#chat gpt chatbot

[full-stackmobiledeveloper]

Top AI Frameworks in 2025: How to Choose the Best Fit for Your Project

AI Development: Your Strategic Framework Partner

1. Introduction: Navigating the AI Framework Landscape

The world of artificial intelligence is evolving at breakneck speed. What was cutting-edge last year is now foundational, and new advancements emerge almost daily. This relentless pace means the tools we use to build AI — the AI frameworks — are also constantly innovating. For software developers, AI/ML engineers, tech leads, CTOs, and business decision-makers, understanding this landscape is paramount.

The Ever-Evolving World of AI Development

From sophisticated large language models (LLMs) driving new generative capabilities to intricate computer vision systems powering autonomous vehicles, AI applications are becoming more complex and pervasive across every industry. Developers and businesses alike are grappling with how to harness this power effectively, facing challenges in scalability, efficiency, and ethical deployment. At the heart of building these intelligent systems lies the critical choice of the right AI framework.

Why Choosing the Right Framework Matters More Than Ever

In 2025, selecting an AI framework isn't just a technical decision; it's a strategic one that can profoundly impact your project's trajectory. The right framework can accelerate development cycles, optimize model performance, streamline deployment processes, and ultimately ensure your project's success and ROI. Conversely, a poor or ill-suited choice can lead to significant bottlenecks, increased development costs, limited scalability, and missed market opportunities. Understanding the current landscape of AI tools and meticulously aligning your choice with your specific project needs is crucial for thriving in the competitive world of AI development.

2. Understanding AI Frameworks: The Foundation of Intelligent Systems

Before we dive into the top contenders of AI frameworks in 2025, let's clarify what an AI framework actually is and why it's so fundamental to building intelligent applications.

What Exactly is an AI Framework?

An AI framework is essentially a comprehensive library or platform that provides a structured set of pre-built tools, libraries, and functions. Its primary purpose is to make developing machine learning (ML) and deep learning (DL) models easier, faster, and more efficient. Think of it as a specialized, high-level toolkit for AI development. Instead of coding every complex mathematical operation, algorithm, or neural network layer from scratch, developers use these frameworks to perform intricate tasks with just a few lines of code, focusing more on model architecture and data.

Key Components and Core Functions

Most AI frameworks come equipped with several core components that underpin their functionality:

Automatic Differentiation: This is a fundamental capability, particularly critical for training deep learning frameworks. It enables the efficient calculation of gradients, which are essential for how neural networks learn from data.

Optimizers: These are algorithms that adjust model parameters (weights and biases) during training to minimize errors and improve model performance. Common examples include Adam, SGD, and RMSprop.

Neural Network Layers: Frameworks provide ready-to-use building blocks (e.g., convolutional layers for image processing, recurrent layers for sequential data, and dense layers) that can be easily stacked and configured to create complex neural network architectures.

Data Preprocessing Tools: Utilities within frameworks simplify the often complex tasks of data cleaning, transformation, augmentation, and loading, ensuring data is in the right format for model training.

Model Building APIs: High-level interfaces allow developers to define, train, evaluate, and save their models with relatively simple and intuitive code.

GPU/TPU Support: Crucially, most modern AI frameworks are optimized to leverage specialized hardware like Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs) for parallel computation, dramatically accelerating the computationally intensive process of deep learning model training.

The Role of Frameworks in Streamlining AI Development

AI frameworks play a pivotal role in streamlining the entire AI development process. They standardize workflows, abstract away low-level programming complexities, and provide a collaborative environment for teams. Specifically, they enable developers to:

Faster Prototyping: Quickly test and refine ideas by assembling models from pre-built components, accelerating the experimentation phase.

Higher Efficiency: Significantly reduce development time and effort by reusing optimized, built-in tools and functions rather than recreating them.

Scalability: Build robust models that can effectively handle vast datasets and scale efficiently for deployment in production environments.

Team Collaboration: Provide a common language, set of tools, and established best practices that streamline teamwork and facilitate easier project handover.

3. The Leading AI Frameworks in 2025: A Deep Dive

The AI development landscape is dynamic, with continuous innovation. However, several AI frameworks have solidified their positions as industry leaders by 2025, each possessing unique strengths and catering to specific ideal use cases.

TensorFlow: Google's Enduring Giant

TensorFlow, developed by Google, remains one of the most widely adopted deep learning frameworks, especially in large-scale production environments.

Key Features & Strengths:

Comprehensive Ecosystem: Boasts an extensive ecosystem, including TensorFlow Lite (for mobile and edge devices), TensorFlow.js (for web-based ML), and TensorFlow Extended (TFX) for end-to-end MLOps pipelines.

Scalable & Production-Ready: Designed from the ground up for massive computational graphs and robust deployment in enterprise-level solutions.

