# Moonshot’s Kimi K3.1 Pro Beats GPT-6 Astra on Benchmarks at Half the Price of Claude Opus 5.5

Moonshot AI has launched **Kimi K3.1 Pro** on the Kimi API, setting a new milestone for soon-to-be open-weight models by outscoring OpenAI's GPT-6 Astra, Anthropic's Claude Fable 5.1, and Google's Gemini 4 Argon on independent Artificial Analysis evaluations—while matching GPT-6.1 Sol's API pricing at **half the token cost of Claude Opus 5.5**. Even better for teams that self-host frontier models, Moonshot has committed to releasing the full model weights on **November 4, 2026**.

For software engineers and product teams balancing API bills against reasoning quality, Kimi K3.1 Pro lands directly in the sweet spot of the late-2026 frontier market. On the standard API tier, Moonshot prices Kimi K3.1 Pro at **$2.00 per million input tokens and $10.00 per million output tokens**, identical to OpenAI's GPT-6.1 Sol and 50% cheaper than Anthropic's flagship Claude Opus 5.5.

For latency-sensitive applications—such as interactive coding assistants, live voice agents, or real-time customer support routers—Moonshot also introduced a **Kimi K3.1 Pro Highspeed** endpoint that generates tokens **2x to 3x faster** at twice the standard price ($4.00/1M input and $20.00/1M output), with a major cost-saving twist: **input token cache hits remain priced at the standard $2.00-tier rate** across both endpoints.

On the independent Artificial Analysis composite intelligence index, Kimi K3.1 Pro scored **55**, placing it second overall behind only Claude Opus 5.5 (**58**) and two points ahead of the three-way tie at **53** shared by Claude Fable 5.1, GPT-6 Astra, and Gemini 4 Argon. That makes Kimi K3.1 Pro the highest-scoring open-weight-bound model ever benchmarked against proprietary frontier systems.

## Fast Facts: Moonshot Kimi K3.1 Pro Pricing, Benchmarks & Open Weights

- **API Launch & Open-Weights Date**: Launched October 23, 2026 on `https://api.moonshot.ai/v1`, with full open model weights scheduled for release on **November 4, 2026**, as tested and documented by [Simon Willison](https://simonwillison.net/2026/Oct/23/kimi-k31-pro/).
- **Artificial Analysis Intelligence Score (`55`)**: Ranks #2 globally behind **Claude Opus 5.5 (`58`)**, outscoring **GPT-6 Astra (`53`)**, **Claude Fable 5.1 (`53`)**, and **Gemini 4 Argon (`53`)**.
- **Standard API Pricing**: **$2.00 / 1M input tokens** and **$10.00 / 1M output tokens**—matching GPT-6.1 Sol and cutting Claude Opus 5.5 costs by 50%.
- **Highspeed Tier (`2x–3x` Faster)**: Priced at 2x standard rates for uncached tokens, while **prompt cache hits pay no Highspeed premium** (staying at the standard cache-hit rate).
- **3 Configurable Reasoning Levels**: Supports `low`, `medium`, and `max` reasoning effort levels via standard OpenAI-compatible `--responses` API parameters.

## Frontier Model Comparison: Kimi K3.1 Pro vs. Proprietary Competitors

The table below compares Kimi K3.1 Pro's intelligence score, pricing, and deployment flexibility against its closest October 2026 frontier rivals.

| Model Name | Artificial Analysis Score | Input / Output Price (per 1M Tokens) | Open Weights Available? | Best Practical Use Case |
|---|---|---|---|---|
| **Anthropic Claude Opus 5.5** | **58** (#1) | $4.00 / $20.00 | No (Proprietary) | Highest-complexity autonomous coding & low-cheat safety tasks |
| **Moonshot Kimi K3.1 Pro (Standard)** | **55** (#2) | **$2.00 / $10.00** | **Yes (Nov 4, 2026)** | High-intelligence reasoning, coding, & SVG/UI generation at 50% off Opus |
| **Moonshot Kimi K3.1 Pro (Highspeed)** | **55** (#2) | $4.00 / $20.00 *(Cache hits = 1x)* | **Yes (Nov 4, 2026)** | Interactive IDE agents & long-context cached workflows needing 2–3x speed |
| **OpenAI GPT-6.1 Sol** | ~54–55 | $2.00 / $10.00 | No (Proprietary) | Long-context financial & enterprise rubric evaluation |
| **OpenAI GPT-6 Astra / Claude Fable 5.1 / Gemini 4 Argon** | **53** | Varies by tier | No (Proprietary) | General-purpose multimodal & enterprise assistant workloads |

## How to Test Kimi K3.1 Pro Right Now From Your Terminal or IDE

Because Moonshot exposes an OpenAI-compatible Responses API at `https://api.moonshot.ai/v1`, developers do not need a custom SDK to test Kimi K3.1 Pro against their existing prompts. The model also supports three explicit reasoning effort levels—`low`, `medium`, and `max`—allowing you to trade off thinking tokens against response latency per request.

### 1. Running Kimi K3.1 Pro via the `llm` CLI Tool

As Simon Willison demonstrated using the `llm openai endpoint` command (introduced in `llm` v0.32), you can test Kimi K3.1 Pro directly from the command line and capture both the final output (`stdout`) and the internal reasoning trace (`stderr`):

```
llm openai endpoint \
https://api.moonshot.ai/v1 \
--key kimi \
-m kimi-k3.1-pro \
"Refactor this Python module to use asyncio TaskGroups and write pytest coverage" \
--responses \
-o reasoning_effort medium \
> kimi-output.txt \
2> kimi-reasoning-trace.txt
```

Switching `-o reasoning_effort` between `low`, `medium`, and `max` lets you inspect how much extra chain-of-thought reasoning improves complex visual or algorithmic tasks (such as generating multi-layered SVG illustrations or debugging race conditions) versus simple transformations.

### 2. Exploiting the Highspeed Cache-Hit Pricing Loophole

For teams running RAG pipelines, multi-turn coding agents, or large system prompts where 80%+ of each request consists of cached prefix tokens, the **Kimi K3.1 Pro Highspeed** endpoint offers an unusually favorable unit economics trick: because Moonshot does not double the price of **input token cache hits** on the Highspeed tier, heavy prompt-caching workloads get **2x to 3x faster output generation** while paying almost the same input bill as the standard endpoint.

## Practical Checklist for Engineering & AI Platform Teams

1. **Add `kimi-k3.1-pro` to Your VS Code 1.143 or LiteLLM Bake-Offs**: Point your OpenAI-compatible router at `https://api.moonshot.ai/v1` and benchmark `kimi-k3.1-pro` (`reasoning_effort: medium` and `max`) side-by-side against Claude Opus 5.5 and GPT-6.1 Sol on your hardest internal prompts.
2. **Use the Highspeed Endpoint for Cached Agent Loops**: If your coding or customer-support agent reuses a large cached codebase prefix across 20+ turns, route those calls to the Highspeed endpoint to cut user wait time by up to two-thirds without paying a 2x input penalty on cached tokens.
3. **Prepare On-Prem / VPC GPU Capacity for November 4**: Organizations in regulated finance, healthcare, or defense that cannot send code to external APIs should schedule self-hosted evaluation runs for **November 4, 2026**, when full Kimi K3.1 Pro weights become downloadable.