Mercury 2
Mercury 2 is Inception's reasoning diffusion language model. It refines tokens in parallel with tunable reasoning depth, native tool use, and a context window of 128K tokens.
import { streamText } from 'ai'
const result = streamText({ model: 'inception/mercury-2', prompt: 'Why is the sky blue?'})Frequently Asked Questions
What makes Mercury 2 architecturally different from other reasoning models?
It uses diffusion instead of autoregressive generation. Mercury 2 starts with a draft of the full response and refines all token positions simultaneously across iterative steps, rather than generating one token at a time left to right. That follows the same conceptual lineage as image and video diffusion models, applied to language.
How does tunable reasoning depth work in Mercury 2?
You adjust the number of diffusion refinement steps at inference time. Fewer steps yield faster responses; more steps let the model converge on higher-quality answers. You match compute to task difficulty on each request.
What throughput does Mercury 2 achieve compared to autoregressive reasoning models?
Mercury 2 generates faster than autoregressive approaches. Live throughput metrics appear on this page.
Is Mercury 2 compatible with OpenAI client libraries?
Yes. Mercury 2 exposes an OpenAI-compatible API. Route existing codebases that use the OpenAI SDK to Mercury 2 through AI Gateway by swapping the base URL and model identifier.
What context length does Mercury 2 support?
A context window of 128K tokens. That suits long document processing, extended conversation history, and multi-document retrieval tasks.
Does Mercury 2 support structured output for agent orchestration?
Yes. Mercury 2 includes native schema-aligned JSON output and tool use. You can plug it into function-calling orchestration frameworks without extra parsing middleware.
How is Mercury 2 priced?
This page lists the current rates. Multiple providers can serve Mercury 2, so AI Gateway surfaces live pricing rather than a single fixed figure.