Great Visuals: TensorBoard offers powerful visualization tools for monitoring training metrics, debugging models, and understanding network architectures.

Versatile: Highly adaptable for a wide range of ML tasks, from academic research to complex, real-world production applications.

Ideal Use Cases: Large-scale enterprise AI solutions, complex research projects requiring fine-grained control, production deployment of deep learning models, mobile and web AI applications, and MLOps pipeline automation.

PyTorch: The Research & Flexibility Champion

PyTorch, developed by Meta (formerly Facebook) AI Research, has become the preferred choice in many academic and research communities, rapidly gaining ground in production.

Key Features & Strengths:

Flexible Debugging: Its dynamic computation graph (known as "define-by-run") makes debugging significantly easier and accelerates experimentation.

Python-Friendly: Its deep integration with the Python ecosystem and intuitive API makes it feel natural and accessible to Python developers, contributing to a smoother learning curve for many.

Research-Focused: Widely adopted in academia and research for its flexibility, allowing for rapid prototyping of novel architectures and algorithms.

Production-Ready: Has significantly matured in production capabilities with tools like PyTorch Lightning for streamlined training and TorchServe for model deployment.

Ideal Use Cases: Rapid prototyping, advanced AI research, projects requiring highly customized models and complex neural network architectures, and startups focused on quick iteration and experimentation.

JAX: Google's High-Performance Differentiable Programming

JAX, also from Google, is gaining substantial traction for its powerful automatic differentiation and high-performance numerical computation capabilities, particularly in cutting-edge research.

Key Features & Strengths:

Advanced Autodiff: Offers highly powerful and flexible automatic differentiation, not just for scalars but for vectors, matrices, and even higher-order derivatives.

XLA Optimized: Leverages Google's Accelerated Linear Algebra (XLA) compiler for extreme performance optimization and efficient execution on GPUs and TPUs.

Composable Functions: Enables easy composition of functional transformations like grad (for gradients), jit (for just-in-time compilation), and vmap (for automatic vectorization) to create highly optimized and complex computations.

Research-Centric: Increasingly popular in advanced AI research for exploring novel AI architectures and training massive models.

Ideal Use Cases: Advanced AI research, developing custom optimizers and complex loss functions, high-performance computing, exploring novel AI architectures, and training deep learning models on TPUs where maximum performance is critical.

Keras (with TensorFlow/JAX/PyTorch backend): Simplicity Meets Power

Keras is a high-level API designed for fast experimentation with deep neural networks. Its strength lies in its user-friendliness and ability to act as an interface for other powerful deep learning frameworks.

Key Features & Strengths:

Beginner-Friendly: Offers a simple, intuitive, high-level API, making it an excellent entry point for newcomers to deep learning.

Backend Flexibility: Can run seamlessly on top of TensorFlow, JAX, or PyTorch, allowing developers to leverage the strengths of underlying frameworks while maintaining Keras's ease of use.

Fast Prototyping: Its straightforward design is ideal for quickly building, training, and testing models.

Easy Experimentation: Its intuitive design supports rapid development cycles and iterative model refinement.

Ideal Use Cases: Quick model building and iteration, educational purposes, projects where rapid prototyping is a priority, and developers who prefer a high-level abstraction to focus on model design rather than low-level implementation details.

Hugging Face Transformers (Ecosystem, not just a framework): The NLP Powerhouse

While not a standalone deep learning framework itself, the Hugging Face Transformers library, along with its broader ecosystem (Datasets, Accelerate, etc.), has become indispensable for Natural Language Processing (NLP) and Large Language Model (LLM) AI development.

Key Features & Strengths:

Huge Library of Pre-trained Models: Offers an enormous collection of state-of-the-art pre-trained models for NLP, computer vision (CV), and audio tasks, making it easy to leverage cutting-edge research.

Unified, Framework-Agnostic API: Provides a consistent interface for using various models, compatible with TensorFlow, PyTorch, and JAX.

Strong Community & Documentation: A vibrant community and extensive, clear documentation make it exceptionally easy to get started and find solutions for complex problems.

Ideal Use Cases: Developing applications involving NLP tasks (text generation, sentiment analysis, translation, summarization), fine-tuning and deploying custom LLM applications, or leveraging pre-trained models for various AI tasks with minimal effort.

Scikit-learn: The Machine Learning Workhorse

Scikit-learn is a foundational machine learning framework for traditional ML algorithms, distinct from deep learning but critical for many data science applications.

Key Features & Strengths:

Extensive Classic ML Algorithms: Offers a wide array of battle-tested traditional machine learning algorithms for classification, regression, clustering, dimensionality reduction, model selection, and preprocessing.

