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Closed Models Dominate Price-Performance Frontier as Open-Weight Share Falls to 3%

Original title: Closed AI models dominate the price-performance frontier
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Village's LLM Atlas, which maps the strongest model at every price point, shows that by October 31, 2026 only 5 of the 21 models on the price-performance frontier were open-weight, all confined to the cheap end. The share of budgets where an open model is the best buy collapsed from 41% in June to 3%, as closed vendors established both capability and cost advantages across nearly every budget tier.

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

Closed models hold almost every budget

Two scatter plots of LLM Atlas capability score against estimated cost per task, relative to GPT-5 at high effort, for every model released by June 30 and by October 31, 2026, with the price-performance frontier drawn through them. In June, 8 of the 19 points on the frontier were open-weight, including DeepSeek-V4-Flash, GLM-5.2, Gemma 4 E4B IT, Hy3 preview and Llama 3 8B Instruct, and an open model was the best buy at 41% of budgets from 1% to 3 times GPT-5’s cost. By October 31, 5 of 21 were, all in the cheaper half of the frontier (DeepSeek V4.1 Flash, Gemma 4 E4B IT, Llama 3 8B Instruct, MiMo-V2.6-Flash), and the share of budgets fell to 3%.

  • 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

The share of budgets, spread evenly on a log scale from 1% to 3 times GPT-5’s cost per task at high effort, where the most capable model costing no more than the budget is open-weight, at the end of each quarter from 2024 and on October 31, 2026: Q1 2024 6%; Q2 2024 42%; Q3 2024 19%; Q4 2024 32%; Q1 2025 7%; Q2 2025 7%; Q3 2025 52%; Q4 2025 30%; Q1 2026 26%; Q2 2026 41%; Q3 2026 20%; October 31, 2026 3%.

  • 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.

Next up

  • [A panel of diagonal hatching in five bands. In each band the lines start dense at the left and spread apart; each lower band opens up sooner, and the bottom band, in blue on a pale blue strip, is nearly empty by the right.

Frontier AI used to take over a year to get 10 times cheaper. Now it takes months.

A task that cost 79 cents with GPT-5 at its launch now costs about 2 cents with a newer model. And the more recently a model launched, the faster cheaper models are catching up to it.

Research · October 30, 2026](https://village.dev/blog/frontier-ai-used-to-take-over-a-year-to-get-10-times-cheaper-now-it-takes-months/)

  • [A field of small grey rings. A diagonal band of nested red rings sweeps across, and a band of nested blue rings follows close behind it.

In open-weight AI, America is the one playing catch-up

China’s best open-weight AI model has outscored America’s every day since September 2024. Even America’s best new open models typically arrive three months after a Chinese one reached the same level.

Research · October 30, 2026](https://village.dev/blog/in-open-weight-ai-america-is-the-one-playing-catch-up/)

  • [A stack of seven sheets climbing to the upper right, each hatched in a different direction. The top sheet is graphite; the ochre sheet just beneath it peeks out below.

AI models built outside the US and China are nearly a year behind

Since 2024, the best AI models from labs outside the US and China have arrived about 10 months after American ones reached the same level, nearly twice China’s lag. Mistral and South Korea are closing some of that gap; Cohere and the UAE have fallen further behind.

Research · October 30, 2026](https://village.dev/blog/outside-the-us-and-china-the-world-is-building-last-years-ai/)