Kimi K2 has 1 trillion parameters and scores 65.8% on SWE-bench — the highest open-model software engineering score ever recorded. Free on LITHOVEX AI.
Kimi-K2 Instruct is an AI model released July 2025 by Moonshot AI, a leading organization in large language model research. Moonshot AI's flagship — 1T MoE param model with exceptional agentic coding, reasoning, and software engineering. With 1T total (32B active via MoE) and a 128K tokens context window, Kimi-K2 Instruct is designed to excel across a range of demanding tasks — from software engineering and mathematical problem-solving to multilingual content generation and long-document analysis.
Kimi-K2 Instruct employs a Mixture-of-Experts (MoE) architecture with 1T total (32B active via MoE). In a MoE design, only a subset of "expert" sub-networks activates for each token during inference — Kimi-K2 Instruct has 1T total (32B active via MoE) total parameters but uses only a fraction per token. This allows the model to store significantly more learned knowledge than a comparably-priced dense model. MoE architectures have proven effective at reaching frontier-level capability while keeping serving costs manageable — a key reason Kimi-K2 Instruct can be offered at the scale that platforms like LITHOVEX AI make possible.
As of 2026-10-16, Kimi-K2 Instruct by Moonshot AI demonstrates measurable performance on standard AI evaluation benchmarks. On SWE-bench Verified — which tests ability to resolve real GitHub issues in production Python repositories — it achieves 65.8%. For software engineering tasks, Kimi-K2 Instruct scores 65.8% on SWE-bench Verified, which tests ability to resolve real GitHub issues in production Python repositories. In mathematical reasoning, Kimi-K2 Instruct records 78.4% on AIME 2025. Across all 6 benchmarks tracked, the model shows consistent capability in its primary task domains. These scores place Kimi-K2 Instruct — at 1T total (32B active via MoE) of parameters — in a competitive position within its model class. Note that benchmark scores reflect controlled evaluation conditions; real-world performance varies by task, prompt design, and domain specificity.
The primary strengths of Kimi-K2 Instruct are: 65.8% SWE-bench Verified — highest open-model software engineering score ever recorded; 97.6% MATH-500 — elite mathematical reasoning; 1 trillion total parameters — largest MoE model publicly released; Agentic by design: multi-step tool use, code execution, browsing. These make it particularly well-suited for Elite software engineering AI: autonomous code review, bug fixing in production repos, Enterprise agentic systems with multi-tool orchestration, Frontier mathematical reasoning. Users working on complex, multi-step problems benefit most from Kimi-K2 Instruct's ability to maintain coherence across long inputs and produce structured, verifiable outputs.
Like all current AI systems, Kimi-K2 Instruct has limitations: 1T params requires 8+ H100 80GB minimum; MoE complexity requires specialized inference stack. For production deployments, validating outputs against authoritative sources — especially for domain-specific or time-sensitive information — remains best practice. Moonshot AI continues to improve Kimi-K2 Instruct through ongoing research.
Kimi-K2 Instruct is available on LITHOVEX AI with a free account. Unlike direct API access — which requires billing setup, API key management, and per-token charges — LITHOVEX AI provides a seamless browser interface with no setup required, making Kimi-K2 Instruct accessible to students, researchers, developers, and early-stage teams globally.
As of 2026-10-16, Kimi-K2 Instruct by Moonshot AI demonstrates measurable performance on standard AI evaluation benchmarks. On SWE-bench Verified — which tests ability to resolve real GitHub issues in production Python repositories — it achieves 65.8%. For software engineering tasks, Kimi-K2 Instruct scores 65.8% on SWE-bench Verified, which tests ability to resolve real GitHub issues in production Python repositories. In mathematical reasoning, Kimi-K2 Instruct records 78.4% on AIME 2025. Across all 6 benchmarks tracked, the model shows consistent capability in its primary task domains. These scores place Kimi-K2 Instruct — at 1T total (32B active via MoE) of parameters — in a competitive position within its model class. Note that benchmark scores reflect controlled evaluation conditions; real-world performance varies by task, prompt design, and domain specificity.
Mixture-of-Experts: 1 trillion total / 32B active per token
The training pipeline for Kimi-K2 Instruct involves multiple stages: large-scale pretraining on Large-scale multilingual pretraining plus reinforcement learning for agentic capabilities to develop broad world knowledge, followed by supervised fine-tuning on instruction-response pairs, and alignment techniques such as RLHF or DPO. Each stage refines the model's helpfulness, accuracy, and safety. Moonshot AI's research team — based in China — has optimized Kimi-K2 Instruct for the specific capability profile described above.
Inference efficiency: Kimi-K2 Instruct at 1T total (32B active via MoE) is designed to deliver quality outputs at practical serving speeds. Its 128K tokens context window enables processing entire documents, multi-file codebases, and extended conversation histories — particularly valuable for contract review, technical documentation analysis, and codebase-level AI assistance.
Getting started with Kimi-K2 Instruct on LITHOVEX AI takes under two minutes. Follow these steps:
For best results with Kimi-K2 Instruct: provide specific, context-rich prompts; include examples when generating code; specify output format when structure matters; break complex tasks into focused sub-questions. Since Kimi-K2 Instruct supports extended reasoning, prompting it to "think step by step" or "show your reasoning" activates deeper chain-of-thought analysis for math, logic, and complex problems.
Complex autonomous software engineering tasks
Repository-level code understanding and modification
Agentic tool-calling and multi-step API workflows
Advanced mathematics and scientific reasoning
Long-context analysis of entire software repositories
Moonshot AI is a Beijing-based startup founded in 2023, focused on ultra-long context language models and advanced agentic reasoning. Best known for its Kimi assistant — which gained prominence for processing extremely long documents — the company scaled rapidly to frontier-level models. Kimi K2, released in 2025, deploys 1 trillion total parameters in a Mixture-of-Experts configuration and achieves top scores on SWE-bench Verified, the gold standard for software engineering AI. Moonshot AI has attracted over $1 billion in funding from investors including Alibaba and Sequoia China, positioning it as one of China's fastest-growing AI research companies.
Funding: Over $1B raised; Alibaba, Sequoia China
Founded: 2023, headquartered in China.
Kimi-K2 Instruct by Moonshot AI competes in a field with several strong alternatives. Below is a detailed side-by-side comparison against commonly listed alternatives, using benchmark data and capability profiles in the catalog.
Both Kimi-K2 Instruct and the models above are available free on LITHOVEX AI — test them side by side on your real tasks before committing to a subscription with any provider.
Kimi-K2 Instruct is released under Modified Apache 2.0 (Kimi K2 Model License), which means you can download and run it on your own hardware. Below are the quickest ways to get started.
LITHOVEX AI runs Kimi-K2 Instruct on cloud infrastructure, so you can use it instantly in your browser without downloading anything, configuring drivers, or managing VRAM. Create a free account and start in under 60 seconds.
Kimi K2 is the first Chinese model to compete directly with Claude and GPT-4o on software engineering benchmarks, signaling the global convergence of AI capability. Its open-weight release makes it accessible to researchers and enterprises worldwide.
Kimi-K2 Instruct by Moonshot AI has the following version history and notable updates:
For the complete changelog and latest release notes, visit the official Moonshot AI model page.