Can Kimi K2 Thinking Break Out of the AI Red Ocean?

Kimi K2 Thinking — an open, tool-first thinking model that natively uses tools while it thinks.

Key Points

Kimi K2 Thinking is an open-source, agentic model that claims to “think while using tools.”

The developer behind the release is Yue zhi an mian (月之暗面).

What Kimi K2 Thinking Claims to Do

Kimi K2 Thinking is built as an agentic model that can autonomously call external tools such as search, Python, and web browsing during multi-step reasoning.

The model reportedly supports up to 300 rounds of tool calls and multi-turn internal reasoning without human intervention.

That continuous tool use is presented as a way to improve continuity and stability when solving complex problems.

The developer highlights strengths in agentic search, agentic coding, creative writing, and general multi-domain reasoning.

Benchmarks: Reported SOTA Results (Developer Claims)

On the multi-domain “Humanity’s Last Exam” (100+ professional fields; tests allow search, Python and web browsing), Kimi K2 Thinking reportedly scored 44.9%.

The team provided a same-session comparison for GPT-5 (High) at 41.7%.

In BrowseComp — a test designed to evaluate persistence and creativity in information-dense web searches — Kimi K2 Thinking reportedly scored 60.2%.

The reported human average on BrowseComp is 29.2%.

The model also claims steady gains on programming benchmarks including SWE-Multilingual, SWE-bench validation, and terminal-based tasks.

Reported generalized improvements include creative writing, academic reasoning, and handling personal/emotional queries.

A quick replication by a reporter requested a high-school-level narrative essay based on Beijing’s 2025 gaokao prompt “When Digital Lights Shine.”

The model produced a complete, on-topic essay, though the phrasing felt a bit mechanical in places.

API, Context Window and Pricing

The API is listed on the Kimi open platform.

The API supports up to 256K token context length.

Published pricing (in RMB) for the K2-0905–equivalent tier is:

• Standard API: ¥4 RMB input / ¥16 RMB output per million tokens (cache-hit input fee ¥1 RMB per million tokens).
• Turbo API (up to 100 tokens/sec): ¥8 RMB input / ¥58 RMB output per million tokens (cache-hit input fee ¥1 RMB per million tokens).

Currency conversions (approximate) using an assumed exchange rate of ¥7.10 = $1 USD are presented for convenience.

• ¥4 RMB ≈ $0.56 USD per million tokens (input).
• ¥16 RMB ≈ $2.25 USD per million tokens (output).
• ¥1 RMB ≈ $0.14 USD per million tokens (cache-hit input).
• ¥8 RMB ≈ $1.13 USD per million tokens (Turbo input).
• ¥58 RMB ≈ $8.17 USD per million tokens (Turbo output).

Note that USD figures are rounded and assume ¥7.10 = $1 USD, which may vary with market rates.

Why “Tool-First” Agentic Behavior Matters

“Thinking while using tools” shifts the model workflow from stateless prompt-response to a persistent agent loop: search → reason → call tools → reason again.

This loop improves performance on long-horizon, open-ended tasks like in-depth research, iterative programming, and multi-step planning.

Kimi’s reported ability to sustain hundreds of tool calls without human intervention is intended to reduce fragility when many lookups and stepwise code executions are required.

If robust in real-world usage, that capability could be decisive for agentic search tasks and developer-assistant tooling.

Market Reality: Technology Is Only the First Step

Technical leadership rarely guarantees market traction in a landscape dominated by platform incumbents and deep ecosystem integration.

The developer cited a sector summary showing that from January through September 2025 leading internet groups released or updated models 182 times.

That equals an average model release or update roughly every 5.7 days, illustrating rapid iteration in the market.

QuestMobile–style reporting points to demand fragmentation and commercialization pressure, with nearly 60% of native apps seeing negative growth in Q3.

Download metrics shared by a market research group in October place Kimi and DeepSeek in 3rd and 4th for new AI-assistant app downloads in a ranking, with Kimi above 4.2 million new installs and DeepSeek about 3.6 million.

Both brands reportedly saw downloads fall more than 13% month-on-month versus September.

By comparison, ByteDance’s Doubao (Doubao 豆包) reported nearly 28 million new downloads, and Tencent’s Yuanbao (Yuanbao 元宝) reported over 13 million downloads with a 14% month-on-month increase.

Those figures underscore the distribution and ecosystem advantage large platform players still hold.

Commercialization Attempts and Product Moves

Kimi has begun exploring vertical partnerships and commerce features.

During the 2025 “Double 11” (Singles’ Day) shopping period, the Kimi app added a product-guidance feature that recommends items and attaches links to Taobao and JD.com (JD.com 京东).

Many recommended items currently come from third-party agent stores rather than official flagship stores.

That means Kimi’s e-commerce path isn’t yet the tightly integrated, closed-loop commerce strategy that firms like Alibaba (e.g., “Tongyi + e‑commerce”) or ByteDance have built.

Market data still shows growth potential for vertical, scenario-focused AI apps.

Examples cited include ByteDance’s iMeng AI (iMeng 即梦), Doubao AiXue (豆包爱学), and Ant Group’s AQ Health Manager (AQ 健康管家), which have reported quarterly active user compound growth rates indicating vertical scenes can still win engagement.

What Kimi Needs to Do to “Break Out”

To translate technical strength into sustained market success, Kimi needs to:

• Turn agentic search and long-form reasoning into clear, repeatable product experiences users rely on daily.
• Lower integration friction with major commerce and content platforms so recommendations translate into reliable revenue.
• Demonstrate robust safety, reliability, and production latency in agentic flows where long multi-step tool usage increases error and cost risks.
• Build defensible vertical use cases — for example, a programming assistant, deep research agent, or developer tool — where persistent tool use creates real switching costs.

The reported SOTA numbers and the 256K context window give Yue zhi an mian (月之暗面) an important technical talking point in a crowded market.

But in today’s “red ocean” of large models, technology is an entry ticket, not the whole playbook.

Real breakout depends on converting technical depth into daily user value, integrated business flows, and a sustainable go-to-market strategy.

Key Takeaways for Investors, Founders, and Builders

If you care about agentic AI, watch these signals closely:

• Productized persistence: Are the long-context and long-tool-call benefits delivered as reliable product features, or just research demos?
• Distribution hooks: Can Kimi lower the friction to monetize recommendations with major platforms like Taobao and JD.com (JD.com 京东)?
• Vertical defensibility: Is there a category (developer tooling, research agents, education) where a “think while using tools” model meaningfully locks in users?
• Operational costs and safety: How will long agentic flows affect latency, cloud costs, and failure modes in production?

These are the practical questions investors and operators should ask beyond benchmarks and lab scores.

Relevant Links and References

• 原生掌握“边思考，边用工具” Kimi “史上最强”开源思考模型能否破局AI红海？ – 每日经济新闻
• 原生掌握“边思考，边用工具” Kimi “史上最强”开源思考模型能否破局AI红海？ – 东方财富
• 2025 Q3 AI App Industry Report – QuestMobile

Kimi K2 Thinking