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Inception releases Mercury 2.5 diffusion LLM, claiming 1,107 tokens/s and 260K context

Inception releases Mercury 2.5 diffusion LLM, claiming 1,107 tokens/s and 260K context

  • Inception Labs released Mercury 2.5, a diffusion language model the company calls its largest to date, claiming 1,107 tokens per second on widely available NVIDIA GPUs and a 260K-token context window.
  • The company says Mercury 2.5 raises its internal intelligence measure by 40% over Mercury 2 while matching cost-optimized models including GPT-5.6 Luna (Low), Gemini 3.5 Flash-Lite, and Claude Haiku 4.5.
  • List pricing is $0.20 per million input tokens and $0.75 per million output tokens; launch pricing cuts this to $0.04 and $0.15, respectively.
  • Inception says it used production failure cases and customer feedback from Mercury 2 workloads to revise evaluations and focus training; the model adds tunable reasoning, parallel tool calls, and schema-aligned JSON.
  • The company also previewed Mercury Voice, with claimed time-to-first-token below 170 ms, and Mercury Router, which routes prompts among open and closed models by quality, speed, and cost.

Hacker News opinions

I like the model, but users should know Inception's terms say submissions may train its models unless they turn off "Improve the model for everyone" in API Platform settings.

I'm glad someone is seriously pursuing diffusion language models. Fast hosted models fill a useful business niche when local deployment is not required.

I saw "widely available GPUs" and hoped for open weights. It looks like it is not open, which is disappointing.

I can imagine Inception becoming an acquisition target if it gets a decent model. Fast diffusion sub-agents and nested sub-agents seem useful in orchestration.

I'm less bullish. Mercury and diffusion sub-agents already exist, and there has been no visible sea change. I do not see much economic moat in diffusion models.

I tested the 2.5 Preview. It is usable as a general chatbot and roughly comparable to some last-generation open-weight models, but nowhere near frontier models. Its price and speed are compelling, though general tool use and agentic coding performed worse with our custom harness.

Mercury 2.5 looks useful as an arbiter in llm-consortium. A judge model adds latency to a multi-model system, and roughly 1,100 tokens per second reduces that cost.

I used Mercury 2.0 for a voice agent. It was fast but personality emulation felt generic compared with Gemma, and its cache use missed more often than I liked. Support responded quickly and said they were working on it, so I will try 2.5.

The logical and historical text output I sampled so far looked good.

Their comparisons look misleading. They compare token speed against older Flash-style models, but intelligence only against Mercury 2, which makes me suspect the model is not very useful.

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