hile the AI industry has bet heavily on LLMs and the massive data centers needed to scale them, one of the neolabs betting on a contrarian AI just released another new model that challenges the incumbent thesis.
On Monday, Inception announced Mercury 3, its latest flagship model that's a diffusion LLM that uses different technology than traditional LLMs, generates text much faster at a lower cost, and is now matching low- and mid-tier models from Anthropic and OpenAI on performance and quality. Inception also reported that it implemented swarms of Mercury 3 agents for recursive self improvement where they increased the overall speed of the model by 16% on top of the upgrades made by the Inception team itself.
"Mercury 3 is the most capable diffusion-based language model on the market," Inception CEO Stefano Ermon told The Deep View. "It's very intelligent. It's comparable to the Luna and Haiku models, but it's way faster. It's getting 1,000 tokens per second. It's almost instant in the way you get answers from these models."
Mercury 3 costs $0.10 per million input tokens and $0.35 per million output tokens. Compared to the leading models from Anthropic's Haiku 5.5 and OpenAI's GPT-6 Luna, that's the same price for input tokens and a 35% discount on output tokens. However, that's based on the launch price for Mercury 3, which is 50% off the eventual standard price. Inception hasn't said how long the launch promotion period will last. But Ermon told The Deep View that the company is working on developments that could allow it to make the launch price permanent in the weeks ahead.
However, thinking of Mercury 3 as a low-cost model isn't the right framing. Anthropic and OpenAI offer cut-rate prices on their budget models so that they can offer a low-cost option while charging steep premiums for their best models. Mercury 3 is Inception's flagship model and its number one benefit is speed more than cost. Inception claims that Mercury 3 performs 4x to 8x faster than Haiku 5.5, GPT-6 Luna, and GLM-5.3 Flash while performing better than all of them on agentic benchmarks.
The way Mercury 3 pulls this off is by parallelizing inference. Conventional LLMs generate text one token at a time in sequence. That's an autoregressive process, meaning that each token is based on the ones before it. Meanwhile, Mercury 3 can generate and refine multiple tokens at the same time. This takes advantage of the enormous parallel processing capabilities of GPUs in a much faster and more efficient way. That's what allows Inception's models to produce responses faster and at a lower cost. Diffusion models are used by many of the leading labs for image and video generation. It's the application of the technology to text that's the breakthrough here.
Mercury 3 is now using 40x as many parameters as Mercury 1 (released in February 2025), but the Inception team isn't saying how many total parameters are in the model. That likely means it's a smaller number and Inception doesn't want people to compare apples to oranges and underestimate the model. Still, Mercury 3 shows that diffusion LLMs are advancing rapidly and could match traditional frontier LLMs in the next year. That's likely why Google DeepMind is quietly working on the same technology as well, releasing DiffusionGemma in June. By the way, it uses 26 billion parameters (miniscule compared to the 1 trillion parameter frontier models) but also delivers similar breakthroughs in speed to Inception's Mercury models.
Our Deeper View
Mercury 3 calls into question the current approach of most of the leading AI labs, which have bet their architecture on building bigger LLMs and then count on faster hardware (and more of it) from Nvidia and other chipmakers to deliver improvements over time. If Inception is right about diffusion LLMs, then it would have big implications. The dilemma facing the AI industry is that improving model intelligence requires additional compute, and lots of it. Upgrading reasoning and increasing the number of agentic steps can typically increase costs and response time. So Inception's breakthrough on diffusion LLMs is all about faster AI response times and more efficient use of expensive GPU hardware, for now. The question remains whether it will be a niche use-case for more real-time AI applications or a bigger long-term challenger to the cutting edge frontier models.