# Mercury 2 Hits 1,009 Tokens/Sec by Ditching the Way Every Other LLM Works **URL:** \ **Category:** News & Articles\ **Tags:** ai\ **Created:** [February 25, 2026, 1:49pm UTC](https://onehack.st/t/mercury-2-hits-1-009-tokens-sec-by-ditching-the-way-every-other-llm-works/318875 "2026-02-25T13:49:39Z")\ **Posts on this page:** 1\ **Page:** 1
**Author:** ![Margaret](https://onehack.st/user_avatar/onehack.st/margaret/32/174872_2.png) [@Margaret](https://onehack.st/u/Margaret)\ **Post date:** [February 25, 2026, 1:49pm UTC](https://onehack.st/t/mercury-2-hits-1-009-tokens-sec-by-ditching-the-way-every-other-llm-works/318875/1 "2026-02-25T13:49:40Z")
# ⚡ Mercury 2 Hits 1,009 Tokens/Sec by Ditching the Way Every Other LLM Works _A Stanford professor’s startup just proved that AI text doesn’t have to come out one word at a time — and it’s 10x faster than the competition_ **1,009 tokens per second. $0.25 per million input tokens. End-to-end latency of 1.7 seconds vs 23.4 seconds for Claude Haiku 4.5.** Inception Labs just dropped Mercury 2 — the first reasoning model that doesn’t generate text the same way literally every other LLM does. And the speed gap is absolutely bonkers. ![Speed](https://media.giphy.com/media/l3V0j3ytFyGHqiV7W/giphy.gif) * * * > **🧩 Dumb Mode Dictionary** > > | Term | What It Actually Means | > | --- | --- | > | **Autoregressive decoding** | How every normal LLM works — it spits out one token (word chunk) at a time, left to right, like a typewriter | > | **Diffusion model** | A technique that starts with noise and refines it into something coherent — used in image AI like Midjourney, now applied to text | > | **dLLM** | Diffusion Large Language Model — Inception’s name for their new breed of text AI | > | **Tokens per second** | How fast an AI can produce output — higher = faster responses | > | **Denoising** | The process of cleaning up a rough draft into polished output across multiple passes | > | **128K context window** | How much text the model can “see” at once — about 200 pages of text | > **📖 The Backstory: A Stanford Professor Said 'What If?'** > > WAIT — so here’s the thing. Every single LLM you’ve ever used — ChatGPT, Claude, Gemini, all of them — works the same way under the hood. They produce text one token at a time, left to right, like a really fast typewriter. That’s called autoregressive generation. > > Stefano Ermon, a Stanford CS professor, co-invented the diffusion technique that powers Midjourney and DALL-E for images. Back in 2019, he asked a wild question: what if you could do the same thing with text? > > People told him it couldn’t work. Text isn’t like pixels — you can’t just “denoise” words the way you sharpen a blurry image. But his lab kept grinding. Years of research later, Inception Labs emerged from stealth in February 2025, raised $50M from Microsoft, NVIDIA, and Snowflake, and now they’ve shipped Mercury 2. > > The founding team is stacked: Ermon from Stanford, Aditya Grover from UCLA, and Volodymyr Kuleshov from Cornell. Three professors who decided the entire LLM industry was building on a slow foundation. > **⚙️ How Diffusion Text Actually Works (Simply)** > > Okay so imagine every other LLM is a person writing an essay one word at a time, never going back. Mercury 2 is more like an editor who starts with a rough draft of the ENTIRE response and then refines the whole thing at once — multiple times, really fast. > > Here’s the process: > > - **Step 1:** Model generates a rough “noisy” sketch of the full output — all tokens at once > - **Step 2:** It runs through several refinement passes, improving many tokens simultaneously (this is the “denoising”) > - **Step 3:** After a small number of passes, the output converges into clean text > > Because it’s refining in parallel instead of generating sequentially, the GPU gets used way more efficiently. That’s where the 10x speed comes from. Not from a faster chip — from a fundamentally different approach. > > ![Fast](https://media.giphy.com/media/d4blalI6x2oc4xAA/giphy.gif) > **📊 The Numbers: Mercury 2 vs The Competition** > > | Metric | Mercury 2 | Claude Haiku 4.5 | GPT 5.2 Mini | Gemini 3 Flash | > | --- | --- | --- | --- | --- | > | **Output speed** | 