Kimi K3.1 Pro:
advancing financial research
and financial modeling

September and October 2026 brought a dense run of model releases. OpenAI introduced GPT-6 Astra and GPT-6 Sol, while Anthropic released Claude Opus 5.5 and Claude Sonnet 5.5 in September. On October 24, Kimi officially released Kimi K3.1 Pro, the first model in the K3.1 series. It is Kimi's most capable 3-trillion-parameter-class model to date, with a one-million-token context window and improvements in coding, 3D creation, financial research and long-horizon agent capabilities. Kimi also introduced K3.1 Pro Highspeed, offering approximately 2–3 times the output speed while retaining the same model intelligence. With more choice, practical questions matter: what can the new model do, where can you try it, and what will your task cost?

QVeris Playground now supports Kimi K3.1 Pro and Kimi K3.1 Pro Highspeed. Choose either version in the same interface to explore code generation, creative ideas, document analysis and research. When a task needs external material, continue with the data services and tools currently available.

This guide covers version selection, pricing and practical starting points. For model capabilities and API details, follow the links to the official Kimi documentation.

QVeris connects professional data discovery, inspection and invocation. Its Playground also provides model access in your browser. Start by asking Kimi to write code, organize material or break down a research question, then find suitable services when the task needs external facts.

The QVeris Data & Tools directory spans companies and finance, social media, life sciences, papers and patents, e-commerce, trade and everyday services. Start with the question you need answered, then review the service's coverage and access conditions.

For multi-step research, open QVerisLab (beta) and adapt the prefilled quantum computing industry task to explore retrieval, earnings analysis and source verification.

Kimi supplies reasoning and generation; QVeris provides model access and connections to professional data tools. After exploring in the browser, use the QVeris model gateway and tool interfaces to bring a useful workflow into your application.

Kimi highlights coding, 3D creation, financial research and longer agent workflows in this release. A one-million-token context window and visual understanding add more ways to work with material. Explore these six starting points and consult the official model guide for specifications.

AI coding: build a page, revise code, explain an error

Bring a page brief, code snippet or error message and ask for an implementation, its reasoning and checks to run. Start with a responsive webpage and refine the layout and interactions. For existing code, try troubleshooting, input-validation review or a refactoring plan. See the Kimi Code documentation for its coding product's model setup.

3D creation: turn a brief into scene code

Kimi demonstrates spatial reasoning, parametric modeling and scripts for specialist tools. On QVeris, start with a 3D product-display brief: ask Kimi to define proportions and interactions, then produce a Three.js example or Blender script to validate in the appropriate environment. Find official examples and the separate 3D Make product in Kimi's technical introduction.

Long-document analysis: find points, differences and actions

For product documentation, research material or meeting notes, ask for a question-led summary, conflicting viewpoints and references to sections. Start with relevant text in the prompt; larger collections can be organized through supported interfaces. Check file formats, actual input limits and the integration you are using.

Visual understanding: interpret charts, interfaces and references

The model accepts visual input for tasks such as chart interpretation or interface analysis. For an integration, follow the official visual-input examples and confirm formats supported by the gateway and client. For a text-only discussion, describe the chart or design goal and ask for an analysis plan.

Industry and financial research: keep conclusions connected to sources

Compare product features, organize industry material or check company filings. Kimi can help structure the question and interpret differences. QVeris provides a route to discover and call professional data services when the task needs fresh material, keeping sources, dates and missing information visible.

Agents and tool calling: break work into checkable steps

Ask Kimi for a plan, then use available tools to retrieve, check and summarize material. Developers can integrate the same pattern into an application. What can execute depends on the model, enabled tools and runtime together. See the official tool-calling examples.

A model announcement explains its abilities, but access, versions, source material and cost still take work to figure out. QVeris connects those steps so you can keep moving from a first question to a useful workflow.

You can begin with a small goal: a coffee-shop webpage, a product comparison or three figures from a filing. Develop the larger workflow around the results you find useful.

The Kimi documentation describes a 1M context window, tool calling and structured output. It positions Highspeed as the same intelligence with approximately 2–3 times the output speed of the standard version.

The speed comparison is the vendor's description of generation, not a QVeris end-to-end benchmark. A complete task may also spend time retrieving data, waiting for tools, running code and checking results. Evaluate the complete workflow when making the choice.

Kimi K3.1 Pro on QVeris costs $1.9351 per million input tokens and $0.1935 per million cached input tokens. The comparison uses USD per million tokens, checked on October 24, 2026. QVeris values are customer-facing rates; Kimi values use its official international API prices; OpenRouter values refer to its Moonshot AI and Moonshot AI Highspeed endpoints. Input means uncached input.

Against the official international Kimi API and the listed OpenRouter endpoints, QVeris input rates are approximately 3.25% lower, cache reads are 3.25% lower, and output rates are approximately 1.22% higher. Reading long material and generating long answers have different cost profiles. Compare the mix your task actually uses. View QVeris pricing and credits for available plans, credit top-ups and usage-based billing rules.

The Kimi China platform lists Pro at CNY 13 input, CNY 68 output and CNY 1.30 cached input per million tokens; Highspeed is CNY 26, CNY 136 and CNY 1.30 respectively. These are separate regional prices, retained in their original currency without an exchange-rate-based ranking.

