# Kimi K3.1 Pro: Specs, Pricing, Release Date (2026)
By Carlos Mendoza · 2026-10-23 · Source: https://www.activepieces.com/blog/kimi-k3-1-pro-for-automation-teams-in-2026
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## Learn Kimi K3.1 Pro's maker and release date
### Kimi K3.1 Pro summary and release date
Kimi K3.1 Pro is a model from the Kimi team, and the release date is whatever the Kimi Code documentation's models page states.
This article doesn't restate it from memory or from third-party coverage, whether that's a roundup, a social post, or a note an [Activepieces](https://www.activepieces.com) workflow happened to pull into someone's feed.
That matters on a Monday morning. When someone on your team asks whether the new model is ready to go into a workflow, the first answer should come from the maker's page, not from a forum thread or a screenshot.
If you build your flows in a workflow builder or in plain scripts, the model name you type into a step has to match what the maker publishes, character for character.
### Kimi K3.1 Pro sources and check date
As read on 2026-10-23, every fact here comes from the Kimi Code docs, specifically the models page. The card below shows what the maker states and when I looked.
Treat it as a snapshot: if your rollout starts weeks from now, the date column tells you how old the reading is, and you should re-check before you commit.
| Item | Stated value | Date checked |
|---|---|---|
| Maker | The Kimi team | 2026-10-23 |
| Model name | Kimi K3.1 Pro | 2026-10-23 |
| Release date | As given on the models page; not restated here | 2026-10-23 |
| Source page | Kimi Code docs, models page | 2026-10-23 |
_Prices and plan limits checked against [kimi.com](https://kimi.com/code/docs/en) and [kimi.com](https://www.kimi.com/code/docs/en/kimi-code/models.html) on October 23, 2026._
Nobody on your team copies a secondhand date into a runbook, because the release date row points to the source instead of repeating a date.
The next step is to open that page, read the entry for this model, and use it as the reference for everything you wire up.
## Why Kimi K3.1 Pro warrants automation review
It matters because it's one more model you can put behind an AI step, and every addition is a reason to re-check the steps you already run, not a reason to swap anything.
### A new model option for AI steps
Picture a six-person support team with four AI steps in one workflow, which means those four steps execute the same way regardless of which team member handles them. Each step has a model behind it, and each model was picked at a different time.
A new option means the pick is open again.
Start with size, the figure [Kimi](https://kimi.com/code/docs/en) publishes. [Kimi's own comparison](https://www.kimi.ai/blog/kimi-k3) lists DeepSeek-V3 at 671 billion parameters, Kimi K2 at 1000 billion, Kimi K3 at 2800 billion and Kimi K3.1 Pro at 3000 billion.
That tells you how the model was built, not how it will handle your tickets. **Parameter count is not a result**, so treat it as context only.
### What to check before switching anything
Look at four steps in a typical workflow, because that is where the model choice sits:
- Classifying an incoming ticket: re-check that the output still lands on your exact label set, and run last month's tickets through it.
- Drafting a reply: re-check tone, and whether a human reviewer still approves drafts with the same edit rate.
- Extracting fields from an invoice: re-check field names, date and currency formats, and what happens when a field is missing.
- Summarising a thread: re-check that names, dates and commitments survive the summary.
Those four checks share one habit. Run the old and new model side by side on a few dozen real cases from your own queue, and let the people who read the output every day judge it.
Change one step at a time, so a bad result has one obvious cause.
## What's new in Kimi K3.1 Pro
Latest flagship, 3T-class parameters, a 1M context window, **thinking mode only**: that is how Kimi's pages describe K3.1 Pro, offered through the Kimi Code programming service. That gives you a short list of things you can confirm before changing a step.
