The Widening Gap in LLM Experience
Andrej Karpathy recently illustrated a widening divide in how people perceive large language models using a four-tier funnel. At the broad base sit non-users and casual users who interact with free, often quantized or older model versions. Above them are power users who pay for premium tiers like ChatGPT Plus or Claude Pro, accessing more capable default models with larger context windows. The next tier comprises developers and researchers building on frontier-grade LLMs via API, where they control parameters, tool use, and system prompts. At the narrow apex sit internal researchers at labs like OpenAI, Google DeepMind, and Anthropic, who work with unreleased models, next-generation architectures, and compute clusters far beyond public reach.
This stratification creates fundamentally different reference points for every conversation about AI progress. A user complaining about hallucinations on a free tier is experiencing a different technological reality than an engineer chaining reasoning traces via API, who in turn sees different capabilities than a researcher evaluating a model that will not ship for another year. The resulting discourse often talks past itself, with each tier extrapolating the trajectory of the entire field from its own narrow vantage point.
Understanding this stratification sets the stage for examining how the most advanced models are actually built.
How Frontier Labs Train LLMs
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. Itโs now less โtwo groups of people speaking past each otherโ and more a sharp funnel.
โ Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all.
โ Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract.
โ Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binariesโฆ This has all happened very quickly and recently โ less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code.
โ And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the โarcheologyโ of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while.The funnel is driven by a combination of factors:
1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel.
2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel.
3. Access. Free-tier, paid-tier, internal.So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
โ Andrej Karpathy (@karpathy) October 11, 2026
With the user landscape in mind, we now turn to the engineering practices that power the models at the apex of this funnel.
Frontier labs converge on a stepwise, reward-based pipeline that moves from supervised fine-tuning through multiple reinforcement learning stages. The process typically begins with high-quality human demonstrations, followed by reward-model training on ranked model outputs, and then policy optimization using algorithms such as PPO or its newer variants. Recent research highlights a shift toward rubric-based self-critique, where models generate and score their own reasoning traces against explicit criteria, and verifiable rewards, which ground reinforcement signals in executable code outcomes or mathematical proofs rather than subjective preferences. This methodology reduces reliance on costly human annotation while improving reliability on complex reasoning tasks, as detailed in a technical overview of how frontier labs train LLMs.
The training stack increasingly separates verifiable domainsโsuch as coding and mathematicsโfrom open-ended generation. In verifiable domains, unit tests and formal checkers provide dense, noise-free reward signals that enable aggressive optimization. For subjective tasks, labs employ constitutional AI principles and calibrated rubrics to align model critiques with human intent. This dual-track approach allows frontier-grade LLMs to scale reasoning capabilities without collapsing into reward hacking or sycophancy, maintaining a tighter coupling between measured performance and genuine utility.
These training choices directly influence how the models perform on the benchmarks that define the current frontier.
Benchmarking Frontier-Grade LLMs
Having outlined the training pipelines, we can now assess how the resulting models compare on public leaderboards.
Public leaderboards now track intelligence, coding, and agentic capabilities across dozens of models, revealing a compressed top tier where proprietary and open-weight systems trade blows. The Opper LLM Leaderboard shows OpenAIโs o1 and o3-mini leading on complex reasoning and tool-use benchmarks, while Anthropicโs Claude 3.5 Sonnet maintains an edge on coding tasks like SWE-bench Verified. Googleโs Gemini 2.0 Flash Thinking and xAIโs Grok 3 round out the frontier cluster, with scores separated by single-digit percentage points on MMLU-Pro, GPQA, and LiveCodeBench.
Open-weight models have closed the gap aggressively. Alibabaโs Qwen2.5-Max matches GPT-4o on several reasoning and coding evaluations, and its 72B variant outperforms Llama 3.3 70B across math and agentic benchmarks while remaining commercially permissive. A detailed breakdown of Qwen2.5โs performance against ChatGPT-4 highlights its strength on Chinese-language tasks and structured output generation, areas where Western models have historically lagged. DeepSeek-V3 and Nemotron 3 Ultra further demonstrate that the performance ceiling for openly available weights continues to rise.
Agentic benchmarksโmeasuring multi-step tool use, browser navigation, and code executionโexpose wider variance than static reasoning tests. Models that excel at single-turn coding often fail at maintaining context across extended tool chains, a gap that reinforcement learning on verifiable environments is beginning to address. The current frontier separates not by knowledge retrieval but by reliability in orchestrating actions toward a goal, a capability that correlates strongly with the rubric-based self-critique and verifiable reward training pipelines now standard at leading labs.
The benchmark results highlight not only raw performance but also the practical implications for developers and endโusers, which we summarize below.
Key Facts
- A four-tier funnel now structures the frontier model landscape: base pre-training, supervised fine-tuning, reward-model-guided reinforcement learning, and rubric-based self-critique with verifiable rewards.
- Training pipelines increasingly split verifiable domains (code, math) from open-ended generation, using executable outcomes to drive aggressive optimization without reward hacking.
- Benchmark leaderboards show a compressed top tier: OpenAIโs o1 and o3-mini lead on reasoning and tool use, Claude 3.5 Sonnet edges coding, while Gemini 2.0 Flash Thinking and Grok 3 sit within single-digit margins.
- Open-weight models have nearly closed the gapโQwen2.5-Max matches GPT-4o on reasoning and coding, and its 72B variant outperforms Llama 3.3 70B on math and agentic tasks under a permissive license.
- Agentic benchmarks reveal wider variance than static tests; multi-step tool orchestration now separates the frontier more than knowledge retrieval.
- An emerging access gap surfaced when OpenAI restricted SearchGPT access for Hugging Face researchers, highlighting tension between proprietary deployment and open evaluation of frontier-grade LLMs, as reported in coverage of the SearchGPT test phase.
Frequently Asked Questions
What are the key stages of a frontierโgrade LLM training pipeline and how do they differ from the pipelines used for consumerโgrade models?
A frontierโgrade pipeline typically starts with massive, highโquality curated datasets, followed by preโtraining on thousands of GPU/TPU years, then multiโphase fineโtuning that includes chainโofโthought prompting, toolโuse alignment, and extensive RLHF loops. In contrast, consumerโgrade pipelines use smaller datasets, fewer compute cycles, and a single fineโtuning stage to keep costs low. The extra stages give frontier models deeper reasoning, better tool integration, and higher reliability on complex tasks.
How do benchmark results for frontierโgrade LLMs compare with those of popular commercial models on math and coding evaluations?
Frontierโgrade LLMs routinely outperform models like ChatGPT Plus or Claude Pro on hard math benchmarks such as MATH and on coding suites like HumanEval, often achieving 10โ20% higher pass rates. They also score better on multiโstep reasoning tests that require tool use or external API calls. The gap is most pronounced in tasks that involve long context windows or complex code generation, where the larger architecture and training budget give a clear advantage.
What is the approximate cost of developing a frontierโgrade LLM and which factors contribute most to that expense?
Developing a frontierโgrade LLM can cost anywhere from $200โฏmillion to $500โฏmillion, driven primarily by the compute budget (hundreds of thousands of GPU/TPU hours) and the scale of the training data. Additional expenses include talent (researchers, engineers, safety experts), data licensing, and extensive evaluation and alignment infrastructure. These high fixed costs are why access is limited to a few labs and why the models are offered via restricted APIs.
Last Updated on October 12, 2026 7:13 am by Laszlo Szabo / NowadAIs | Published on October 12, 2026 by Laszlo Szabo / NowadAIs