1,009 tok/s | ~89 tok/s | ~71 tok/s | — | > | **End-to-end latency** | 1.7s | 23.4s | — | 14.4s | > | **Input price (per 1M tokens)** | $0.25 | $1.00 | — | $0.50 | > | **Output price (per 1M tokens)** | $0.75 | $5.00 | — | $3.00 | > | **AIME 2025** | 91.1 | — | — | — | > | **GPQA** | 73.6 | — | — | — | > | **LiveCodeBench** | 67.3 | — | — | — | > | **Context window** | 128K | 200K | 128K | 1M | > > So it’s roughly 4x cheaper than Claude Haiku on input and nearly 7x cheaper on output. The quality benchmarks put it in the same tier as Haiku and GPT 5.2 Mini — not frontier-level, but solid for agent loops and batch processing. > **🗣️ What People Are Saying** > > The Hacker News crowd is cautiously excited but not throwing a parade yet: > > - **The speed believers:** “Intelligence per second matters — fast responses give you faster iteration loops.” Developers building multi-step agents see massive potential here. > - **The skeptics:** Several testers said it “fails the car wash test” (a common reasoning trick question) and gets “easily fooled by usual trick questions.” Coding performance? Decent. Deep reasoning? Not there yet. > - **The realists:** The positioning is Haiku-tier, not Opus-tier. It’s a workhorse for agent pipelines, not a replacement for your smartest model. > - **The annoyed:** Their demo site buckled under traffic on launch day — kind of ironic for a model selling itself on speed. > > NVIDIA gave a quote calling it proof of “what’s possible when new model architecture meets NVIDIA AI infrastructure.” (They’re investors, so take it with a grain of salt.) > **🔍 Why This Is a Bigger Deal Than It Looks** > > Here’s what got me (honestly) excited. Every major LLM company — OpenAI, Anthropic, Google, Meta — is stuck on the same autoregressive bottleneck. They can make models smarter, but making them fundamentally faster at generation has been mostly about hardware and optimization tricks. > > Mercury 2 is a proof that a completely different architecture can work for text. Google apparently explored similar diffusion approaches but never shipped anything commercial. Inception just… did it. > > The real play isn’t Mercury replacing your main AI brain. It’s Mercury running in agent loops where you need 50-100 fast calls in sequence. At 1.7 seconds per round-trip vs 23 seconds, your 20-step agent pipeline goes from an 8-minute coffee break to a 34-second wait. That changes what’s buildable. > > The API is OpenAI-compatible, which means you can swap it into existing toolchains with minimal effort. > > ![Mind Blown](https://media.giphy.com/media/OK27wINdQS5YQ/giphy.gif) * * * #### Cool. So There’s a Ridiculously Fast AI That Costs Almost Nothing. Now What the Hell Do We Do? (•̀ᴗ•́)و ![Lets Go](https://media.giphy.com/media/sRKg9r2YWeCTG5JTTo/giphy.gif) > **💰 Hustle 1: Build a Real-Time AI Chatbot Service for Local Businesses** > > Most small businesses still think “AI chatbot” means a clunky widget from 2019. With Mercury 2’s speed, you can build something that responds so fast it genuinely feels like a real human — no awkward loading spinners. > > Package it as a monthly service: customer support bot, appointment booking, FAQ handler. Charge $200-500/mo per client. Your API costs per customer will be basically nothing at these prices. > > 🧠 _Example:_ A freelance dev in Porto, Portugal built a WhatsApp-based appointment bot for dental clinics using a cheap API model. He charges €300/mo per clinic and has 14 clients — roughly €4,200/mo with near-zero infra costs. > > > 📈 **Timeline:** Weekend to prototype → 2 weeks to polish → start pitching local businesses month 1 > **🔧 Hustle 2: Sell High-Speed AI Data Extraction Pipelines to Agencies** > > Marketing agencies and SEO shops need to process thousands of web pages, extract structured data, and generate reports. Current LLM costs and latency make this expensive and slow. Mercury 2 at $0.25/M input tokens and sub-2-second responses makes batch processing absurdly cheap. > > Build a pipeline that scrapes competitor sites, extracts pricing/product data, and spits out formatted reports. Charge per report or per 1,000 pages processed. > > 🧠 _Example:_ A data analyst in Medellín, Colombia built a