Discovering Multiple Frontiers in AI: An In-depth Analysis of Reasoning Models, Cost, and Speed

Welcome to my talk about AI frontiers, where we'll explore the trade-offs between intelligence, cost, and speed in the rapidly evolving landscape of artificial intelligence.

1. George is a co-founder of Artificial Analysis, an independent AI benchmarking company.
2. Artificial Analysis benchmarks a broad spectrum across AI, including models for intelligence, API endpoints for speed and cost, hardware accelerators, and various modalities such as language, visio
3. The company publishes benchmarks for over 150 different models across various metrics on their website, artificialanalysis.ai, along with reports (many publicly accessible) and enterprise subscript
4. George discusses the progress in AI, highlighting OpenAI's role in kickstarting advancements with chatGPT and GBD 3.5 releases.
5. The frontier AI intelligence landscape includes models like O3, 04 mini with reasoning mode high, Deepseek R1, Rock 3 mini reasoning high, Gemini 2.5 Pro, Claude 4 opus thinking, with benchmarks ba
6. George emphasizes that there is more than one frontier in AI and explores reasoning models, open weights frontier, cost frontier, and speed frontier.
7. Reasoning models offer greater intelligence but require more output tokens, which can lead to trade-offs in request latency and cost.
8. Artificial Analysis found an order of magnitude difference in output tokens between reasoning and non-reasoning models (e.g., GPT 4.1 requiring 7M tokens vs. Gemini 2.5 Pro's 130M tokens).
9. Reasoning models also exhibit longer latency; for example, GBD 4.1 took a median of 4.7 seconds compared to 04 mini high's 40+ seconds.
10. Longer latency has implications for applications and users requiring responsiveness, such as enterprise chatbots or agent-based systems with multiple queries in succession.
11. Around the time of GPT4 release, there was a significant gap between open weights intelligence and proprietary models; however, recent releases like Mixture Late Time 7 and LM45B have narrowed tha
12. China-based AI labs contribute significantly to leading open weights models in reasoning and non-reasoning categories (e.g., DeepSeek's R1 and Alibaba's Quen 3 series).
13. The cost frontier is crucial, as it impacts application building; for example, 03 cost Artificial Analysis $2,000 to run their intelligence index, while 4.1 nano was over 500 times cheaper.
14. Users pay not only for the cost per token but also for verbose reasoning tokens during inference time, which can significantly increase costs.
15. The cost of accessing GPT4-level intelligence has decreased by over 100x since mid-2023 across all quality bands.
16. The speed frontier (output tokens per second) has increased dramatically since early 2023, with more intelligent models becoming available at faster speeds.
17. Hardware improvements like H100's faster processing than A100 and specialized accelerators have contributed to increased system output throughput on chips.
18. Artificial Analysis believes that demand for compute will continue to increase due to larger models, reasoning models requiring more compute, and agent-based systems with multiple sequential reque
19. The company's house view suggests that compute demands will grow as a result of insatiable intelligence demands, increasingly complex reasoning models, and agents requiring more sequential request
20. Artificial Analysis encourages developers to consider what if cost wasn't a barrier when building applications, as cost structures may change over time.

Source: AI Engineer via YouTube

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