Kimi K3.1 Pro and Kimi K3.1 Pro Highspeed are available now in AI Gateway

Moonshot AI launched Kimi K3.1 Pro and Kimi K3.1 Pro Highspeed on Vercel AI Gateway, introducing production-grade routing for the company's newest flagship foundation models as detailed in Vercel Blog's report. The release establishes K3.1 Pro as the premier offering in Moonshot AI's new K3.1 lineup, while pairing it with the Highspeed variant for latency-sensitive interactive tasks. Both releases preserve the established one-million-token context window alongside native multimodal image input capabilities for complex software environments.

This technical report covers the architectural upgrades, agentic coding capabilities, financial reasoning benchmarks, and integration pathways across developer environments. It is written for software engineers, systems architects, and engineering leaders seeking to integrate high-context foundation models into autonomous coding agents or enterprise API gateways. Readers will find exact environment setup commands, protocol options across the AI SDK and Chat Completions interfaces, operational guardrails, and key dates surrounding public weights availability.

What shipped in Kimi K3.1 Pro and Kimi K3.1 Pro Highspeed

Moonshot AI released two distinct foundation models through Vercel AI Gateway: Kimi K3.1 Pro and Kimi K3.1 Pro Highspeed. Serving as the foundational release of Moonshot AI's K3.1 series, K3.1 Pro delivers upgraded reasoning capacity across complex developer tasks. Meanwhile, K3.1 Pro Highspeed functions as an accelerated variant engineered specifically for interactive deployments where request round-trip time directly impacts developer velocity. Both foundation models retain Moonshot AI's signature one-million-token context window, allowing engineering teams to ingest massive code repositories and dense source documents without truncation.

The new models process native image inputs alongside full-context text comprehension across all deployment paths. Vercel AI Gateway exposes both systems through multiple integration protocols, including the AI SDK, Chat Completions, Responses, and Anthropic Messages interfaces. In addition to direct API access, developers can invoke both endpoints inside major autonomous coding environments like Claude Code, Codex, and Cursor. The availability guarantees that developers can alternate between exhaustive reasoning depth and accelerated interactive execution simply by modifying model identifier strings inside their existing application code.

What improved in Kimi K3.1 Pro and Kimi K3.1 Pro Highspeed

Building upon the previous Kimi K3 architecture, K3.1 Pro introduces performance gains across software engineering, three-dimensional scene modeling, financial spreadsheet construction, and autonomous agent loops. In programming tasks, the model expands from targeted bug fixing to full-project generation, sustaining multi-step agent runs across extensive execution sequences. Front-end code generation produces web pages and interactive components with noticeably stronger typography and fluid animations compared to earlier releases. For financial workflows, the system builds linked financial models featuring dynamic scenario planning and research grounded directly in source documents.

A central advancement in the K3.1 architecture is a marked decrease in factual inaccuracies across demanding prompts. Earlier Kimi models frequently suffered from factual drift when answering complex multi-stage queries, whereas K3.1 Pro demonstrates tighter alignment with grounded facts. The model reliably synthesizes information from extensive technical references, minimizing hallucinations during prolonged automated sessions. These precision improvements provide software teams with predictable behavior when running automated agents across large code repositories, auditing legal or financial documents, and orchestrating multi-turn generative scripts.

What you gain from Kimi K3.1 Pro and Kimi K3.1 Pro Highspeed

Engineering teams adopting K3.1 Pro acquire an agentic engine optimized for multi-step reasoning without context starvation. By combining a one-million-token window with lower factual error rates, the system executes complex workflows such as generating Blender scripts for parametric 3D models or synthesizing dense financial statements from raw source materials. Developers running long agent loops can delegate end-to-end repository creation, confident that intermediate tool calls and state transitions will remain coherent across extended execution traces without manual intervention or context resets.

Integrating through Vercel AI Gateway yields operational reliability advantages beyond raw inference execution. The gateway layer manages automatic retries, active failover configurations, and latency optimizations designed to provide higher uptime than standalone model provider endpoints. Organizations gain access to Zero Data Retention compliance, customized usage reporting, programmable routing rules, and per-key spend limits. Vercel applies zero platform fees to inference and passes provider pricing through directly without markup, even for teams utilizing Bring Your Own Key configurations across production systems.

How to get Kimi K3.1 Pro and Kimi K3.1 Pro Highspeed

Calling either model requires referencing standard identifier strings inside existing application logic or autonomous programming agents. Developers invoke the standard foundation tier using moonshotai/kimi-k3.1-pro or select moonshotai/kimi-k3.1-pro-highspeed whenever application requirements favor rapid interactive responses. Integration is supported across the Vercel AI SDK as well as Chat Completions, Responses, and Anthropic Messages protocols. Teams wishing to benchmark model behavior before committing production traffic can test prompt responses, evaluate token throughput, and inspect generation quality directly within Vercel's interactive model playground interface.

# Update and configure gateway credentials for coding agents
npx vercel ai-gateway setup

```javascript
import { streamText } from 'ai';

```bash
# Agent model switch, pinned launch, and default environment configuration
# In-app model switch command inside coding agents:
/model moonshotai/kimi-k3.1-pro

Set default model identifier in shell environment:

To configure local autonomous agents like Claude Code, Codex, or Cursor, developers install the latest Vercel CLI tooling and execute the initial setup routine. The automated command authenticates the local workspace against AI Gateway and registers appropriate routing configurations for installed tools. Once configured, developers designate moonshotai/kimi-k3.1-pro or moonshotai/kimi-k3.1-pro-highspeed as their active model target. This connection enables agents to execute codebase refactoring, create new project directories, and run multi-step terminal actions directly against Moonshot AI's upgraded model infrastructure without requiring manual proxy configurations.

What to watch after Kimi K3.1 Pro and Kimi K3.1 Pro Highspeed

The primary upcoming milestone on Moonshot AI's schedule is the official release of the K3.1 Pro model weights by November 4. Following the distribution of open weights, independent cloud providers and self-hosted enterprise infrastructure are expected to add hosting support for the new architecture. This release will enable engineering organizations with strict data residency requirements to deploy private instances of K3.1 Pro within their own virtual private clouds while retaining the long-context capabilities demonstrated on managed gateway endpoints.

Engineering teams will also monitor how the Highspeed variant balances latency gains against output fidelity in high-concurrency production deployments. Because Moonshot AI designed Highspeed for interactive workflows, real-world benchmarks will demonstrate whether its reasoning quality matches standard K3.1 Pro across intensive programming benchmarks and complex financial calculations. As additional hosting platforms incorporate Moonshot AI's weights after November 4, developers using AI Gateway will benefit from built-in performance optimization and automatic failover routing across newly deployed upstream infrastructure providers worldwide.