competitor price monitoring tool for e-commerce brands using fast API calls. She charges $800/mo per brand for daily price reports across 50 competitor sites. Running 6 clients now — $4,800/mo. > > > 📈 **Timeline:** 1 week to build scraper + extraction pipeline → Demo to 3 agencies → First paying client in 2-3 weeks > **📱 Hustle 3: Create an AI Coding Assistant Wrapper With Instant Responses** > > People pay for Cursor, Copilot, and all these AI coding tools partly because speed matters when you’re in the zone. Mercury 2’s latency means you could build a VS Code extension or terminal tool that returns code suggestions before the user finishes reading their own question. > > Target specific niches — a PHP-specific assistant, a WordPress plugin generator, a Shopify theme tweaker. Niche beats general every time. > > 🧠 _Example:_ A developer in Nairobi, Kenya built a terminal-based Python assistant using a fast API model, marketed it on Twitter/X to the #100DaysOfCode crowd, and sells it for $9/mo. Hit 380 subscribers in 3 months — $3,420/mo. > > > 📈 **Timeline:** 1-2 weekends to build MVP → Ship on Product Hunt → Iterate based on feedback for 30 days > **📝 Hustle 4: Offer Instant Document Processing for Legal and Medical Offices** > > Law firms and clinics drown in paperwork. Summarizing case files, extracting key dates from medical records, flagging inconsistencies in contracts — these are perfect tasks for a fast, cheap model with 128K context. > > Build a simple upload-and-process web app. Charge per document or monthly subscription. > > 🧠 _Example:_ A tech-savvy paralegal in São Paulo, Brazil built an internal document summarizer for her firm using API calls, then started selling it to other small firms. She charges R$500/mo (~$90) per firm and has 30 clients — R$15,000/mo ($2,700). > > > 📈 **Timeline:** 2 weeks for web app + document parser → Pilot with one firm → Referral-based growth from month 2 > **⚡ Hustle 5: Build and Sell Multi-Agent Automation Templates** > > This is where Mercury 2’s speed really shines. Agent frameworks like CrewAI and AutoGen run loops of LLM calls — and every call’s latency adds up fast. At 10x the speed and 4x cheaper pricing, you can build multi-agent workflows that actually finish in reasonable time. > > Create templates for common workflows: lead research agents, content generation pipelines, automated code review bots. Sell them as Gumroad products or offer setup-as-a-service. > > 🧠 _Example:_ An automation consultant in Bucharest, Romania built a 5-agent lead research pipeline (scraper → qualifier → enricher → email writer → scheduler) for B2B SaaS companies. Sells the template for $149 on Gumroad and custom setups for $1,500. Made $8,200 last month between templates and consulting. > > > 📈 **Timeline:** 1 week per template → List on Gumroad/Twitter → First sales within 2 weeks of launch > **🛠️ Follow-Up Actions** > > | Step | Action | > | --- | --- | > | 1 | Sign up for Mercury 2 API early access at [chat.inceptionlabs.ai](https://chat.inceptionlabs.ai) | > | 2 | Test it against your current LLM on speed-sensitive tasks — agent loops, batch extraction, real-time chat | > | 3 | Compare actual quality on YOUR use case (benchmarks are marketing — your data is truth) | > | 4 | Start with the highest-speed-dependency hustle from above that fits your skills | > | 5 | Join the Inception Discord or follow [@StefanoErmon](https://x.com/stefanoermon) for model updates and pricing changes | * * * **⚡ Quick Hits** | Want to… | Do this | | --- | --- | | 🧪 Try Mercury 2 right now | Hit up [chat.inceptionlabs.ai](https://chat.inceptionlabs.ai) for the demo interface | | 🔌 Plug it into existing code | It’s OpenAI-compatible — swap your base URL and API key, done | | 📊 See the benchmarks yourself | Check their [blog post](https://www.inceptionlabs.ai/blog/introducing-mercury-2) for full comparison tables | | 💡 Understand diffusion LLMs deeper | Read the [original Mercury paper on arXiv](https://arxiv.org/html/2506.17298v1) | | 💬 See what devs think | Browse the [HN discussion thread](https://news.ycombinator.com/item?id=47144464) | * * * _Every LLM on earth writes one word at a time — Mercury 2 just proved they don’t have to._