OpenAI Responses API vs. Chat Completions vs. Anthropic Messages API

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The LLM API landscape has never been more fragmented. As teams move from prototypes to production, the choice of which API format to build on shapes your vendor flexibility, your codebase complexity, and how quickly you can swap models when something better comes along.

Today, three API formats dominate how AI Agents talk to LLMs:

• OpenAI's Chat Completions API — a widely adopted standard 
• OpenAI's Responses API — the newer, agent-oriented evolution with built-in tools and state management
• Anthropic's Messages API — Claude's native interface, with capabilities like extended thinking and prompt caching
  
  

Each was designed with different goals in mind. Understanding the differences affects how you build, how you scale, and how locked in you are to a single provider.

API comparison at a glance

	Chat Completions	Responses API	Messages API
Provider	OpenAI	OpenAI	Anthropic
Endpoint	POST /v1/chat/completions	POST /v1/responses	POST /v1/messages
Design goal	Stateless text generation	Agentic workflows with built-in tools	Claude-native capabilities
State management	Manual (client-side)	Manual, previous_response_id, or Conversations API	Manual (client-side)
Streaming	Yes	Yes	Yes
Tool/function calling	Yes	Yes (with extensive built-in tools)	Yes
Built-in web search	Yes (via web_search_options)	Yes	Yes (via server tools)
Reasoning transparency	Reasoning effort controls	Reasoning effort controls	Adaptive thinking with visible reasoning blocks
Prompt caching	Yes	Yes	Yes
Computer use	No	Yes	Yes (beta)
Ecosystem compatibility	Broad	Growing	Claude-specific

What makes each format different

Chat Completions

Chat Completions (POST /v1/chat/completions) is a widely adopted LLM API format. The application sends an array of messages, each with a role (system, user, assistant, or tool), and the model replies. It is stateless by design: the application owns the conversation history and passes it with every request.

This simplicity is its primary strength. Because the model retains no memory between calls, the application has full control over what context the model sees. And because virtually every major provider has adopted this format, code written against Chat Completions works across OpenAI, Anthropic (via adapters), Google Gemini, Mistral, Amazon Bedrock, and others with minimal changes.

What it does well:

• Wide ecosystem of compatible tools, frameworks, and libraries
• Predictable, well-understood response format
• Supports prompt caching 
• Web search is available via web_search_options
• Portable across providers

Key limitations:

• No built-in agentic tools (code execution, file search, computer use require external orchestration)
• Does not currently support server-side state management
• No native reasoning transparency

Best suited for: Text generation workloads such as chatbots, summarization, classification, content generation, and Q&A. It is the right default for teams using frameworks that abstract over providers, or when cross-provider portability is a priority.

Responses API: built for autonomous agents

The Responses API (POST /v1/responses) is designed to run agentic loops where the model can call multiple built-in tools within a single API request, without the application orchestrating each step.

The built-in tool set has expanded significantly and now includes web search, file search, code interpreter, computer use, shell execution, image generation, file patching, and remote MCP server connections (with built-in connectors for services like Google Drive, Gmail, Microsoft Teams, Outlook, SharePoint, and Dropbox).

State management comes in three forms. The application can manage context manually, chain responses, or use the Conversations API for durable, long-lived conversation objects.

What it does well:

• Built-in tools that execute within a single request, reducing orchestration complexity
• Multiple state management options, including persistent server-side conversations
• Designed for multi-step agentic workflows where context and tool results accumulate

Key limitations:

• Natively available on OpenAI models only (though AI gateways can translate across providers)
• More complex response structure (output is an array of typed items rather than a single message)
• Server-side tool execution creates additional data exposure surface
• Higher complexity for security review due to the breadth of built-in tool capabilities

Best suited for: Autonomous agents that use built-in tools, or multi-turn workflows where server-side state management reduces token overhead. It is the right choice when agentic behavior and tool use are core to the application.