### Kimi K3.1 Pro details and access
To see what each [Kimi docs page](https://www.kimi.com/code/docs/en/kimi-code/models.html) states, read the table below. Copy only the rows with a value into your change log, and treat the rest as open questions.
| Item | What the page states | Link | Date checked |
|---|---|---|---|
| Capabilities | Latest flagship, first of the K3.1 series, thinking mode only | [models page](https://www.kimi.com/code/docs/en/kimi-code/models.html) | 2026-10-23 |
| Model identifier | k3.1-pro (high-speed variant: k3.1-pro-highspeed) | [models page](https://www.kimi.com/code/docs/en/kimi-code/models.html) | 2026-10-23 |
| Access routes | Kimi Code programming service | [Kimi Code docs](https://kimi.com/code/docs/en) | 2026-10-23 |
| Context length | Up to 1M | [Kimi Code docs](https://kimi.com/code/docs/en) | 2026-10-23 |
| Price per input/output token | not stated on the pages | n/a | 2026-10-23 |
You can't switch reasoning off for cheap, simple steps, because thinking mode is the only mode, so expect every call to behave like a reasoning call.
The parameter count is a size reference only. Set it beside DeepSeek-V3 if you like, but it says nothing about how either model performs in your workflow.
There's nothing to put in a cost estimate yet: the pages checked on 2026-10-23 state no per-token price. Budget lines built on a guessed rate would be your own number, not Kimi's.
They also give the model identifier string, **k3.1-pro**, so you can hard-code it into a workflow step. They give no price and no performance figures, and I haven't filled any in.
## Connecting Kimi K3.1 Pro to a workflow today
Call it over an API, because **self-hosting is impractical at this size**. The only weight sizes available are for its predecessor, Kimi K3, from [Particula](https://particula.tech/blog/self-host-kimi-k3-gpu-sizing-mxfp4-vllm): 5697 GB at FP16/BF16 and 2849 GB at FP8, so self-hosting requires specialized infrastructure most organizations lack.
A five-person ops team isn't running this on a spare box.
No native connector for K3.1 Pro is documented in the pages above, so you assemble it from three blocks.
Option 1 is an HTTP request step. Send the call yourself with a request step. It takes an afternoon to set up, and you own the payload, headers and retry logic.
Option 2 is an AI step with an OpenAI-compatible provider. Point the builder's AI step at a custom base URL, if the maker documents one. Your team sees a familiar step, but you depend on the provider's compatibility being faithful.
Option 3 is an MCP server. An MCP server exposes your tools to the model. It suits agent-style steps, but someone has to keep it running.
Pick by step type, not by preference, since the table shows effort rising as control rises.
| | Setup effort | What you maintain | Main trade-off | Best-fit step type |
|---|---|---|---|---|
| HTTP request step | Low | Payloads, auth, retries | Most control, most hand-built | One-off calls, testing |
| AI step, OpenAI-compatible provider | Low to medium | Provider settings | Familiar, but compatibility gaps | Routine text steps |
| MCP server | Highest | Server and tool definitions | Powerful, but another system | Multi-tool agent steps |
Start with the HTTP step to prove the call works, then move repeat steps to the AI step.
The model is your decision, not a catalogue entry a platform sells you. A platform that resells its own model has chosen for you and priced it before you open the product.
The model is your decision, not a catalogue entry a platform sells you.
Here the HTTP step, a custom-provider AI step or an MCP server point at Kimi on your own provider key, and **the token bill lands on your Kimi account**.
Companies such as MoneyGram, Moneypenny, Alan and FundingSocieties run their automation on platforms of this kind, and a vendor's pricing page should list which tiers accept your own keys.
## Kimi K3.1 Pro's available pricing and speed
Only list prices can be compared so far, and they favor Kimi; we haven't run Kimi K3.1 Pro, so we have no measured speed or cost per task.
### What Kimi K3.1 Pro's published pricing shows
LLM Counter's listing for GPT-5.6 Sol, an older OpenAI model, showed $20, so moving a step off it would have saved $5 on every million tokens generated.
LLM Counter listed Claude Fable 5, an earlier Anthropic model, at $50, more than three times Kimi's rate, which hurts most when a step produces long drafts all day, so the daily bill rises fast.
The Kimi figure is for K3, not K3.1 Pro, and covers output only. Input prices, retries and wordy answers all change the real bill.
In an afternoon, a team of two can finish the test below. These steps turn list prices into a number your finance lead can use:
1. Pick one real workflow step, such as ticket triage or invoice field extraction, so the test reflects work you actually do.
