# Kimi K2

> Moonshot AI's original open-weight 1T-parameter MoE (32B active) for agentic coding and tool use.

- Maker: Moonshot AI (Kimi)
- Line: Kimi K2
- Canonical page: https://ai-tldr.dev/models/kimi-k2/
- Updated: 2026-06-18

## In short

Kimi K2 is Moonshot AI (Kimi)'s original trillion-parameter open-weight model, released on July 11, 2025. It is a Mixture-of-Experts (MoE) language model with 1 trillion total parameters but only 32 billion activated per token, which keeps inference costs far below a dense model of comparable scale. It has a 128K-token context window and costs $0.55 per million input tokens and $2.20 per million output tokens. You can use it through OpenRouter.

## Facts

| Fact | Value | Source |
| --- | --- | --- |
| Context window | 128K (max output 128K) | [1] |
| Input price | $0.55 / 1M tokens (cached $0.15 / 1M tokens) | [2] |
| Output price | $2.20 / 1M tokens | [2] |
| Licence | Modified MIT License · open weights | [1] |
| Released | 11 July 2025 | [3] |
| Parameters | 1T total / 32B active (MoE) | [1] |

## Sources

1. [Kimi-K2-Instruct model card (Hugging Face)](https://huggingface.co/moonshotai/Kimi-K2-Instruct)
2. [Kimi K2 0711 — pricing & providers (PricePerToken)](https://pricepertoken.com/pricing-page/model/moonshotai-kimi-k2)
3. [Kimi K2: Open Agentic Intelligence (official)](https://moonshotai.github.io/Kimi-K2/)

## Overview

Kimi K2 is Moonshot AI (Kimi)'s original trillion-parameter open-weight model, released on July 11, 2025. It is a Mixture-of-Experts (MoE) language model with 1 trillion total parameters but only 32 billion activated per token, which keeps inference costs far below a dense model of comparable scale. Moonshot shipped it as two checkpoints under a Modified MIT License: Kimi-K2-Base, the raw pre-trained foundation model, and Kimi-K2-Instruct, post-trained for chat, coding and tool use.

Kimi K2 was positioned as an 'agentic intelligence' model rather than a step-by-step reasoner: it answers directly (no long chain-of-thought) but is tuned to call tools and execute multi-step coding and software-engineering tasks. It was pre-trained on 15.5 trillion tokens with Moonshot's Muon optimizer and uses Multi-head Latent Attention (MLA) and SwiGLU across 61 layers and 384 experts, with a 128K-token context window. It handles text only.

At launch Kimi K2 stood out as one of the strongest open-weight non-reasoning models on coding and agentic benchmarks such as SWE-bench Verified, and its open weights on Hugging Face made it widely self-hostable. It later seeded a fast-moving line (Kimi K2-Instruct-0905, the K2 Thinking reasoning variant, and the K2.5/K2.6 releases). The original 0711 checkpoint reached end-of-life on Moonshot's first-party API on May 25, 2026, but the weights remain freely available and the model is still served by third-party providers.

## Benchmarks

- SWE-bench Verified (agentic, single attempt): 65.8% ([source](https://huggingface.co/moonshotai/Kimi-K2-Instruct))
- SWE-bench Verified (agentless): 51.8% ([source](https://huggingface.co/moonshotai/Kimi-K2-Instruct))
- LiveCodeBench v6 (Pass@1): 53.7% ([source](https://huggingface.co/moonshotai/Kimi-K2-Instruct))
- MultiPL-E (Pass@1): 85.7% ([source](https://huggingface.co/moonshotai/Kimi-K2-Instruct))
- AIME 2024 (Avg@64): 69.6% ([source](https://huggingface.co/moonshotai/Kimi-K2-Instruct))
- AIME 2025 (Avg@64): 49.5% ([source](https://huggingface.co/moonshotai/Kimi-K2-Instruct))
- MATH-500: 97.4% ([source](https://huggingface.co/moonshotai/Kimi-K2-Instruct))
- MMLU (Exact Match): 89.5% ([source](https://huggingface.co/moonshotai/Kimi-K2-Instruct))
- MMLU-Pro (Exact Match): 81.1% ([source](https://huggingface.co/moonshotai/Kimi-K2-Instruct))
- GPQA-Diamond (Avg@8): 75.1% ([source](https://huggingface.co/moonshotai/Kimi-K2-Instruct))
- Tau2 Retail (Avg@4): 70.6% ([source](https://huggingface.co/moonshotai/Kimi-K2-Instruct))
- AceBench: 76.5% ([source](https://huggingface.co/moonshotai/Kimi-K2-Instruct))

