Kimi K3
Run frontier-level coding and reasoning tasks on a 2.8-trillion-parameter open-weight model, with the option to self-host once the full weights ship.
The verdict on Kimi K3: Developers who need long-horizon reasoning and multi-file coding work from an open-weight model Kimi K3 is aimed at developers who need long-horizon reasoning and multi-file coding work from an open-weight model that competes with closed frontier performance on benchmarks. Pricing: Pay-per-token API ($0.30-$15 per million tokens) / full weights free to self-host after public release. Last reviewed: August 2026.
Best For
Developers who need long-horizon reasoning and multi-file coding work from an open-weight model
Standout Feature
A 2.8-trillion-parameter model competitive with closed frontier models on independent coding and math benchmarks
TL;DR
Genuinely impressive open-weight performance, self-hosting the full model requires serious GPU infrastructure and it's very new.
Alternatives
Overview
Kimi K3 is Moonshot AI's 2.8-trillion-parameter open-weight model, built for long-horizon reasoning and coding work that runs across many steps instead of a single response. It handles multi-file codebases, agentic tool-calling loops, and extended chains of reasoning at a scale that puts it in range of the top closed frontier models on independent coding and math benchmarks. Access is available now through the API at usage-based rates, with the full model weights scheduled for a free release so teams can self-host rather than depend on a single vendor's uptime and pricing.
The scale that makes K3 competitive also means it needs serious infrastructure to self-host once the weights are public, so most users will start on the hosted API and only move to self-hosting once they have the hardware to justify it. Kimi K3 fits engineering teams and researchers who want frontier-level reasoning and coding performance with the option to eventually run the model on their own infrastructure instead of staying locked into a single API.
Our Take
At 2.8 trillion parameters, agentic tool-calling loops and extended reasoning chains are practical rather than theoretical. Open weights mean no long-term vendor lock-in, and the option to self-host matters for teams with data sovereignty requirements. Two honest constraints apply: self-hosting the full model requires substantial GPU infrastructure, and it's a new release with limited long-term reliability data. If coding and reasoning at frontier scale is the requirement, evaluate it now, but plan for real infrastructure costs.
Key Features
- 2.8-trillion-parameter open-weight architecture
- Long-horizon multi-step reasoning and agentic tool-calling
- Multi-file codebase support for real-world coding tasks
- Free self-hostable weights on public release
- • Competitive with closed frontier models on coding and math benchmarks
- • Open weights mean no long-term vendor lock-in
- • Pay-per-token API access available immediately, no waitlist
- • Self-hosting the full model requires substantial GPU infrastructure
- • Very new release with limited independent long-term reliability data
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