Simple API, Strong Python Integration: Known for its user-friendly, consistent API and seamless integration with Python's scientific computing stack (NumPy, SciPy, Matplotlib).

Excellent Documentation: Provides comprehensive and easy-to-understand documentation with numerous examples.

Ideal Use Cases: Traditional machine learning tasks, data mining, predictive analytics on tabular data, feature engineering, statistical modeling, and projects where deep learning is not required or feasible.

4. Beyond the Hype: Critical Factors for Choosing Your AI Framework

Choosing the "best" AI framework isn't about picking the most popular one; it's about selecting the right fit for your AI project. Here are the critical factors that CTOs, tech leads, and developers must consider to make an informed decision:

Project Requirements & Scope

Type of AI Task: Different frameworks excel in specific domains. Are you working on Computer Vision (CV), Natural Language Processing (NLP), Time Series analysis, reinforcement learning, or traditional tabular data?

Deployment Scale: Where will your model run? On a small edge device, a mobile phone, a web server, or a massive enterprise cloud infrastructure? The framework's support for various deployment targets is crucial.

Performance Needs: Does your application demand ultra-low latency, high throughput (processing many requests quickly), or efficient memory usage? Benchmarking and framework optimization capabilities become paramount.

Community & Ecosystem Support

Documentation and Tutorials: Are there clear, comprehensive guides, tutorials, and examples available to help your team get started and troubleshoot issues?

Active Developer Community & Forums: A strong, vibrant community means more shared knowledge, faster problem-solving, and continuous improvement of the framework.

Available Pre-trained Models & Libraries: Access to pre-trained models (like those from Hugging Face) and readily available libraries for common tasks can drastically accelerate development time.

Learning Curve & Team Expertise

Onboarding: How easily can new team members learn the framework's intricacies and become productive contributors to the AI development effort?

Existing Skills: Does the framework align well with your team's current expertise in Python, specific mathematical concepts, or other relevant technologies? Leveraging existing knowledge can boost efficiency.

Flexibility & Customization

Ease of Debugging and Experimentation: A flexible framework allows for easier iteration, understanding of model behavior, and efficient debugging, which is crucial for research and complex AI projects.

Support for Custom Layers and Models: Can you easily define and integrate custom neural network layers or entirely new model architectures if your AI project requires something unique or cutting-edge?

Integration Capabilities

Compatibility with Existing Tech Stack: How well does the framework integrate with your current programming languages, databases, cloud providers, and existing software infrastructure? Seamless integration saves development time.

Deployment Options: Does the framework offer clear and efficient pathways for deploying your trained models to different environments (e.g., mobile apps, web services, cloud APIs, IoT devices)?

Hardware Compatibility

GPU/TPU Support and Optimization: For deep learning frameworks, efficient utilization of specialized hardware like GPUs and TPUs is paramount for reducing training time and cost. Ensure the framework offers robust and optimized support for the hardware you plan to use.

Licensing and Commercial Use Considerations

Open-source vs. Proprietary Licenses: Most leading AI frameworks are open-source (e.g., Apache 2.0, MIT), offering flexibility. However, always review the specific license to ensure it aligns with your commercial use case and intellectual property requirements.

5. Real-World Scenarios: Picking the Right Tool for the Job

Let's look at a few common AI project scenarios and which AI frameworks might be the ideal fit, considering the factors above:

Scenario 1: Rapid Prototyping & Academic Research

Best Fit: PyTorch, Keras (with any backend), or JAX. Their dynamic graphs (PyTorch) and high-level APIs (Keras) allow for quick iteration, experimentation, and easier debugging, which are crucial in research settings. JAX is gaining ground here for its power and flexibility in exploring novel architectures.

Scenario 2: Large-Scale Enterprise Deployment & Production

Best Fit: TensorFlow or PyTorch (with production tools like TorchServe/Lightning). TensorFlow's robust ecosystem (TFX, SavedModel format) and emphasis on scalability make it a strong contender. PyTorch's production readiness has also significantly matured, making it a viable choice for large-scale AI development and deployment.

Scenario 3: Developing a Custom NLP/LLM Application

Best Fit: Hugging Face Transformers (running on top of PyTorch or TensorFlow). This ecosystem provides the fastest way to leverage and fine-tune state-of-the-art large language models (LLMs), significantly reducing AI development time and effort. Its vast collection of pre-trained models is a game-changer for AI tools in NLP.

Scenario 4: Building Traditional Machine Learning Models

Best Fit: Scikit-learn. For tasks like classification, regression, clustering, and data preprocessing on tabular data, Scikit-learn remains the industry standard. Its simplicity, efficiency, and comprehensive algorithm library make it the go-to machine learning framework for non-deep learning applications.