2. Fix 50 sample inputs and do not change them, so every model faces identical work.
3. Run each model with the same prompt, so differences come from the model, not your wording.
4. Log tokens, latency and pass/fail for every run, with a named reviewer deciding pass or fail.
5. Compute cost per task from the published prices: output tokens used, times the price per million, divided by 1,000,000.
Cost per task and pass rate, side by side: that is what you get. A cheaper token price only matters if the model passes as often as the one it replaces, and only this test tells you that.
## Monitoring Kimi K3.1 Pro after its release
Watch the maker's own pages and your own test log, because a pass rate you measured yourself is the only evidence that settles whether the switch pays off.
If you need the exact name your workflow should call, treat the Kimi Code docs model list as the source of truth, since access runs through it.
If your team hard-codes a model name in a dozen flows, a rename means a morning of edits and a round of retesting. Keep the name in one shared setting so a change touches one place.
Pricing gets published on the models page. Save a dated copy of it today, because without a baseline you can't tell next month whether a price moved or whether you misremembered it.
Compare that page against your current model only after your own test has shown the pass rates are comparable.
### What Kimi K3.1 Pro's docs leave out
As read on this date, neither page gives you your pass rate, your latency under load, or how the model behaves on your messy inputs.
Those gaps belong to your test log, and the people running the flows need to own it. Your checks should be quick enough that the team will keep doing them.
These five sources cover everything worth rechecking, and each one shows a change in a different way:
- The Kimi Code docs model list. A change looks like a new, renamed or removed model entry.
- The models page pricing. A change looks like a different published rate against your saved copy.
- Your workflow platform's public repository. A change looks like a pull request that mentions Kimi.
- Your workflow platform's integration catalog. A change looks like a Kimi entry appearing in the catalog.
- Your own test results log. A change looks like a pass rate drifting from your last recorded run.
Review these on a fixed day each week, and update the log whenever any of them moves.
## What Activepieces does about this
You can reach Kimi K3.1 Pro without a dedicated integration, and the article's own checks show why that needn't block you. The Kimi Code pages state the model identifier (k3.1-pro) but no per-token price, so there is little a vendor could responsibly pre-bake into a catalogue entry.
What you can do today is build the call yourself with the pieces Activepieces already ships: an HTTP request step, an AI step pointed at a custom provider, or an MCP server.
Each of those lets you put the model name in one place and change it when Kimi's models page changes.
That fits the monitoring habit described above. Activepieces is open source under the MIT licence for its community edition, so the repository and the integration catalog are public.
When you check them weekly for a pull request that mentions Kimi, or a Kimi entry in the catalog, you're reading the same code your flows run on. You can also self-host the platform, which is a separate matter from self-hosting the model.
The model stays behind Kimi's API, and your workflow engine stays where you choose.
The token bill lands on your Kimi account because you bring your own provider key.
That keeps the cost-per-task calculation from the 50-input test clean: your tokens times Kimi's published rate, with the platform charge as a separate, known line. The pricing page lists which tiers accept your own keys, so check it before you budget.
The practical route is the one the article already describes. Prove the call with an HTTP step, run your 50 fixed inputs through the old and new model, log pass rate and cost per task, and only then move repeat steps to an AI step.
MoneyGram, Moneypenny, Alan and FundingSocieties run Activepieces, which tells you the platform is in use at companies with real workflows. It tells you nothing about how Kimi K3.1 Pro will perform in yours, and that is still for your own test log to settle.
## Related reading
- [Kimi API Pricing in 2026 and Cost per 1,000 Tasks](https://www.activepieces.com/blog/kimi-api-pricing-in-2026-and-cost-per-1-000-tasks)
- [Best Data Automation Tools of 2026 for Analytics Teams](https://www.activepieces.com/blog/best-data-automation-tools-of-2026-for-analytics-teams)
- [Reverse Logistics Automation Tools for Operations Teams](https://www.activepieces.com/blog/reverse-logistics-automation-tools-for-operations-teams)
## References
- [Particula](https://particula.tech/blog/self-host-kimi-k3-gpu-sizing-mxfp4-vllm)
- [LLM Counter](https://llmcounter.com/compare/claude-fable-5-vs-gpt-5-6-sol/)
- [Kimi](https://www.kimi.ai/blog/kimi-k3)