance
"claude-3-7-sonnet-latest"
High-performance model with early extended thinking
"claude-3-7-sonnet-20250219"
High-performance model with early extended thinking
"claude-3-5-haiku-latest"
Fastest and most compact model for near-instant responsiveness
"claude-3-5-haiku-20241022"
Our fastest model
"claude-haiku-4-5"
Hybrid model, capable of near-instant responses and extended thinking
"claude-haiku-4-5-20251001"
Hybrid model, capable of near-instant responses and extended thinking
"claude-sonnet-4-20250514"
High-performance model with extended thinking
"claude-sonnet-4-0"
High-performance model with extended thinking
"claude-4-sonnet-20250514"
High-performance model with extended thinking
"claude-sonnet-4-5"
Our best model for real-world agents and coding
"claude-sonnet-4-5-20250929"
Our best model for real-world agents and coding
"claude-opus-4-0"
Our most capable model
"claude-opus-4-20250514"
Our most capable model
"claude-4-opus-20250514"
Our most capable model
"claude-opus-4-1-20250805"
Our most capable model
"claude-3-opus-latest"
Excels at writing and complex tasks
"claude-3-opus-20240229"
Excels at writing and complex tasks
"claude-3-haiku-20240307"
Our previous most fast and cost-effective
role: "assistant"
Conversational role of the generated message.
This will always be "assistant".
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 requestedmax_tokensor the model's maximum"stop_sequence": one of your provided customstop_sequenceswas 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.
stop_sequence: string
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.
type: "message"
Object type.
For Messages, this is always "message".
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.
Breakdown of cached tokens by TTL
ephemeral_1h_input_tokens: number
The number of input tokens used to create the 1 hour cache entry.
ephemeral_5m_input_tokens: number
The number of input tokens used to create the 5 minute cache entry.
cache_creation_input_tokens: number
The number of input tokens used to create the cache entry.
cache_read_input_tokens: number
The number of input tokens read from the cache.
input_tokens: number
The number of input tokens which were used.
output_tokens: number
The number of output tokens which were used.
The number of server tool requests.
web_fetch_requests: number
The number of web fetch tool requests.
web_search_requests: number
The number of web search tool requests.
service_tier: "standard" or "priority" or "batch"
If the request used the priority, standard, or batch tier.
type: "succeeded"
BetaMessageBatchErroredResult = object { error, type }
BetaInvalidRequestError = object { message, type }
type: "invalid_request_error"
BetaAuthenticationError = object { message, type }
type: "authentication_error"
BetaBillingError = object { message, type }
type: "billing_error"
BetaPermissionError = object { message, type }
type: "permission_error"
BetaNotFoundError = object { message, type }
type: "not_found_error"
BetaRateLimitError = object { message, type }
type: "rate_limit_error"
BetaGatewayTimeoutError = object { message, type }
type: "timeout_error"
BetaAPIError = object { message, type }
type: "api_error"
BetaOverloadedError = object { message, type }
type: "overloaded_error"
type: "error"
type: "errored"
BetaMessageBatchCanceledResult = object { type }
type: "canceled"
BetaMessageBatchExpiredResult = object { type }
type: "expired"
curl https://api.anthropic.com/v1/messages/batches/$MESSAGE_BATCH_ID/results \
-H 'anthropic-version: 2023-06-01' \
-H 'anthropic-beta: message-batches-2024-09-24' \
-H "X-Api-Key: $ANTHROPIC_API_KEY"