API Support Matrix

Valar gives you four endpoints. Three are inference surfaces shaped after APIs you already know, and one batches work asynchronously:

API	Endpoint	Maturity
OpenAI Responses	`POST /v1/responses`	Stable
OpenAI Chat Completions	`POST /v1/chat/completions`	Stable
Anthropic Messages	`POST /v1/messages`	Stable
Batch	`POST /v1/batches`	Private Preview

The same models and completion windows work across all three inference surfaces. The Batch API layers on top: it wraps a large set of Responses API calls into one asynchronous job.

Behavior shared across every API

Before the per-API detail, a few rules hold no matter which surface you call:

Streaming is available on Chat Completions and Messages. Pass stream: true and you get Server-Sent Events (chat.completion.chunk on Chat Completions, Anthropic-style events on Messages); add stream_options.include_usage on Chat Completions for a closing usage chunk. The Responses API rejects stream: true. For long jobs, use background: true on the Responses API and poll or wait on webhooks.
Completion windows steer scheduling and price. Set metadata.completion_window (or the X-Valar-Completion-Window header) to "asap" (the Now tier), "priority", "standard", or "flex" (background only). See Completion windows and Pricing.
Webhooks fire on completion. Set metadata.completion_webhook to receive a POST when processing finishes. See Webhooks.
Responses are always stored. store: false is unsupported.

Inference APIs

Each accordion below lists what the API accepts and what it rejects. Open the one that matches the SDK you’re using.

Responses API - POST /v1/responses

RecommendedOpenAI Responses formatOpenAI SDK compatibleAPI reference

This is the surface we recommend reaching for first.Supported

Feature	Details
Core parameters	`model`, `input` (string or message array), `max_output_tokens`, `temperature`, `top_p`, `user`, `prompt_cache_key`
Structured outputs	`text.format` with `type: "text"` or `type: "json_schema"`
Reasoning	`reasoning.effort` (`none` / `minimal` / `low` / `medium` / `high` / `xhigh`), `reasoning.generate_summary` (`auto` / `concise` / `detailed`)
Function tools	`tools` with `type: "function"` - client-side function calling with `name`, `description`, `parameters`, `strict`
Custom tools	`tools` with `type: "custom"`
Tool choice	`tool_choice`: `"none"`, `"auto"`, `"required"`, or a specific function/custom tool
Background mode	`background: true` returns `202` immediately; poll with `GET /v1/responses/{id}`
Prompt cache routing	`prompt_cache_key` is an optional routing hint for requests that share a large prompt prefix
Image input	`input_image` content blocks on multimodal models. Non-multimodal models accept text only.
Output logprobs	`include: ["message.output_text.logprobs"]` returns one logprob per output token (best effort; omitted for models served via a proxy that does not return logprobs).

Not yet supported

Feature	Notes
Streaming	`stream: true` is rejected. Every response comes back as one JSON object.
Instructions	`instructions` is unsupported. Put system messages straight into `input`.
Conversation chaining	`previous_response_id` and `conversation` are unsupported. Resend the full input on each call.
Prompt templates	The `prompt` parameter is unsupported.
Server-side tools	`web_search`, `file_search`, `code_interpreter`, `computer_use`, `mcp`, `image_generation`, `shell`, `apply_patch` are unsupported.
Multimodal input	Audio and file input blocks are unsupported. Image input works on multimodal models (see above).
Include	Accepted for compatibility when passed as an array of strings. A request is rejected if it includes `reasoning.encrypted_content`, `web_search_call.action.sources`, `code_interpreter_call.outputs`, `computer_call_output.output.image_url`, or `file_search_call.results`.
Truncation	`"disabled"` is the only accepted value; custom truncation strategies are unsupported.
Parallel tool calls	`parallel_tool_calls` is unsupported.
json_object format	`text.format.type: "json_object"` is unsupported. Reach for `"json_schema"` instead.
Service tier	`"auto"` is the only accepted value. Use `metadata.completion_window` to govern response timing instead.
Delete / cancel	`DELETE /v1/responses/{id}` and the cancel endpoints are not implemented.

Chat Completions API - POST /v1/chat/completions

OpenAI Chat Completions formatOpenAI SDK compatibleAPI reference

This surface streams chat.completion.chunk events.Supported

Feature	Details
Core parameters	`model`, `messages`, `max_completion_tokens`, `temperature`, `top_p`, `user`
Message roles	`system`, `user`, `assistant`, `tool`, `function` (deprecated), `developer`
Structured outputs	`response_format` with `type: "text"`, `"json_object"`, or `"json_schema"`
Reasoning	`reasoning_effort` (`none` / `minimal` / `low` / `medium` / `high` / `xhigh`)
Function tools	`tools` with `type: "function"` - standard `{type, function: {name, description, parameters, strict}}` format
Custom tools	`tools` with `type: "custom"`
Tool choice	`tool_choice`: `"none"`, `"auto"`, `"required"`, or a specific function/custom tool
Parallel tool calls	`parallel_tool_calls` is passed through
Metadata	`metadata` with string key-value pairs, including `completion_window` and `completion_webhook`
Streaming	`stream: true` returns Server-Sent Events (`chat.completion.chunk`); `stream_options.include_usage` adds a final usage chunk. Reasoning is streamed as `reasoning_content` deltas and tool calls are emitted atomically.
Image input	`image_url` content parts on multimodal models. Non-multimodal models accept text only.

