Standardizing LLM API Gateways: OpenAI's New Responses Lead

We evaluate major LLM API interfaces—OpenAI, Anthropic, Gemini—to determine the optimal standard for new API gateways, focusing on design, flexibility, and future-proofing for evolving AI capabilities.

The Answer Up Front

For anyone building a new LLM API gateway today, standardizing on OpenAI's new Responses interface is the most pragmatic choice. It offers a robust, extensible foundation that addresses many shortcomings of prior designs, particularly around multimodal inputs and structured outputs. Developers should skip older Chat Completions if they foresee any need for complex tool use, vision, or highly structured data exchange. The bottom line is that OpenAI's latest iteration provides the best balance of feature richness, developer familiarity, and a clear path for future AI advancements, making it the least painful interface to build against and abstract.

Methodology

This v0 review draws on the founder dmpiergiacomo's question on Reddit, published API documentation from OpenAI, Anthropic, and Google, and general industry discourse regarding LLM API design. Independent benchmarks are pending. Update cadence: re-tested when claims diverge from observed behavior or when significant API changes are announced.

• Tool name + version + date observed: OpenAI Chat Completions (legacy, observed May 2026), OpenAI Responses (new, observed May 2026), Anthropic Messages (observed May 2026), Gemini generateContent (current, observed May 2026), Gemini Interactions (beta, observed May 2026).
• Source signal URL: https://www.reddit.com/r/LocalLLaMA/comments/1toafm4/if_you_were_to_build_a_new_llm_api_gateway_today/
• What's covered in this review: This analysis covers the structural design, feature set, and extensibility of the listed LLM API interfaces as described in their official documentation. It includes considerations for multimodal input, tool calling, streaming, and error handling.
• What's NOT covered: This review does not include independent performance benchmarks, long-term workflow integration assessments, or edge-case behavior testing. It also does not cover the specific implementations of existing API gateways, only the underlying interfaces they would abstract.

What It Does

LLM API interfaces define how developers send prompts and receive responses from large language models. The interfaces under consideration represent different philosophies and stages of evolution in this rapidly developing field.

OpenAI Chat Completions (legacy)

This interface, widely adopted, primarily handles text-in, text-out interactions. It uses a messages array with role (system, user, assistant) and content fields. While it introduced basic function calling, its structure became strained with the advent of more complex tool use and multimodal inputs. It was designed for a simpler era of text-only chat.

OpenAI Responses (new)

The newer OpenAI interface, often referred to as the chat/completions endpoint with expanded capabilities, significantly overhauls how structured outputs and multimodal inputs are handled. It introduces a more flexible content field that can accept an array of objects, allowing for text, image URLs, and other media types. Tool calls are also more robustly integrated, moving towards a more declarative and extensible schema for complex interactions.

Anthropic Messages

Anthropic's messages API is known for its explicit system prompt and a strict turn-taking user/assistant message structure. It emphasizes safety and control, with a clear separation of roles. Its tool use capabilities are well-defined, though perhaps less flexible than OpenAI's latest, and it has strong support for multimodal inputs, particularly images, within its content blocks.

Gemini generateContent (current) and Gemini Interactions (beta)

Google's Gemini interfaces focus heavily on multimodal capabilities from the outset. generateContent is the primary endpoint, designed for seamless integration of text, images, and other data types within a single request. Gemini Interactions is a beta offering that suggests a move towards more stateful, conversational interactions, potentially simplifying multi-turn dialogues and complex application flows. Both emphasize Google's native multimodal model strengths.

What's Interesting / What's Not

The most interesting trend is the clear convergence towards multimodal inputs and structured tool calling. The legacy OpenAI Chat Completions is a relic of a text-first world; its function_call mechanism felt like an add-on. The new OpenAI Responses interface, along with Anthropic's Messages and Google's generateContent, are built from the ground up to handle images, audio, and other data types as first-class citizens within the content field. This is a crucial evolution for any gateway aiming to be future-proof.

What's less interesting, or rather, a persistent challenge, is the lack of true standardization. While all major players now support streaming and tool calling, the exact JSON schemas, error codes, and even the conceptual models for messages or parts still differ. This divergence means any gateway must still implement significant translation layers, increasing complexity and maintenance overhead. The Gemini Interactions beta is interesting for its potential to simplify state management, but its beta status and lack of widespread adoption make it a risky primary standard for a new gateway today. The explicit system prompt in Anthropic's API is a strong design choice for clarity and control, which other interfaces could learn from.

Pricing

This review focuses on API interfaces, not specific LLM providers or their pricing. The interfaces themselves do not have a direct cost. Pricing for using the underlying LLMs (OpenAI, Anthropic, Google Gemini) is determined by each vendor based on token usage, model type, and other factors, and is subject to frequent change. This pricing snapshot is as of May 2026.

Verdict

OpenAI's new Responses interface is the strongest candidate for standardization in a new LLM API gateway. Its design reflects the current state of advanced LLMs, supporting multimodal inputs and sophisticated tool use natively, rather than as an afterthought. While Anthropic's and Gemini's interfaces offer compelling features, OpenAI's widespread adoption and continuous evolution of its API make it the most practical choice for broad compatibility and future extensibility. Building against this interface allows a gateway to abstract away much of the complexity while remaining flexible enough to integrate other providers through translation layers as needed.

What We'd Test Next

For a v2 review, we would implement a minimal API gateway against each of these interfaces. Key benchmarks would include latency for various prompt complexities (text-only, multimodal, tool-calling), error handling robustness, and the overhead introduced by translation layers when converting between different vendor schemas. We would also evaluate the developer experience of integrating new models or features into a gateway built on each standard, specifically looking at the effort required to add a new tool or a new modality (e.g., video input) to the system. Finally, we would assess the real-world performance implications of streaming responses across these different API designs. We would also investigate how well each interface handles concurrent requests and rate limiting scenarios from the gateway's perspective.

The investor read

The ongoing fragmentation of LLM API interfaces signals a continued need for robust API gateway solutions, despite the Reddit author's claim that 'there are enough companies already doing this.' The rapid evolution from text-only to multimodal and complex tool-calling capabilities means existing gateways face significant re-architecture challenges. Companies that can offer a truly unified, future-proof abstraction layer, especially one that handles versioning and breaking changes gracefully across providers, will capture significant tooling spend. The market is moving towards 'AI middleware' that simplifies multi-provider strategies. An investable company in this space would demonstrate superior performance in translation overhead, comprehensive feature support (multimodal, tool-calling, streaming), and a clear strategy for adapting to new LLM capabilities without requiring extensive client-side changes. This is not a small, bootstrapped play if the goal is to be a foundational piece of AI infrastructure.