Report information

• Title: State of AI | OpenRouter (Empirical Study)
• Publishing Authority: OpenRouter
• Published: December 5, 2024 (reported observation period spans 2024-2025)
• Original link: State of AI | OpenRouter
• Data Foundation: Real interactive metadata based on OpenRouter unified inference layer 100 trillion (Trillion) tokens, covering 300+ models and 60+ providers around the world.

Report Key Insights (Abstract & Empirical Findings)

OpenRouter’s report avoids traditional subjective evaluations and instead starts from the “real behavior” of large-scale production environments, revealing in-depth patterns in the following dimensions:

1. Paradigm shift of Agentic Inference

• Absolute dominance of inference models: The report points out that since the release of the o1 class model at the end of 2024, AI usage has shifted from “content generation” to “multi-step reasoning.” By 2025, more than 50% of total token traffic will flow to inference optimization models (led by xAI’s Grok Code Fast, followed by the Gemini 2.5 series and DeepSeek R1).
• Tool-Calling Trend: Data display tool calling is no longer an option for developers, but a default for high-value workflows. The Claude 3.5/3.7 series dominated in the early days, and then Grok and GLM 4.5 quickly entered the market, reflecting that action through planning is the moat for future models.

2. Dynamic balance between open source and closed source (Market Equilibrium)

• 30% “Open Source Ceiling”: Although closed source models still account for 70% of the market share (focusing on regulated enterprise-level workflows), open source/weighted open models (OSS) have stabilized at around 30%.
• The Rise of China’s Open Source Model: The release of DeepSeek V3 and Qwen 3 Coder directly led to a surge in usage. Especially in the field of code assistance (Programming), the Chinese open source model briefly accounted for more than half of OSS code tasks in mid-2025.

3. Model family usage profiles (Provider Profiles)

The report reveals users’ “cognitive division of labor” towards different model brands:

• Anthropic (Claude): Extreme focus on Programming and Technology (80%+). Users consider Claude their go-to choice for complex reasoning and engineering.
• Google (Gemini): The most diverse performance, covering translation, science, law and general knowledge, showing the characteristics of “digital encyclopedia/information engine”.
• OpenAI (GPT): It has undergone a transformation from early science and general knowledge to deeper developer workflow and productivity tools. Its positioning is between Claude’s professionalism and Google’s diversity.
• DeepSeek: shows an amazing consumer-heavy, with more than 2/3 of the traffic coming from creativity, entertainment and role-playing.

4. Retention Analysis: Cinderella’s “Glass Slipper Effect”

This is the most interesting finding in the report. The study observed:

• Foundational Cohorts: Early users (such as first-month users of Gemini 2.5 or Claude 4 Sonnet) have retention rates of up to 40% after 5 months, much higher than those of subsequent users.
• First solution advantage: When a model is the first to solve a specific problem (such as a complex Tool-use or logic difficulty), the user group will have a strong path dependence (Cognitive inertia). This is the “glass shoe” effect: once adapted, a powerful locking effect will occur.

5. Cost and Demand Elasticity (Jevons Paradox)

• Price Inelastic: Interestingly, a 10% price drop only drives 0.5-0.7% usage growth. This shows that “quality and trust” are far more important than price, and top companies are willing to pay a premium for stability.
• Jevons Paradox: Although the price elasticity is low at the macro level, in the field of “Efficient Giants” (such as Gemini Flash or DeepSeek), low cost does induce larger Token consumption (users start to perform more iterations and longer context queries).

My understanding: academic and practical inspiration behind the data

This “empirical” report elevates the discussion of AI from “whether it is easy to use” to “how to deploy it systematically.”

1. Implications for personal research fields (political work and assistant behavior)

• Inference Dividends and Agentization: More than 50% of the models in the report turned to inference, validating my observation that assistants in political work are leveraging AI to handle more “judgmental” tasks. Future research should shift from “whether assistants use AI” to “how assistants guide public opinion through AI’s agentic workflow.”
• Scenario Adaptation (Glass Slipper): For political assistants, the first model that can accurately craft their own tone or analyze the sensitivities of a constituency will create loyalties that are extremely difficult to break. This explains why some offices are stuck on older versions of GPT or specific models.

2. Inspiration for the development of AI in Taiwan and cross-strait

• Export advantages of open source models: The explosive growth of DeepSeek and Qwen on OpenRouter proves that “open source orientation” is the main driver of Chinese models going global. Taiwan can boldly integrate these “Efficient Giants” on the application side and use cost savings for front-end scene optimization.
• The necessity of multi-model architecture: Since no single model can dominate all portraits (DeepSeek for Roleplay, Claude for Code), Taiwanese companies and think tanks should adopt “Multi-model Stack” to achieve the best balance between cost and performance.

3. Thoughts on the direction of academic research

• Focus on the bias of agent inference: When AI begins to autonomously call tools and plan paths (Agentic Inference), its bias will no longer just be “saying the wrong thing”, but “doing the wrong thing”. This is an extremely critical new topic in political and legal studies.
• Application of Retention Rate Research: We should study how to shorten the journey for users to find the “glass slipper” model to increase the success rate of digital transformation.

Conclusion

OpenRouter’s data reveals a cruel but hopeful truth: In the great era of AI, the emergence of inference models such as o1 has not capped competition, but has opened up a new battlefield of “multi-step operation” and “scene adaptation”**. The future does not belong to the person with the biggest model, but to the person who can accurately find the “glass slipper”.