6d • General discussion
Mercury 2
Introducing Mercury 2: The Fastest Reasoning LLM
Mercury 2 is a new reasoning language model built for real production environments where speed actually matters.
Modern AI systems are no longer single prompt, single response. They run in loops with agents, retrieval pipelines, tool calls, and background jobs. In these systems, latency compounds across every step. Traditional LLMs decode one token at a time, which creates a built-in speed bottleneck.
Mercury 2 changes the architecture.
Instead of sequential decoding, it uses diffusion-based generation. It produces multiple tokens in parallel and refines them over a few steps. Think less typewriter and more editor revising a full draft at once. The result is over 5x faster generation and a fundamentally different speed curve.
Key highlights:
- 1,009 tokens per second on NVIDIA Blackwell GPUs
- $0.25 per 1M input tokens and $0.75 per 1M output tokens
- 128K context window
- Tunable reasoning
- Native tool use
- Structured JSON output
- OpenAI API compatible
The bigger shift is in the reasoning trade-off. Normally, better reasoning requires more test-time compute, which increases latency and cost. Diffusion-based reasoning delivers reasoning-grade quality within real-time latency budgets.
Where Mercury 2 shines:
- Coding and autocomplete where flow cannot be interrupted
- Agent workflows with many chained inference calls
- Real-time voice interfaces with tight latency constraints
- Search and RAG pipelines where multiple steps stack delay
Mercury 2 is built for production AI systems that need responsiveness under high concurrency, stable throughput, and consistent performance.
It is available now via early access and integrates into existing OpenAI-compatible stacks without rewrites.
The core idea is simple: faster reasoning unlocks better systems.
This will be interesting for building marketing ai agents. what uses do you see for it ?
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Mercury 2
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