Can Kimi’s “Strongest-Ever” Open-Source Thinking Model Break Out of the AI Red Ocean?

Key Points

Kimi K2 Thinking debuts as an agent-native model that “thinks while using tools”.

Overview: Kimi K2 Thinking announces an agent-native, tool-aware architecture

On the evening of November 6, 2025, Yue Zhi An Mian (Yue zhi an mian 月之暗面) released an upgraded Kimi large model called Kimi K2 Thinking.

The team describes it as “Kimi’s strongest open-source thinking model to date.”

The company says Kimi K2 Thinking is trained under a “model-as-agent” paradigm and natively integrates the ability to “think while invoking tools.”

According to Kimi’s published test results, the model reaches SOTA (state-of-the-art) performance on several benchmarks including Humanity’s Last Exam, BrowseComp (autonomous web browsing), and SEAL-0 (complex information collection and reasoning).

Agentic design: up to 300 rounds of tool use and multi-step reasoning

Kimi positions K2 Thinking as a new-generation “Thinking Agent.”

The model reportedly performs tool use and internal multi-step reasoning in an integrated, autonomous way—capable of executing up to 300 rounds of tool calls and iterative thinking without human intervention.

That continuous, agentic workflow is intended to boost stability when solving complex, long-horizon tasks.

Why this matters:

• Agentic tool loops allow the model to alternate between internal reasoning and external calls (search, Python, browsing) for sustained problem solving.

• Long-horizon work benefits from repeated, autonomous tool calls because short single-shot responses often miss multi-step dependencies.

• Risk and reward—agentic operation improves depth but raises safety and cost questions that product teams must manage.

Benchmark highlights: outscoring GPT-5 (High) on a multi-domain exam

Kimi released comparative results showing clear gains in several dimensions: agentic search, agentic coding, creative writing, and composite reasoning.

• Humanity’s Last Exam (100+ professional domains, tools allowed such as search, Python and web browsing): Kimi K2 Thinking scored 44.9%, which Kimi reports as SOTA.

• For comparison, Kimi published GPT-5 (High) at 41.7% on the same test.

• BrowseComp (evaluates persistence and creativity in information-dense web-search tasks): Kimi K2 Thinking reportedly scored 60.2% (human average: 29.2%), setting a new SOTA benchmark.

• Software-engineering and terminal tasks (SWE-Multilingual, SWE-bench, terminal usage): the model shows steady improvement in multilingual coding and real-world programming tasks.

General capability upgrades and sample test

Kimi says general foundational abilities improved in parallel: creative writing, academic-style responses, and handling personal/emotional queries.

As a practical check, the original reporting outlet used the same prompt set provided to Kimi and asked the model to write a Category-1 high-school narrative essay based on the 2025 Beijing college entrance exam prompt “When Numbers Shine” (2025年北京高考作文题“数字闪耀时”).

The model produced a structurally complete, on-topic essay, though the outlet noted the expression remained somewhat stiff—similar to earlier K2 outputs.

Platform access, context size and pricing for Kimi K2 Thinking

Kimi K2 Thinking is available via API on the Kimi open platform.

Key commercial details released by Kimi are below.

• Context window: up to 256K tokens.

• Pricing for Kimi K2-0905 (same as the K2-0905 tier):

• Standard API: input ¥4 RMB ($0.56 USD) per million tokens, output ¥16 RMB ($2.22 USD) per million tokens; cache-hit input ¥1 RMB ($0.14 USD).

• Turbo API (up to 100 Token/s): input ¥8 RMB ($1.11 USD) per million tokens, output ¥58 RMB ($8.06 USD); cache-hit input ¥1 RMB ($0.14 USD).

• Note on currency conversions: USD values are approximate conversions based on ¥1 ≈ $0.14 (¥1 / $ ≈ 7.20 CNY per USD) and rounded to two decimals.

Market context: faster iteration, tougher competition

The Kimi upgrade comes as the AI model market enters a rapid iteration phase and a consolidation stage favoring large incumbents.

QuestMobile’s 2025 Q3 AI-app industry report shows leading internet groups are releasing updates at a very high cadence—averaging roughly one model release every 5.7 days for major players between January and September 2025.

That cadence is pushing competition from architecture toward practical application and deeper in-context reasoning.

Can technical lead convert to market share?

Technical metrics alone do not guarantee product-market success.

Data in October from the QuantumBit think tank (量子位智库) reported Kimi and DeepSeek ranked third and fourth in new AI assistant app downloads, with roughly 4.2 million and 3.6 million downloads respectively in the ranking period.

Both were down more than 13% month-over-month from September.

By contrast, ByteDance (Zìjié Tiàodòng 字节跳动) “Doubao” (豆包) achieved nearly 28 million new downloads, and Tencent (Tengxun 腾讯) “Yuanbao” (元宝) exceeded 13 million downloads with monthly growth of 14%—evidence that ecosystem incumbents still dominate.

Cross-industry entrants are accelerating too.

For example, Meituan (Meituan 美团) announced LongCat-Flash-Omni (LongCat-Flash-Omni 龙猫系列) as a new open model—Meituan’s fourth model release in two months—showing that non-traditional AI players are aggressively building in-house models tied to their scenarios.

Industry signal: lower interaction cost and rising commercialization

QuestMobile also highlights falling per-capita token consumption, signaling a shift toward efficiency, tighter cost control and value-driven usage—hallmarks of an industry moving from experimentation to commercial maturity.

That shift magnifies the importance of converting model capability into scenario-specific, monetizable products.

Kimi’s path to commercial relevance: scenario execution and partnerships

This year Kimi has pursued vertical partnerships and scenario-focused features to try to establish commercial footholds.

During the 2025 “Double 11” shopping festival, Kimi reportedly added a “shopping guide” feature that recommends goods and provides links to Taobao (Táobǎo 淘宝) or JD.com (Jingdong 京东) entries.

At present the recommendation flow often points to third-party agency stores rather than verified flagship stores—suggesting the product-level commerce integration and ecosystem tightness are still being built.

Compared to ecosystem-level pairings like ByteDance’s “Doubao+Douyin” or Alibaba (Alibaba 阿里巴巴) “Tongyi+e-commerce” integrations, Kimi has not yet formed equivalent commercial bindings.

Market data suggests vertical, scenario-focused AI apps can still grow if they deliver clear, repeatable value.

Examples cited include niche apps from ByteDance and Ant Group showing multi-quarter user expansion across education and health verticals.

Conclusion: technology is an entry ticket—product and scenario execution decide the winner

Kimi K2 Thinking demonstrates depth in long-form reasoning and stronger agentic tool use—important technical advantages for a “thinking agent” strategy.

But in a market where large platforms own user flows, commerce connections and distribution advantages, the critical question is whether Kimi can turn its “long-thought, deep-reasoning” strengths into habitual user value and a sustainable business model.

Technical leadership may win attention and benchmarks; sustainable growth requires scenario-anchored products that users rely on daily.

The path forward for Kimi will hinge on tighter ecosystem partnerships, improved commerce integration, and turning agentic capability into repeatable, monetizable workflows that reduce interaction costs for end users.

Final thought: long-form agentic advantage is a strong technical position, but market success depends on product-market fit and execution—this is the test for Kimi K2 Thinking.

References

• Kimi K2 Thinking release and coverage – 每日经济新闻
• Aggregated article: 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 (overview) – QuestMobile