Sky9 Portfolio Company, Moonshot AI, Unveils Kimi K2 Thinking, Its Best Open-Source Reasoning Model

This week, Moonshot AI, a leading AI technology company and a Sky9 Capital portfolio company, announced the open-source release of its “Kimi K2 Thinking” model, which is its most powerful open-source reasoning model to date, according to the company.

Built as a thinking agent, Kimi K2 Thinking reasons step by step WHILE using tools, achieving state-of-the-art performance on Humanity’s Last Exam (HLE), BrowseComp, and other benchmarks, with major gains in reasoning, agentic search, coding, writing, and general capabilities.

Kimi K2 Thinking can execute up to 200 – 300 sequential tool calls without human interference, reasoning coherently across hundreds of steps to solve complex problems.

It marks Moonshot AI’s latest efforts in test-time scaling, by scaling both thinking tokens and tool calling steps.

K2 Thinking is now live on kimi.com under the chat mode, with its full agentic mode available soon. It is also accessible through the Kimi K2 Thinking API.

Evaluations

Kimi K2 Thinking sets new records across benchmarks that assess reasoning, coding, and agent capabilities. K2 Thinking achieves 44.9% on HLE with tools, 60.2% on BrowseComp, and 71.3% on SWE-Bench Verified, demonstrating strong generalization as a state-of-the-art thinking agent model.

Agentic Reasoning

K2 Thinking demonstrates outstanding reasoning and problem-solving abilities. On Humanity’s Last Exam (HLE)—a rigorously crafted, closed‑ended benchmark—spanning thousands of expert‑level questions across more than 100 subjects, K2 Thinking achieved a state-of-the-art score of 44.9%, with search, python, and web-browsing tools, establishing new records in multi‑domain expert‑level reasoning performance.

By reasoning while actively using a diverse set of tools, K2 Thinking is capable of planning, reasoning, executing, and adapting across hundreds of steps to tackle some of the most challenging academic and analytical problems.

Agentic Coding

K2 Thinking exhibits substantial gains in coding and software development tasks. It achieves scores of 61.1% on SWE-Multilingual, 71.3% on SWE-Bench Verified, and 47.1% on Terminal-Bench, showcasing strong generalization across programming languages and agent scaffolds.

The model delivers notable improvements on HTML, React, and component-intensive front-end tasks—translating ideas into fully functional, responsive products. In agentic coding settings, it reasons while invoking tools, integrating fluidly into software agents to execute complex, multi-step development workflows with precision and adaptability.

Agentic Search and Browsing

K2 Thinking demonstrates strong performance in agentic search and browsing scenarios. On BrowseComp—a challenging benchmark designed to evaluate models’ ability to continuously browse, search, and reason over hard-to-find real-world web information—K2 Thinking achieved a score of 60.2%, significantly outperforming the human baseline of 29.2%. This result highlights K2 Thinking’s superior capability for goal-directed, web-based reasoning and its robustness in dynamic, information-rich environments.

K2 Thinking can execute 200–300 sequential tool calls, driven by long-horizon planning and adaptive reasoning. It performs dynamic cycles of think → search → browser use → think → code, continually generating and refining hypotheses, verifying evidence, reasoning, and constructing coherent answers. This interleaved reasoning allows it to decompose ambiguous, open-ended problems into clear, actionable subtasks.

General Capabilities

Creative Writing: K2 Thinking delivers improvements in completeness and richness. It shows stronger command of style and instruction, handling diverse tones and formats with natural fluency. Its writing becomes more vivid and imaginative—poetic imagery carries deeper associations, while stories and scripts feel more human, emotional, and purposeful. The ideas it expresses often reach greater thematic depth and resonance.

Practical Writing: K2 Thinking demonstrates marked gains in reasoning depth, perspective breadth, and instruction adherence. It follows prompts with higher precision, addressing each requirement clearly and systematically—often expanding on every mentioned point to ensure thorough coverage. In academic, research, and long-form analytical writing, it excels at producing rigorous, logically coherent, and substantively rich content, making it particularly effective in scholarly and professional contexts.

Personal & Emotional: When addressing personal or emotional questions, K2 Thinking responds with more empathy and balance. Its reflections are thoughtful and specific, offering nuanced perspectives and actionable next steps. It helps users navigate complex decisions with clarity and care—grounded, practical, and genuinely human in tone.

Inference Efficiency

Low-bit quantization is an effective way to reduce inference latency and GPU memory usage on large-scale inference servers. However, thinking models use excessive decoding lengths, and thus quantization often results in substantial performance drops.

To overcome this challenge, Kimi adopts Quantization-Aware Training (QAT) during the post-training phase, applying INT4 weight-only quantization to the MoE components. It allows K2 Thinking to support native INT4 inference with a roughly 2x generation speed improvement while achieving state-of-the-art performance. All benchmark results are reported under INT4 precision.

Full Evaluations*

The table below shows that Kimi K2 Thinking matches or surpasses the latest open-source and frontier models across a wide range of tasks, excelling on benchmarks for reasoning, agentic search, and coding.

*Testing Details: a. All benchmarks were evaluated at temperature = 1.0 and 256 k context length for K2 Thinking, except for SciCode, for which we followed the official temperature setting of 0.0. b. HLE (no tools), AIME25, HMMT25, and GPQA were capped at a 96k thinking-token budget, while IMO-Answer Bench, LiveCodeBench and OJ-Bench were capped at a 128k thinking-token budget. Longform Writing was capped at a 32k completion-token budget. c. For AIME and HMMT (no tools), we report the average of 32 runs (avg@32). For AIME and HMMT (with Python), we report the average of 16 runs (avg@16). For IMO-AnswerBench, we report the average of 8 runs (avg@8).