Speed, the rising product feature in AI

Developments in early 2026 show a change in how AI companies compete. Response speed, otherwise known as latency, appears to be moving from a background engineering concern to a visible product differentiator.

Within the first six weeks of 2026, OpenAI released a model built specifically for real-time interaction, Anthropic introduced a premium fast mode for its flagship model, and Nvidia launched hardware explicitly positioned around the cost and speed of generating AI responses.

These are three companies making independent product decisions that land on the same conclusion: speed is now a feature worth pricing, marketing, and engineering around.

Response speed, or latency, in this context, refers to how long it takes an AI system to fulfill a task (or respond) after receiving a request.

The feature details in this article are drawn from company announcements, developer documentation, and press releases.

Context and background

For most of the past three years, AI development has, primarily, been measured by capability. The differentiators were how well a model reasons, writes code, or generates images. Speed was seemingly a secondary concern, something engineers tried to optimize after the model worked.

That dynamic appears to be changing. As AI systems move into real-time workflows (such as in live coding sessions, customer support conversations, interactive tools) the time it takes to produce a response or solution directly affects whether the product is usable; or at least, that seems to be the industry reading right now.

A model that takes tens of seconds to suggest a code edit is a batch tool. A model that responds quickly enough for live interaction is a collaborator.

The shift is visible across multiple companies simultaneously, spanning different approaches to the same problem: smaller purpose-built models, premium inference tiers, and next-generation hardware.

OpenAI: introducing a model designed for speed

On February 12, OpenAI released a research preview of GPT-5.3-Codex-Spark, a smaller version of its GPT-5.3-Codex model designed specifically for real-time coding.^[1] OpenAI described it as the company's first model optimized for interactive, low-latency use rather than long-running autonomous tasks.

Codex-Spark delivers more than 1,000 tokens per second when served on hardware from Cerebras, a chip company specializing in low-latency inference.^[2]

The company reported an 80% reduction in per-roundtrip overhead and a 50% reduction in time-to-first-token.

Codex-Spark became available as a research preview for ChatGPT Pro subscribers through the Codex app, CLI, and VS Code extension.

The model is smaller and less capable than the full GPT-5.3-Codex on some benchmarks. But OpenAI acknowledged this tradeoff directly, positioning the two models as complementary: Codex-Spark for real-time iteration, and the full model for long-running, complex tasks.

Anthropic: making speed a premium tier

Just five days prior to the Codex-Spark announcement, Anthropic announced a fast mode for its flagship Claude Opus 4.6 model as a research preview on February 7.

Unlike OpenAI's approach of releasing a separate, smaller model, Anthropic is offering the same flagship model with faster inference at a higher price.

Anthropic said fast mode delivers up to 2.5x faster output token generation using the same model weights and capabilities as standard Opus 4.6. The feature is activated via the /fast command in Claude Code or the speed: "fast" parameter in the API.^[3]

The pricing reflects Anthropic's framing of speed as a premium product. Standard Opus 4.6 is priced at $5 per million input tokens and $25 per million output tokens. Fast mode is $30/$150, which is six times the standard rate.

While the approach is different, what’s similar between OpenAI and Anthropic is that both companies seemingly arrived at the same product conclusion: there’s demand in the market for faster responses.

Nvidia: hardware designed around inference cost

If we go back even earlier in the year, at CES 2026 on January 5, Nvidia launched its Rubin platform, a six-chip architecture the company positioned around reducing the cost and increasing the speed of AI inference, which can translate into faster user-visible responses in interactive systems.^[4]

Nvidia's positioning is notable because it places inference performance, which is the speed and cost of generating each token a user sees, at the center of its product narrative, alongside training performance.

Taken together, these updates suggest companies now see latency as part of the user experience, not just a backend metric.

Why this matters

These three developments come from companies deeply involved in AI products (two model providers and the world’s primary chipmaker) and they converge on the same point. Each seems to be treating response speed as a feature that should be available, not just an engineering metric to optimize in the background.

For developers, faster inference means tighter feedback loops. For companies deploying AI at scale, lower cost per token determines whether a use case is economically viable. The fact that two competing model providers independently chose to ship speed-focused products in the same week suggests companies believe demand exists.

The tradeoffs are real and differ by approach. OpenAI acknowledged that Codex-Spark is less capable than its full model. Anthropic's fast mode preserves capability but at six times the cost.

Outlook

The developments described above do not, on their own, prove a durable trend by any means. But when multiple companies independently prioritize the same feature in the same timeframe, it is worth paying attention.

What remains uncertain is where the quality floor sits and what price the market will bear. Faster responses are only valuable if they are accurate and reliable enough for the task, and premium speed tiers are only sustainable if enough developers find the cost worthwhile. How companies navigate those tradeoffs (and whether users notice the difference) may very well shape which real-time AI products succeed beyond their initial deployments.

All in all, speed is entering the feature list.