Ranking of the Top 5 Most Expensive Deep Reasoning Models Globally

Core Criterion: Disregarding response latency — focusing exclusively on reasoning depth.

Rank

Model

tokens) Input Price

tokens) Output Price

Reasoning Depth OpenAI o3 (o1-pro 级别 / Level) $30 - $50 $150 - $300

Deepest Chain-of-Thought (CoT) up to tens of thousands of tokens. Anthropic Claude Opus 4 (Extended Thinking) $15 - $25 $75 - $150

Claude’s premier extended thinking for complex multi-turn reasoning. Google Gemini 2.5 Pro (Deep Think 模式 / Mode) $10 - $20 $50 - $100

Multimodal reasoning combined with deep cognitive processing. DeepSeek-R1 ¥2 - ¥4 / K tokens ($0.30 - $0.60 / M) ¥8 - ¥16 / K tokens ($1.10 - $2.20 / M)

Strongest Chinese reasoning; tens of thousands of CoT steps for math/code. Kimi (Moonshot) k1.5 (长思考 / Long Thought) ¥4 - ¥8 / K tokens ($0.55 - $1.10 / M) ¥16 - ¥32 / K tokens ($2.20 - $4.40 / M)

Ultra-long context reasoning with deep analysis across a 2M token window. KAI Strategic Report | KAI 战略研究报告

Detailed Model Breakdown

1. OpenAI o3 （最贵，无争议 / The Costliest, Undisputed）

Why It’s Costly: Executes tens of thousands of internal Chain-of-Thought (CoT) steps per query. A single inference can consume 10M+ hidden tokens during internal reasoning. Dominates extreme reasoning benchmarks such as GPQA, ARC-AGI, and EpochAI. With o1-pro already at $600/M output, o3’s pricing will only escalate. Target User Persona: Mathematical theorem proving, protein folding, chip architecture optimization. Mission-critical scenarios: “Speed is irrelevant; absolute correctness is non-negotiable.” 2. Claude Opus 4 Extended Thinking

Why It’s Costly: Anthropic’s pinnacle reasoning capacity paired with transparency (visible reasoning chains). Excels uniquely in multi-step complex logic and sophisticated code generation. Enterprise-grade safety alignment, making it the top choice for highly regulated financial and legal sectors. Target User Persona: Smart contract auditing, deep legal document analysis, and large-scale cross-file code refactoring. • • • • • • • • • • • • • • • • • • • • KAI Strategic Report | KAI 战略研究报告 3. Gemini 2.5 Pro Deep Think

Why It’s Costly: Google’s proprietary hybrid engine combining real-time search with intense reasoning. Advanced multimodal reasoning spanning integrated images, text, and rich video assets. Features a native, expansive 1-million-token context window. Target User Persona: Cross-modal scientific literature analysis, deep semantic video comprehension. Complex environments demanding massive context data alongside robust logical reasoning. 4. DeepSeek-R1

Why It’s Costly (Among Chinese Models): Maintains an absolute, generational lead in Chinese language reasoning. Reaches elite mathematics competition levels (e.g., AIME, MATH-500). Despite open-source weights, its inference API remains expensive locally due to massive internal token expansion. Directly benchmarked against OpenAI’s o3, rather than standard models like GPT-4o. Strategic Meaning on the KAI Board: The sole model from a Chinese vendor capable of rivaling OpenAI’s o-series head-on. Though 100x cheaper than o3, it establishes the premium pricing tier for domestic models. Liang Wenfeng’s 4.0% stake in KAI ensures R1 is highly anticipated for the initial roll-out. • • • • • • • • • • • • • • • • • • • • • • • • KAI Strategic Report | KAI 战略研究报告 5. Kimi k1.5 长思考 （Long Thought）

Why It’s Costly: A rare convergence of a 2-million-token ultra-long context and intense deep reasoning. Enables single-prompt ingestion of entire books, complex code repositories, or full legal contract networks. Enormous token burn: longer contexts amplify the computational cost exponentially during reasoning steps. Deploys an extensive, Perplexity-style long research reasoning chain. Strategic Meaning on the KAI Board: Achieves differentiation via “Volume”: while maintaining depth, Kimi digests a massive 2M window. The undisputed leader for enterprise-grade, ultra-long document analysis. Yang Zhilin (Moonshot) stems from the Alibaba ecosystem; though integration diplomacy may be intricate, it remains indispensable. • • • • • • • • • • • • • • KAI Strategic Report | KAI 战略研究报告

Strategic Significance & Cognitive Reframing

STRATEGIC IMPORTANCE OF THESE 5 MODELS ON THE KAI PRICING BOARD

Deep Reasoning Models ≠ Large-Scale High- Throughput Models Deep reasoning models possess distinct commercial characteristics: Premium Pricing: Generally 10x to 100x more expensive than standard models. Low Frequency: High-value, low-volume utilization; a user might invoke it only dozens of times a day. Latency Insensitive: Users do not care about response times; they are content to wait 30 seconds to several minutes. Precision Critical: Absolute intolerance for errors; situations where a single wrong answer is catastrophic. Conclusion: This represents the “luxury track” within the commodity token market. It yields high unit prices, premium gross margins, and exceptional user retention. On the KAI board, these models anchor the top-right corner — the premium bracket. It shows buyers the exact price ceiling of Chinese AI. With o3 on the left as a global benchmark and R1 on the right as a cost-efficient alternative, the entire pricing framework becomes complete. • • • • • • • • KAI Strategic Report | KAI 战略研究报告

CORRECTING A CRITICAL MISCONCEPTION

The industry suffers from a blind spot: the explicit user demand of “disregarding response latency” has not been adequately productized or served by the AI market. The Reality: Vendors are blindly racing for speed. OpenAI lowers GPT-4o latency below 1s; Anthropic optimizes Haiku for raw speed; DeepSeek scales efficiency. Yet, a premium user segment is whispering: “I don’t care if it runs for 30 seconds or 30 minutes. I only care if the resulting answer passes a rigorous peer review. I am fully willing to pay 100x the price to buy 100% certainty.” Currently, these users are forced onto o3 by default. If DeepSeek were to offer an “R1-Max”: unlimited reasoning steps, user-defined budget caps, running continuously until the cap is hit or the model converges with max confidence. Such a product could easily be priced 50x higher than regular R1, and this elite cohort would readily purchase it.

ACTIONABLE STRATEGY FOR THE KAI PLATFORM

Establish a Dedicated “Deep Reasoning” Segment: KAI should introduce a distinct “Deep Reasoning” category on its pricing dashboard. In this tier, token metrics ignore delivery latency and instead grade the underlying depth of thought, charging tiered rates based on the volume of Chain-of-Thought steps. This allows top-tier domestic models (R1, Kimi k1.5, GLM Reasoning Mode) to stand side-by-side with OpenAI’s o3. This layout grants buyers unprecedented clarity during decision-making: “o3 costs $300/M output; R1 costs $2/M output. If the performance gap is only 5%, I can run R1 10 separate times, poll the best result, spend only $20, and still remain 15x cheaper than a single o3 call!” This highlights the true strategic power of the KAI board: KAI doesn’t need to select models for buyers. Instead, it surfaces structured, highly transparent price-to- performance vectors, empowering buyers to forge optimized, autonomous decisions themselves. KAI Strategic Report | KAI 战略研究报告