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.
• 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.

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.