Closed AI models now hold nearly every price point where they beat open ones
Closed AI models dominate the price-performance frontier
Village's LLM Atlas tracks the price-performance frontier, and the picture has flipped: open-weight models were the best buy across 41% of budgets in June 2026, but by October 31 that share had collapsed to 3%, the lowest since tracking began in 2024. Two OpenAI releases, GPT-6 Luna and GPT-6.1 Terra, plus Anthropic's Claude Haiku 5.5, reclaimed the budgets open models used to win. The authors point to in-house distillation, full model families, and cheaper inference at scale as likely reasons.
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.
- 2001zhaozhao
Are these comparisons based on API price? If so, then the OpenAI/Anthropic subscriptions (which are a 15-40X discount on model pricing) turn the comparison into a bloodbath, even more in favor of the closed source models.
- gregwebs
These general intelligence/capability benchmarks don't apply to your specific task. If you are coding, you want just benchmarks for coding. I don't see performance+price at this benchmark for coding. When you look at just coding, DeepSeek is Pareto optimal for a good portion of coding benchmarks.
For example here, although even this is very flawed because it only benchmarks max/xhigh effort: https://artificialanalysis.ai/agents/coding-agents
Closed models might be better generalists because they don't have that much constraint on size and come from well funded labs that can afford to better train for generalization. If a model's actual goal is to be open then smaller is better.
- ddxv
I think the biggest money unlock for most people is just to not use the largest models. Luna / Deepseek are a dollar or two a day for coding. Accidentally (or purposefully set a different model) and boom the cost shoots up. I think most people could slash their budgets by 90% just by choosing cheaper models most of the time.
- tobblegobble
I'm just going to paste my comment from another post since it is applicable here too, maybe even more so.
I've had really great results with cheap flash agents using a system that has a parent agent that spawns 2 sub agents. They remain active throughout the whole session and don't lose context.
The 2 sub agents get the exact same prompt and tools as each other and they independently answer but before responding back up to the parent they exchange their findings which must be backed up by a citation and then debate their findings and citations repeatedly until they can agree on correctness of all points. They are also heavily encouraged to never assume and always verify with the prompt and tools and guards where they can be realistically enforced.
It churns through tokens but tokens are crazy cheap with some of the plans out there. Could Opus one shot most of those same queries in like 1/4 time? Yep, but that's not the point for me.
DeepSeek Flash for parent then DeepSeek Flash and GLM 5.3 flash as the debating subagents gives best overall results in my experience.
For context I do equal parts sysadmin/devops and having them write software
- hedora
Without knowing which dots are which, this is useless.
Let’s say I want to run qwen 27B or flash next on a machine I own, off solar panels that are also sunk cost.
How did they price the marginal cost of a token? Depreciation on the box + reverse billing opportunity cost with the power company?