Guides Inception published oct 20, 2026

Mercury 3: How to Use Inception’s Fast AI Model

beginner

The Rundown

Inception announced Mercury 3 on October 19, 2026. It is a diffusion text model built for quick replies and repeated tasks. Inception reports 1,068 output tokens per second in its testing and compares its quality with GPT-6 Luna and Claude Haiku 5.5. Your results will depend on the task, settings, and service.

Start with a job where shorter waits would help: turning sales-call notes into follow-up drafts. You will give Mercury three leads, check its recommendations, and change one requirement that makes an earlier recommendation invalid. We ran this exercise in Mercury Chat at Medium and Low reasoning effort on October 20. The public demo let us complete it without signing in or making a purchase.

University Pro members get the complete practice prompt, 12 additional cases, an answer key, sample drafts, and a comparison log. All names, companies, product terms, and call notes in this exercise are fictional.

What is Mercury 3, and how does diffusion work?

Most language models produce text in sequence. Mercury uses diffusion to generate and refine several tokens in parallel. A token is a small unit of text. Inception’s streaming system can send refined blocks as they become ready. The chat’s optional Diffusion Effect shows text changing during generation.

That approach aims to reduce time spent generating an answer. Reading a prompt, calling an outside service, and checking the result still take time.

Mercury 3 accepts and returns text. Image understanding and image generation are outside this model’s capabilities.

Mercury 3 at a glance

DetailCurrent specification
Context windowUp to 1 million tokens.
Maximum completion131,072 tokens, including reasoning within the completion budget.
Reasoning settingsInstant, Low, Medium, and High.
Workflow supportTool calling and structured output.
Direct API model namemercury-3.

Inception’s model specifications describe the model’s limits. A chat app can expose fewer controls or set a smaller output budget. The API completion budget includes both reasoning and the visible answer.

The context window is the text the model can consider in one request. A large window lets you supply more material. Check whether the model actually uses the details your task needs before handing it a whole archive.

Mercury 3 vs Haiku and GPT-6 Luna

Inception’s launch benchmark table compares all three at Medium reasoning effort. It shows Mercury ahead on instruction-following and some reasoning tests, while Haiku leads the listed DeepSWE coding and MRCR long-context recall tests. The same named effort level does not establish equal computation across models.

Our recommendation: test Mercury against the model already doing your routine work. Keep your current choice for difficult tasks until the new model passes the same checks. Our Claude Haiku workflows guide uses a similar approach to separating routine work from harder cases.

SituationWhat we would do
You repeatedly turn short notes into drafts or structured records.Try Mercury 3 on a small batch with known answers.
Your current model is accurate but pauses interrupt your work.Compare time to a checked result, including corrections.
The task needs complex judgment or missing facts.Keep human review and ask for the missing information.
The job uses images, audio, or a browser.Check the surrounding app and tools. Mercury 3 itself is a text model.

Why the speed headline may differ from your result

OpenRouter’s Mercury 3 page shows live provider throughput and latency metrics. Those figures change with the traffic and measurement window. Inception’s launch result comes from a separate test, so the two measurements should be read in their own contexts.

Judge three things separately: how soon an answer begins, when it finishes, and how long you spend checking it. We reviewed the outputs below without conducting a controlled speed or cost benchmark.

Mercury 3 pricing

Inception’s pricing page lists these direct API rates in US dollars per million tokens:

Token typeLaunch priceRegular listed price
Input$0.10$0.20
Cached input$0.01$0.02
Output$0.35$0.70

The pages show a 50% launch discount without an end date. Check the price in the service you use before buying credits. API rates cover token usage; chat services can have separate access terms.

For a hypothetical 1,000 jobs, each using 10,000 uncached input tokens and 2,000 total billed output tokens, the token cost is $1.70 at launch rates or $3.40 at regular rates. This calculation assumes fixed usage for every job.

Inception’s token-accounting guidance includes reasoning tokens in the billed completion total. Do not add them twice. Retries, tools, platform charges, and human review can add cost.

Where Mercury 3 could fit next

Repeated taskA small first useKeep under review
Sales follow-upTurn approved facts and call notes into drafts.Prices, promises, and sending.
Customer supportSuggest a reply from a supplied policy.Refunds, access changes, and unclear cases.
ResearchExtract a stated fact and quote from each source.Conflicting sources and the final recommendation.
CodingExplain a test failure or find relevant code in supplied text.Code changes and passing tests.

These are suggestions for further trials. The hands-on test in this guide covers the fictional sales-follow-up exercise.

For an app, Mercury supports structured output and tool calling. A structured result can require fields such as lead ID and plan. A developer must still verify the values and control any action they trigger.

Keep private customer notes out of a trial until your organization approves the provider and data terms. Inception’s API data-handling guidance discusses custom retention arrangements separately.

Learn to choose models from the work they produce

Nate Grahek’s Intro to AI Evals teaches how to choose test inputs, define expected answers, and compare quality, speed, and cost. Its examples use a spreadsheet and Claude Code. University Pro members can apply that method to this practice task and download the prepared prompts, additional cases, answer key, sample drafts, and comparison log.

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