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Tomer Tunguz 博客(VC 分析)· Tomasz Tunguz·· 2026-06-03精选AI 评分66

智能性价比

Intelligence Per Dollar

AI 导读

微软在模型发布卡中首次加入平均token使用量指标。其模型在SWE-Bench Verified上达71.6分,仅消耗约Claude Haiku 4.5三分之一的token。Artificial Analysis的Intelligence Index显示GPT 5.5与Claude Opus 4.8得分相近(约60分),但Opus 4.8运行成本高出40%($4,685 vs $3,357)。Uber因四个月内AI预算超支而限制员工使用;Salesforce花费$3亿购买Anthropic tokens并冻结工程招聘。模型公司如今需同时在性能和成本两个维度竞争。

推荐理由

微软在模型发布卡上悄悄加了“平均token消耗”这个指标,这不是小改动,而是宣告AI从堆算力转向算账时代。Uber和Salesforce的预算教训已经很清楚了。

正文 · 原文

In short : Microsoft's MAI-Code-1-Flash achieves 60% fewer tokens than Claude Haiku 4.5 on hard coding tasks (SWE-Bench Verified), a +16-point lead on SWE-Bench Pro, and adaptive solution length control. The key insight: intelligence per dollar, value per token spent, is the right metric for evaluating AI models in production.

Screenshot 2026-06-02 at 9.22.43 PM

Yesterday Microsoft added a new metric to a model release card, one that will likely become a standard.1

Average token usage.

In the first row, the Microsoft model hits 71.6 on SWE-Bench Verified using about a third of the tokens Claude Haiku 4.5 burns.

Benchmarks are now measured on two different dimensions, the overall performance & the cost to achieve that intelligence.

This is yet another sign that the era of subsidies2, tokenmaxxing3, & all-out performance for many use cases is over.

Even the most valuable companies in the world cannot afford state-of-the-art intelligence for every conceivable use case.4 Uber capped employee AI spending after blowing through its budget in four months.5 Salesforce is spending $300M on Anthropic tokens & has frozen engineering hires.6

This new dual benchmark answers the buyer’s only question : what is my intelligence per dollar?

Screenshot 2026-06-03 at 5.49.00 AM

Artificial Analysis already benchmarks this.7 GPT 5.5 & Claude Opus 4.8 land within a point of each other on the Intelligence Index, around 60. Running the index costs $3,357 on GPT 5.5 & $4,685 on Opus 4.8. Same answer, 40% more expensive.

Model companies must now compete on both dimensions. The application layer will compete one level up, on dollars per outcome, what a closed ticket, a shipped PR, or a resolved support case actually costs.

Every layer in the stack now has to price the same way the customer thinks : per result, not per token.



  1. Introducing MAI-Code-1-Flash — Microsoft announces a new coding model with average token usage on the release card. ↩︎

  2. The Unsustainable Subsidy — The era of AI subsidies is ending. ↩︎

  3. Tokenmaxxing — Models that game benchmarks with extra tokens are losing their edge. ↩︎

  4. Microsoft cancels Claude Code licenses, shifting developers to GitHub Copilot CLI — Microsoft cancelled Claude Code licenses across its Experiences and Devices division (Windows, Microsoft 365, Outlook, Teams, Surface) after engineering usage outran budgets. ↩︎

  5. Uber caps employee AI spending after blowing through budget in 4 months — Uber caps employee AI spending after blowing through budget in four months. ↩︎

  6. Salesforce Spends $300M on AI, Freezes Engineering Hires — Salesforce Spends $300M on AI, Freezes Engineering Hires. ↩︎

  7. AI Model & API Providers Analysis — Independent analysis of AI model costs. ↩︎

来源:Tomer Tunguz 博客(VC 分析) · tomtunguz.com