Messages API: Claude's native interface

Anthropic's Messages API (POST /v1/messages) is designed around how Claude operates. While it shares surface similarities with Chat Completions, it exposes capabilities that are specific to Claude's architecture.

What it does well:

• Adaptive thinking: Current Claude models return type: "thinking" content blocks that expose the model's reasoning process. On newer models, thinking is adaptive by default and depth is controlled via reasoning effort levels (low, medium, high, xhigh, max).
• Fine-grained prompt caching: Enables caching of specific content blocks with configurable TTLs, reducing latency and cost for repeated context.
• Rich content blocks: The response content array supports text, images, PDFs, tool use, thinking blocks, server tool results, and citations pointing to specific source documents and character ranges.
• Web search and fetch: Server-side web search and page fetching via the tools array, with dynamic filtering on current model versions.
• Granular stop reasons: stop_reason values include end_turn, max_tokens, stop_sequence, tool_use, pause_turn, and refusal (with structured details on newer models).

Key limitations:

• Does not currently support server-side state management (the application manages conversation history)
• Does not natively support compatibility with non-Anthropic providers without a translation layer

Best suited for: Applications building specifically on Claude that need reasoning transparency for complex problem-solving, prompt caching for document-heavy workloads, or rich content handling with citations.

The governance challenge: three formats, one security posture

For security and platform teams, the real challenge is not choosing one API format. It is maintaining consistent governance when development teams are using all three.

Governance concern	Impact of fragmentation
Observability	Each format returns different response structures. Without normalization, logging and monitoring require format-specific parsers.
Data exposure	Server-side state (Responses API Conversations), prompt caching (all three), and tool execution (Responses API, Messages API) each create distinct data residency considerations.
Access control	Different authentication patterns and API key scopes across providers make centralized access management difficult.
Cost attribution	Token counting, caching discounts, and pricing models differ by provider and format, complicating cost allocation across teams.
Policy enforcement	Content filtering, prompt injection detection, and output validation must work consistently regardless of which API format a request uses.

This is the problem that an AI gateway solves. Rather than building separate integrations, observability pipelines, and governance controls for each provider and format, a gateway provides a single policy enforcement point through which all AI traffic flows.

How Prisma AIRS™ AI Gateway addresses this

Prisma AIRS AI Gateway provides a centralized AI control plane that sits between applications and every LLM provider. It supports all three API formats natively, handling the translation so that governance policies, observability, and security controls apply uniformly regardless of which format or provider a request targets.

Unified observability: Every request is logged, traced, and searchable across all providers and API formats. Security teams get a single view into what data is being sent, which models are being called, and how responses are being used, with OpenTelemetry-compliant telemetry and filterable metrics.

Format-agnostic routing: Applications can use any of the three API formats with any supported provider. Prisma AIRS handles the translation automatically. This means development teams can choose the format that best fits their use case without creating provider lock-in, and platform teams can enforce a switch if needed without application code changes.

Security controls: Prompt injection scanning, data leakage reduction, content moderation, regex-based filtering, and JSON schema validation apply consistently across all traffic regardless of API format or destination provider.

Intelligent reliability. 

Failover and load balancing across providers and API keys: If one provider is down or rate-limited, traffic routes to a backup without application-level changes.

Granular cost governance: Unified cost tracking and attribution across all providers, models, and teams, with configurable budget limits and alerts.

As LLM capabilities evolve, the divergence between API formats will only accelerate. OpenAI is expanding the Responses API with new built-in tools and persistent state. Anthropic is deepening Claude's reasoning transparency and caching controls. 

For security and platform teams, the question is no longer which format to standardize on. It is how to maintain consistent governance as development teams adopt whichever format best fits their use case. A centralized AI gateway turns that fragmentation from a security liability into a managed, observable, policy-governed architecture, giving development teams the flexibility they need without sacrificing the visibility and control that security requires.

To learn more about how Prisma AIRS AI Gateway can help your organization manage AI security across providers and API formats, visit the Prisma AIRS AI Gateway page.