Kimi K3.1 Pro is Moonshot AI’s post-trained successor to Kimi K3. The important availability distinction: hosted inference is available now, but Moonshot schedules the full weights release for November 4. An open-weight announcement is not proof that you can already download and deploy the model.
For an engineering team, this is a candidate for long-context coding and multimodal agent workflows, not a reason to replace a working model without evaluation. This guide separates confirmed access and prices from decisions you still need to test. We have not independently benchmarked K3.1 Pro.
Confirmed specifications and availability
Moonshot’s announcement describes a 3T-class model derived from K3 through further post-training, alongside a Highspeed option. Both are available through Kimi products and its API. Moonshot also reports coding and agent benchmarks; these are vendor results, not our own measurements.
The OpenRouter listing, checked October 26, provides these hosted-route details:
| Item | Current listing |
|---|---|
| Model ID | moonshotai/kimi-k3.1-pro |
| Context window | 1,048,576 tokens |
| Modalities | Text, image and video input; text output |
| Standard input / output | $2 / $10 per million tokens |
| Standard cache read | $0.20 per million tokens |
| Highspeed provider input / output | $4 / $20 per million tokens |
| EU in-region routing | Not currently available for this listing |
Those are OpenRouter route prices, not a promise that every Moonshot product or subscription has identical billing. Check the selected endpoint. A dated OpenRouter path is not evidence of another separately priced foundation model.
What changed from Kimi K3?
Moonshot identifies K3.1 Pro as a post-trained successor rather than a new label for the original model. Keep the Kimi K3 guide for that release’s context; do not silently replace K3 identifiers in an existing deployment.
Before migrating, ask whether the candidate completes your actual repository tasks more reliably, whether responses remain compatible with your tool executor, and whether total cost improves after retries and context growth. A better aggregate benchmark cannot answer these questions for an individual application.
Start with the same task set, execution budget and acceptance criteria on both models. Include cases where the agent should decline to act. A model that writes a plausible patch but ignores an authorization boundary has not passed a production evaluation.
Pricing: compare accepted tasks, not only input tokens
Calculate uncached input, cached input and output separately. Do not assume a cache hit just because two requests contain similar documents. Record actual usage and the selected provider with each run.
For a simple standard-route example, 100,000 uncached input tokens and 10,000 output tokens cost $0.30 at the listed $2/$10 rates, before any other applicable platform charges. This is arithmetic, not a measured coding session. An agent can make many model calls and invoke separately paid tools.
A router that chooses a more expensive endpoint can invalidate a budget based only on the lowest displayed price. Keep provider routing explicit. The original K3 pricing guide covers a different model; the AI API pricing comparison supplies broader purchasing context.
API access and integration boundaries
Use moonshotai/kimi-k3.1-pro when selecting the checked OpenRouter model. A direct Kimi account, Kimi Code subscription and OpenRouter account are separate access and billing arrangements. Do not transfer a subscription entitlement or provider-prefixed identifier between them without checking the destination’s documentation.
Before sending production data, verify supported parameters, output limits, retention, region and tool behavior on the actual endpoint. A context-window number does not establish any of those properties. See the OpenRouter guide and AI gateway architecture for the surrounding integration decisions.
For direct access, Kimi’s quickstart uses the base URL https://api.moonshot.ai/v1 and model kimi-k3.1-pro, without OpenRouter’s provider prefix. The Highspeed direct identifier is kimi-k3.1-pro-highspeed. For example:
curl https://api.moonshot.ai/v1/chat/completions \
-H "Authorization: Bearer $MOONSHOT_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"kimi-k3.1-pro","messages":[{"role":"user","content":"Explain the risks in this API migration plan."}]}'This is a documentation-based example, not a paid API test. Direct Kimi billing includes cache-write items, so do not reuse an OpenRouter-only estimate as a complete direct bill. Keep keys server-side and verify the applicable direct pricing before deployment.
Keep the model name in configuration rather than scattered throughout application code. Retain a previous-model route behind a controlled release switch. A rollback should change inference configuration, not require reversing unrelated application changes. Test authentication failures and quota exhaustion as well as successful responses.
What a million-token context does not guarantee
A larger window lets you submit more material; it does not prove every relevant detail will be retrieved correctly. Repository snapshots can contain stale generated files, duplicated dependencies and conflicting instructions. Sending everything can increase cost while obscuring the task.
Build a context policy around relevance: task, trusted constraints, selected files and necessary evidence. Keep retrieved documents distinct from instructions. Test whether the model cites the correct implementation instead of an obsolete note. Compare targeted retrieval against a larger-context approach on identical questions.
Measure accepted answers, missing evidence, latency and cost rather than rewarding token consumption. The AI Application Architecture foundation explains where these controls belong in a system.
Coding and agent uses worth evaluating
Repository work with visible acceptance criteria
Start with bounded changes: repair a failing integration test, implement a small API change, or update a dependency with a defined compatibility check. Give the agent a separate working tree, scoped credentials and a stop condition. Require an inspectable diff and independent checks before accepting the result.
Do not let the agent redefine success by weakening tests. Preserve baseline fixtures outside its writable area where practical. The AI-generated code testing workflow is a better release criterion than a confident final message.
Visual evidence alongside code
Image and video inputs make screenshot-led investigation a relevant experiment. Provide interface evidence alongside the responsible components and ask for a narrow repair. Evaluate the rendered result, accessibility and existing interactions, not just compilation.
Video input is not video generation. It also does not establish compatibility with every agent client’s attachment format. Verify how your client packages media and whether the chosen endpoint accepts it. Keep sensitive screenshots subject to the same data policy as source code.
Longer workflows with tools
For investigation, editing, testing and reporting, track failures at every stage. A correct final answer can conceal a failed command, unnecessary retries or an unauthorized action. Separate task completion from permission compliance.
Use AI Testing & Evaluation to build stable cases, including deliberate failure scenarios. Add timeouts, spend ceilings and human approval for consequential actions. Better reasoning does not justify broader authority. Preserve tool traces so reviewers can reconstruct what actually happened.
Can you self-host K3.1 Pro?
Not on the strength of the announcement alone. Moonshot schedules the full weights release for November 4. Wait for actual artifacts, license, deployment instructions and supported runtimes before recommending a self-hosted configuration.
Do not copy K3’s hardware requirements or license onto its successor. The original K3 deployment guide describes another release. Even when weights arrive, their availability will not automatically make workstation deployment practical. Verify memory requirements and serving support before buying infrastructure.
My take
K3.1 Pro merits a controlled trial when repository context, visual evidence or multi-step coding matter. It does not merit an automatic production switch based on vendor rankings. A repeatable task set that includes human review effort is more useful than a launch leaderboard.
Keep the incumbent until the candidate clears quality and safety gates. If EU in-region routing or immediate self-hosting is mandatory, resolve availability first. Capability cannot compensate for a deployment route that fails your requirements.
FAQ
Is K3.1 Pro the same as Kimi K3?
No. Moonshot describes a post-trained successor. Treat it as a separate version and evaluate before changing a K3 route.
What does K3.1 Pro cost?
The checked OpenRouter standard route lists $2 input, $10 output and $0.20 cached input per million tokens. Highspeed costs more. These are not universal subscription rates.
Are the weights available now?
Moonshot’s announced release date is November 4. Do not assume a license or local deployment instructions before the artifacts are published.
Does it generate video?
The hosted listing specifies text, image and video input with text output. Video understanding is not video generation.
Should I replace my current coding model?
Only after evaluating accepted changes, tool behavior, authorization, latency and total task cost. This guide does not claim independent benchmark reproduction or universal superiority.