## Pricing

- Input: $0.55 / 1M tokens per 1M tokens
- Cached input: $0.15 / 1M tokens per 1M tokens
- Output: $2.20 / 1M tokens per 1M tokens

Original Moonshot/Kimi launch pricing for kimi-k2-0711-preview (cache-miss input $0.55, cached input $0.15, output $2.20). Third-party listings (e.g. OpenRouter) are broadly similar (~$0.57 input / ~$2.30 output). The first-party Kimi/Moonshot API retired the 0711 checkpoint on 2026-05-25; because the weights are open, it can also be self-hosted or run via third-party providers.

Source: https://pricepertoken.com/pricing-page/model/moonshotai-kimi-k2

## Strengths

- Open weights under a permissive Modified MIT License, fully self-hostable
- Strong agentic coding for its release window (65.8% on SWE-bench Verified, agentic)
- Trillion-parameter quality at 32B active-parameter inference cost (sparse MoE)
- Excellent math accuracy (97.4% on MATH-500)
- 128K-token context for large codebases and long documents
- Tool-use / agent oriented post-training in the Instruct checkpoint

## Best for

- Agentic coding assistants and automated software-engineering workflows
- Self-hosted or private deployment where open weights are required
- Tool-calling agents that orchestrate multi-step tasks
- Math and quantitative reasoning over text
- Long-document and large-codebase analysis using the 128K context
- Cost-sensitive high-volume inference via the sparse MoE design

## How to access

- [OpenRouter](https://openrouter.ai/moonshotai/kimi-k2) — model id `moonshotai/kimi-k2`

## Kimi K2 versions

- [Kimi K2.7-Code](https://ai-tldr.dev/models/kimi-k2-7-code/) (12 June 2026)
- [Kimi K2.6](https://ai-tldr.dev/models/kimi-k2-6/) (20 April 2026)
- [Kimi K2.5](https://ai-tldr.dev/models/kimi-k2-5/) (27 January 2026)
- [Kimi K2-Instruct-0905](https://ai-tldr.dev/models/kimi-k2-instruct-0905/) (9 September 2025)
- Kimi K2 (11 July 2025) — this page

## FAQ

### Is Kimi K2 open source?

Yes. Moonshot AI released Kimi K2 with open weights on Hugging Face under a Modified MIT License, in two checkpoints: Kimi-K2-Base (the pre-trained foundation model) and Kimi-K2-Instruct (post-trained for chat and agentic use). You can download and self-host either one.

### How big is Kimi K2 and what context window does it support?

Kimi K2 is a Mixture-of-Experts model with 1 trillion total parameters but only 32 billion activated per token, so inference is far cheaper than a dense model of similar size. It supports a 128K-token context window and handles text only.

### How is Kimi K2 different from Kimi K2 Thinking?

The original Kimi K2 (0711) is a non-reasoning, agentic model: it answers directly and is tuned for tool use and coding. Kimi K2 Thinking, released later, is an explicit reasoning variant that produces extended chain-of-thought and chains hundreds of sequential tool calls. They share the trillion-parameter MoE architecture.

### Can I still use Kimi K2 0711?

Moonshot retired the original 0711 checkpoint on its first-party Kimi/Moonshot API on May 25, 2026, recommending newer models like K2.5 or K2.6. However, because Kimi K2's weights are open, you can still self-host it or access it through third-party inference providers such as OpenRouter.

## Links

- [Kimi K2: Open Agentic Intelligence (official)](https://moonshotai.github.io/Kimi-K2/)
- [Kimi-K2-Instruct model card (Hugging Face)](https://huggingface.co/moonshotai/Kimi-K2-Instruct)
- [Kimi K2 GitHub repository](https://github.com/MoonshotAI/Kimi-K2)
- [Kimi K2 0711 — OpenRouter pricing & providers](https://openrouter.ai/moonshotai/kimi-k2)
- [Kimi K2 0711 — pricing & providers (PricePerToken)](https://pricepertoken.com/pricing-page/model/moonshotai-kimi-k2)
- [Artificial Analysis — Kimi K2 intelligence & price analysis](https://artificialanalysis.ai/models/kimi-k2)
- [Moonshot AI (Kimi) — official site](https://www.moonshot.ai/)

Source: AI/TLDR LLM registry, https://ai-tldr.dev/models/kimi-k2/