Inception Labs introduced Mercury 3 on Monday, describing it as the most capable diffusion-based large language model released to date. According to the company's announcement, Mercury 3 outperforms speed-optimized frontier models including GPT-6 Luna and Claude Haiku 5.5 across a range of benchmarks while running 4 to 8 times faster, at more than 1,000 tokens per second.
The launch is the strongest answer yet to the question that has hung over commercial diffusion language models since the category emerged: can text diffusion scale to compete with autoregressive architectures on complex workloads, or does it remain a research curiosity optimized only for latency? Inception Labs, which was founded by former Stanford and UC Berkeley researchers including CEO Stefano Ermon, says the answer is now settled. The company reports that Mercury 3 is more than 40 times larger than Mercury 1 and that measured intelligence across standard benchmarks roughly tripled between Mercury 1 (mini) and Mercury 3, evidence, it argues, that the scaling laws which drove progress in autoregressive models hold for diffusion too. For more context on this story, see our ongoing AI industry coverage.
Speed, Context and Pricing
The headline figures are aggressive. Inception Labs says Mercury 3 generates 1,068 tokens per second on NVIDIA GPUs, maintaining the signature advantage of the diffusion approach over token-by-token autoregressive decoding. The context window has grown to 1 million tokens, up from 260,000 in Mercury 2.5, which the company released in September with 1,107 tokens per second of sustained output.
Mercury 3 is priced at $0.20 per million input tokens and $0.70 per million output tokens. At launch, the model is 50 percent off, bringing the effective rate to $0.10 per million input tokens and $0.35 per million output tokens, with cached tokens priced 90 percent below the input rate. That undercuts the typical pricing of the frontier speed-optimized models it benchmarks against, though independent verification of the quality claims has not yet been published.
Benchmark and Agent Results
Beyond the general benchmark comparisons against GPT-6 Luna and Claude Haiku 5.5, Inception Labs published results from its production web-agent harness on 50 live Mind2Web navigation tasks. Mercury 3 completed 50 percent of the tasks, which the company says is significantly more than GPT-6.1-Sol managed, while using the same token volume and the same number of agent steps as the frontier model at a fraction of the per-token cost.
The company positions latency-sensitive agent workloads as the core commercial case for the model. Because browser-use agents, voice assistants, and interactive applications wait on the model at every step, small delays compound quickly, and a diffusion model's high throughput translates directly into faster task completion and more attempts within a fixed time budget.
Early Customers Report Production Use
Inception Labs says early-access partners are already running Mercury 3 in production for voice and chat support, real-time tutoring, data engineering, and web navigation.
Shuhao Zhang, cofounder of TinyFish AI, said agents built on Mercury 3 finish sooner and have time to try more approaches before answering, whether they are navigating websites, researching across sources, or analyzing logs and tracing incidents.
Michael Tong, co-founder and CTO of General Learning, which operates RevisionDojo, said Mercury 3 allowed the company to give every student instant feedback without giving up reasoning quality, returning hints, marked answers, and definitions in about a second, and described the model as a drop-in swap for its existing stack.
Kuan Zou, founder of search startup Octen, reported that in the company's Sense API tests its own search took about 80 milliseconds while model generation was the bottleneck, and that Mercury 3 delivered the best balance of speed and quality among the fast models tested, with a median generation time of 0.9 seconds and a median end-to-end response time of 2.2 seconds.
Why Diffusion Scaling Matters
Autoregressive transformers, which generate text one token at a time in sequence, have carried essentially every frontier model from GPT through Gemini and Claude. Diffusion models instead refine an entire response in parallel through iterative denoising, a property that makes generation fast and controllable but which, until recently, lagged badly on complex reasoning tasks.
Ústředním tvrzením Mercury 3 je, že tato kvalitativní mezera se v měřítku uzavřela. Pokud čísla společnosti obstojí i při nezávislém hodnocení, praktickým důsledkem je důvěryhodná druhá rodina architektur pro komerční vyvození, s důsledky pro ekonomiku velkoobjemových pracovních zátěží agentů, kde agregované útratě tokenů dominují modely na úrovni rychlosti.
Nároky zatím zůstávají nahlášené dodavatelem. Společnost Inception Labs nepoukázala na hodnocení Mercury 3 od třetích stran a její srovnání s benchmarky pochází z jejích vlastních svazků. Dřívější modely společnosti jsou však veřejně nabízeny od roku 2025, což kupujícím umožňuje porovnávat.
Dostupnost
Mercury 3 je okamžitě k dispozici prostřednictvím API Inception Labs za zlevněnou sazbu, přičemž společnost uvádí, že tokeny uložené v mezipaměti jsou účtovány za 10 procent vstupní ceny. Nebylo oznámeno žádné vydání otevřených vah; stejně jako jeho předchůdci, Mercury 3 je komerční model pouze pro API.
Chcete-li pokračovat v pokrytí nových verzí modelů, difúzních architektur a rozšiřující se úrovně rychlosti hraniční umělé inteligence, sledujte AI Buzz Wire.
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