Not yet supported

Feature	Notes
Multiple choices	`n` is required to be `1`.
Multimodal content	Audio (`input_audio`) content parts are unsupported. Image (`image_url`) input works on multimodal models (see above).
Sampling controls	`frequency_penalty`, `presence_penalty`, `logit_bias`, `stop`, `seed`, `top_logprobs`, `logprobs`, `verbosity` are unsupported.
Audio modality	Neither `audio` nor `modalities: ["audio"]` is supported.
Predicted output	`prediction` is unsupported.
Web search	`web_search_options` is unsupported.
Service tier	`"auto"` is the only accepted value.
CRUD endpoints	The `GET`, `POST`, and `DELETE` operations on stored completions are not implemented.
Deprecated fields	`max_tokens`, `functions`, and `function_call` are rejected; switch to their modern replacements.

What comes back

A response always carries exactly one choice (n=1).
finish_reason is either "stop" or "tool_calls" - values such as "length" and "content_filter" are never returned.
Neither system_fingerprint nor service_tier appears in responses.
logprobs is always null.

Messages API - POST /v1/messages

Anthropic Messages formatAnthropic SDK compatibleAPI reference

Valar’s Anthropic-compatible surface. The Anthropic Python SDK and the Claude Agent SDK work against it with a base URL and key change.Supported

Feature	Details
Core parameters	`model`, `max_tokens` (required), `messages`, `system` (string or content blocks)
Sampling	`temperature` (0–1), `top_p` (0–1), `top_k`
Streaming	`stream: true` returns Anthropic SSE events (`message_start`, `content_block_delta`, `message_delta`, `message_stop`)
Extended thinking	`thinking` with `type: "enabled"` and `budget_tokens`, or `output_config.effort` (`low` / `medium` / `high` / `xhigh` / `max`)
Structured outputs	`output_config.format` with `type: "json_schema"` (translated to the Responses API’s `text.format`); see Structured outputs
Function tools	`tools` with `name`, `description`, `input_schema`; `tool_choice` (`auto` / `any` / `{type:"tool",name}`)
Tool-result blocks	`tool_result` content blocks in subsequent `user` messages
Stop sequences	`stop_sequences`
Image input	`image` content blocks on multimodal models. Non-multimodal models accept text only.
Metadata	`metadata` with string key-value pairs, including `completion_window` and `completion_webhook`
Completion window header	`X-Valar-Completion-Window` (`asap` / `priority` / `standard` / `flex`) as a fallback when `metadata.completion_window` is absent
Count tokens	`POST /v1/messages/count_tokens` returns an estimated token count

Not yet supported

Feature	Notes
Multimodal content	Document (`document`) content blocks are unsupported. Image input works on multimodal models (see above).
Service tier	`service_tier` is unsupported. Use `metadata.completion_window` or the `X-Valar-Completion-Window` header.
Inference geo	`inference_geo` is unsupported.
Batches	`POST /v1/messages/batches` and its related endpoints are not implemented.

What comes back

stop_reason is end_turn, tool_use, or max_tokens. (A run halted on a stop_sequences match comes back as end_turn, not stop_sequence.)
The response content is one or more blocks: text, thinking, and tool_use.
usage includes input_tokens, output_tokens, cache_read_input_tokens, and cache_creation_input_tokens.

Authenticating with the Anthropic SDKValar accepts both Authorization: Bearer <key> (the OpenAI convention) and the Anthropic x-api-key: <key> header. With the Anthropic SDK, either auth_token or api_key works:

from anthropic import Anthropic

client = Anthropic(
    auth_token="YOUR_VALAR_API_KEY",  # sends Authorization: Bearer
    base_url="https://api.valarhq.ai",  # no /v1 — the SDK appends it
)

The anthropic-version header is neither required nor inspected, and errors come back in the OpenAI-style error envelope format. See Anthropic SDK & Claude Agent SDK for end-to-end examples.

Batch API

The Batch API runs large volumes of Responses API requests asynchronously. Every entry targets /v1/responses - you can’t batch /v1/chat/completions or /v1/messages today. One POST /v1/batches call takes up to 100,000 requests; you then poll GET /v1/batches/{id} for status and fetch each result by its custom_id. For the end-to-end workflow, see Sending Requests at Scale; for the request and response schemas, see the Batch API reference.

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API Support Matrix

Behavior shared across every API

Inference APIs

Batch API

​Behavior shared across every API

​Inference APIs

​Batch API

Behavior shared across every API

Inference APIs

Batch API