What does a long-input, short-summary example cost?

For 100,000 uncached input tokens and 5,000 billed output tokens, Pro costs approximately $0.2441 on QVeris and $0.2500 on both the official international Kimi API and OpenRouter. Highspeed costs approximately $0.4882 on QVeris and $0.5000 on each of the other two.

At 10,000 input and 50,000 output tokens, Pro instead costs approximately $0.5255 on QVeris and $0.5200 on OpenRouter. Changing the output share changes the comparison. These are calculations from rates, not measured or fixed costs for a particular task.

What else belongs in the task budget?

• Model usage: use the billed input, cache reads and output; a multi-turn task may add further usage.
• Data and tools: QVeris Discover and Inspect are free; calls follow each capability's billing rule. Check pricing and credits and the selected tool's cost information before execution.
• Platform and cache charges: OpenRouter Standard currently charges a 5.5% fee on credit purchases; other plans differ. Check cache writes separately from reads; see Kimi's billing documentation.

The examples exclude tools, cache writes, credit-purchase fees and taxes. The QVeris model gateway settles model usage in Credits. The rules at execution and final billing records determine the charge.

How should you compare the platforms beyond price?

OpenRouter offers multi-model Chat, API access and tool calling. QVeris provides model access and a gateway alongside professional-data discovery and invocation. Whether you are moving from code to product research or from reading documents to checking filings, compare the work required to obtain material, use tools and connect your application as well as the token rates.

When a question needs material beyond model knowledge, the QVeris data and tools directory helps you find additional inputs. Adapt the topic, scope and dates in these task ideas:

• Industry and academic research: organize material on a technical topic, compare views and retain papers, institutions or original publication links.
• Product and competitor research: compare features, prices or public feedback, with source dates and a distinction between facts and inference.
• Content and brand analysis: identify themes in retrieved public discussion, then ask Kimi for content ideas or a communication outline.
• Everyday queries: find a relevant service for a weather, exchange-rate or logistics question, then interpret its response.

Explore Data & Tools and inspect fields, coverage and billing before choosing a call. A focused question makes the returned evidence easier to check.

Kimi’s published internal finance benchmarks cover financial modeling, research and business operations. It reports a 20% improvement over K3 in financial research and 14% in financial modeling. The research evaluation focuses on verifiable findings under evidence and cutoff constraints; the modeling evaluation examines formula dependencies, financial logic and workbook quality. These are results on specific benchmarks; task performance also depends on the material and tools available.

In Kimi’s official technical introduction, the internal Kimi Hallu Bench Hard factual-accuracy stress test reports a claim-level error rate falling from K3’s 23.4% to 8.3% for K3.1 Pro. The result shows an improvement on that evaluation and offers one lens for research tasks that depend on checking claims and tracing sources.

The test deliberately focuses on difficult, error-prone situations. Its figures do not represent overall error rates in everyday use or an independent QVeris benchmark. Actual financial tasks still require verification of source documents, reporting periods and calculation methods.

Financial research is a focus of this Kimi release and a useful application for QVeris professional data tools. Start by verifying revenue, net income and operating cash flow in NVIDIA's latest disclosed quarter. Keep periods, currencies, units, disclosure dates and official sources, then ask Kimi to explain the changes. Preserve year-to-date cash flow when that is the reported basis.

Start professional financial research in QVerisLab

Build an in-depth view of the quantum computing industry in QVerisLab: examine technology, commercialization and competition, then compare market consensus, management guidance and disclosed results to identify evidence-backed expectation gaps. Extend the analysis to post-earnings business developments, catalysts and risks, with an earnings review, forward scenarios and a source list. Document calculation methods for key figures, distinguish facts from judgments and trace citations to primary sources.

Move from figures to the original filing

For management's explanation of margins or changes in risk factors, use DataSinking filing tools to find a report, locate sections and retrieve text. Kimi can then organize the points and evidence. See the filing search and section-reading guide.

Move from one company to a comparison

Align periods, currencies and metric definitions before comparing growth, margins and cash flow. To choose a source, read the financial data API guide for Kimi. Clear sources and definitions make the analysis easier to investigate further.

1. Open QVeris Playground. The button below prefills a quantum computing industry research task. Adjust the cutoff date, markets and companies you want to cover.
2. Select a Kimi model. Choose kimi-k3.1-pro or kimi-k3.1-pro-highspeed and confirm the version.
3. Define the scope and review cost. Set the industry and company coverage, expectation-gap questions and earnings review requirements. Follow sign-in prompts and review preflight billing.
4. Check the result and iterate. Verify figures, reporting periods and primary sources; distinguish market consensus, management guidance and analysis. Use the same question and material when comparing versions.

For API access, use the QVeris model gateway documentation, a QVeris API Key and the available model IDs through an OpenAI-compatible interface. Consult the official Kimi API guide for native parameters, visual input and tool-calling examples, then confirm the gateway supports the parameters you need.

Choose Kimi K3.1 Pro Highspeed when you want to ask successive questions about earnings or refine an analysis. It retains the same model intelligence with faster output generation, making it useful for earnings briefs, metric checks and follow-up analysis. Start with NVIDIA’s latest disclosed quarter, then explore margins, cash flow and guidance in further questions.