闭源 AI 模型主导价格性能前沿

闭源 AI 模型主导价格性能前沿
Closed AI models dominate the price-performance frontier

原始链接: https://village.dev/blog/closed-ai-models-now-dominate-the-price-performance-frontier/

Village 的 LLM Atlas 追踪每个价格点上最强大的 AI 模型。开源权重模型现在仅在 3% 的预算点上是最佳选择，低于 6 月的 41%，以及 2025 年 9 月近一半的峰值；剩余的多数胜利集中在最便宜端（Gemma 4 E4B、MiMo-V2.6-Flash）。GPT-6 Luna、GPT-6.1 Terra 和 Claude Haiku 5.5 夺回了开源模型曾赢得的预算点。开源模型倾向于在发布廉价强模型后（尤其是 DeepSeek）的突发性增长中获得份额，随后在闭源实验室发布更便宜的层级时失去份额。闭源实验室可能获胜是因为它们能从自己的前沿模型蒸馏、发布覆盖许多价格点的完整模型家族，以及大规模提供推理服务。Atlas 按发布日期比较各模型，在从 GPT-5 成本 1% 到 3 倍的 201 个预算点上进行对比，分数来自 2026 年 10 月 31 日；开源模型在自有硬件上可能更便宜，而扩大预算范围可将最便宜端的开源份额提高到 16%。

HN评论者就一篇声称闭源AI模型在价格性能前沿占主导地位的文章展开辩论。一些人认为通用基准具有误导性：在编程方面，像DeepSeek这样的开源模型可能是帕累托最优的，而订阅/折扣使API价格比较更加复杂。另一些人反驳说，编程往往需要更广泛的领域知识，因此通用智能很重要，闭源前沿模型在复杂任务上仍然更强。用户讨论实际经济问题：本地推理、硬件成本、缓存TTL、订阅，以及闭源实验室是否补贴使用。一些人称赞廉价的Flash模型和多智能体设置；另一些人说闭源模型仍最适合高端工作。其他主题包括蒸馏、可持续性、隐私、定制，以及开源模型的可检查性和本地控制是否超越价格性能具有意义。总体共识是微妙的：闭源模型在前沿领先，但开源/更便宜的模型根据任务、工作流和价格结构可以极具竞争力。

Village’s LLM Atlas maps the price-performance frontier: the strongest AI model at every price point.

Closed models hold almost every budget

An open-weight model is the best buy at 3% of budgets. Nearly all of those are at the cheap end, held by Google’s Gemma 4 E4B and Xiaomi’s MiMo-V2.6-Flash.
In June, it was 41%. DeepSeek V4 Flash alone was the best buy across nearly a third of budgets, with Zhipu’s GLM-5.2 just above it.
Two OpenAI releases took most of that back. GPT-6 Luna in September and GPT-6.1 Terra in October now cover the budgets open models used to win, alongside Anthropic’s Claude Haiku 5.5.

Open models have led on value before

Today’s share is the lowest we have measured. At the end of every quarter since 2024, open models held at least 6% of budgets.
The peaks came from single releases. DeepSeek-V3.2-Exp was the best buy at nearly half of all budgets the week it launched in September 2025.
None of them lasted. After each peak, the open share fell by about half or more by the next quarter as closed labs shipped cheaper tiers.

Open models win on value in bursts: a strong, cheap release, usually from DeepSeek, takes a wide band of budgets until the next closed model undercuts it. The latest closed releases have been both cheaper and stronger than anything open, so the band has shrunk to the very bottom of the price range.

Why closed labs may be winning on value

Three explanations fit the pattern:

Closed labs can distill their own frontier models. A lab with the strongest model can train cheaper ones on its outputs in-house, and today’s cheap closed tiers followed their flagships within weeks, as Claude Haiku 5.5 did two weeks after Claude Opus 5.5. Open labs have to start from their own, weaker models, since closed labs’ terms generally bar training competitors on their outputs.
Closed labs release whole families. OpenAI’s GPT-6 and GPT-6.1 models, from Luna and Terra up to Sol and Astra, hold 11 of the frontier’s 21 points between them, so one generation covers nearly every price.
Closed labs may serve more cheaply. OpenAI, Anthropic and Google run inference at enormous scale, Google on its own chips, which can lower what each task costs; most open-model labs serve far less traffic.

How we built this

This study uses Village’s LLM Atlas, which estimates a capability score for each model and reasoning setting from public benchmark results and, with a separate cost model, its cost per task from evaluators’ runs, described in its methodology. Costs are relative to GPT-5 at high effort. For each date, we take every model released by then and find the most capable one at each of 201 budgets spread evenly on a log scale from 1% to three times GPT-5’s cost. The scores are from October 31, 2026; Atlas updates as new results arrive.

Costs reflect what evaluators paid through hosted APIs, and open models can be cheaper to run on your own hardware. Restricting to models scored on at least 10 benchmark families, or narrowing the budgets to 3% to one times GPT-5’s cost, gives the same pattern; widening them to 0.3% to ten times raises today’s share to 16%, because tiny open models still win at the very cheapest end.