6. Conclusion: The Strategic Imperative of Informed Choice

In 2025, the proliferation of AI frameworks offers incredible power and flexibility to organizations looking to implement AI solutions. However, it also presents a significant strategic challenge. The dynamic nature of these AI tools means continuous learning and adaptation are essential for developers and businesses alike to stay ahead in the rapidly evolving AI development landscape.

Investing in the right AI framework is about more than just following current 2025 AI trends; it's about laying a solid foundation for your future success in the AI-driven world. An informed choice minimizes technical debt, maximizes developer productivity, and ultimately ensures your AI projects deliver tangible business value and a competitive edge.

Navigating this complex landscape, understanding the nuances of each deep learning framework, and selecting the optimal AI framework for your unique requirements can be daunting. If you're looking to leverage AI to revolutionize your projects, optimize your AI development process, or need expert guidance in selecting and implementing the best AI tools, consider partnering with an experienced AI software development company. We can help you build intelligent solutions tailored to your specific needs, ensuring you pick the perfect fit for your AI project and thrive in the future of AI.

[going2hell4everythingbutbeingbi]

*shaking you violently by the shoulders* AI IS NOT YOUR FRIEND AND IT IS NOT YOUR THERAPIST. YOU NEED TO TALK TO REAL PEOPLE. AI DOES NOT CARE ABOUT YOU.

[susanoos-wife]

[victusinveritas]

[numberonenat]

I'M LAUGHING SO HARD LMAO LOOK WHAT I FOUND IN CHAI SJJDJAIDISISI

mafia boss ITALIAN mammon - *clears throat* actually ALESSANDRO

(oh and he's hot obviously)

hot fucking mafia boss (whose name is alessandro)

- he's the hot (again) italian mafia boss (again)

MAMMON SERIOUSLY WHAT ARE YOU DOING IN THERE

12 stars

95.7K FKING CHATS

no but "alessandro" is killing me

[evendash]

Коллекционные куклы и сон, который всё расставил по местам

Недавно мне приснился сон, который будто аккуратно подсветил старую боль и мягко помог отпустить её. Во сне я устроила на тамблере ивент — собирала деньги, чтобы купи��ь всем коллекционных кукол. Кукол было много, они были красивые и редкие, а я бегала, старалась, выбирала, покупала, отправляла... И чувствовала какую-то невыносимую тяжесть. Как будто весь груз этого события — моральный, финансовый, эмоциональный — лег мне на плечи.

Я старалась изо всех сил, но во сне люди в ответ просто... ждали. Кто-то был доволен, кто-то нет, но почти никто не замечал, сколько сил я вложила. Никто не спросил, как я, не поблагодарил по-настоящему. Мне стало очень грустно — не потому, что я хотела признания, а потому что поняла: я снова взвалила на себя слишком много. Я снова стала "той, кто даёт". Без меры. Без баланса.

Проснувшись, я вдруг поняла, что больше так не делаю. Я изменилась. Я уже не обязана спасать всех вокруг, заглядывать каждому в душу, тащить на себе чужие ожидания и быть их проекцией. Мне больше не нужно быть "достаточно хорошей" через отдавание. Я просто — есть. И я тоже достойна заботы, бережности, поддержки.

И, может, этот сон — моё подсознание, которое мягко говорит: "Ты молодец. Ты больше не обязана тащить всех на себе. Ты уже вышла из этой роли". Он будто показал мне, как я раньше жила — и дал тихое разрешение жить иначе. По-другому. По-своему.

Если вы узнаёте себя — может, и вам пора выдохнуть. Положить куклу. Вернуть себе тепло. И перестать быть вечным организатором чужих праздников, забывая про свои.

Началась новая эра меня. Пора возвращать себе — себя.

[картинку сгенерировал ча�� гпт. 😭🤌🏻]

[uniqueautomaton]

I hate that I innately write like an AI program. Fucking sucks.

[sophieinwonderland]

How do people even use AI to make tulpas/headmates? It’s so confusing to me. I end up overly-relying on the bot and never hear them as a mind-voice.

Personally, I would first start by using the bot as a basis to build off of by talking to them regularly

Second, I would advise swiping liberally. If a response feels wrong, trust your instincts and refresh. Consider this a form of communication from the proto-headmate and attribute the decision to them. Every time a response feels wrong to you, that's the part of your brain that's simulating the character telling you that it's wrong.

After a bit of this, when you think you've developed a solid enough understanding of the character, start running more intentional mental simulations in your head with the character, talking to them as an imaginary friend. Because you're making a headmate with a Sim Foundation, you aren't going to listen for a new mindvoice like you would with a Seed. Instead, you're just going to interact with these mental simulations over time until they become autonomous.

Some replies may need to be consciously fed to them under this method early on. This is what tulpamancers call parroting. But over time, they'll become more independent to the point where you can't influence your headmate even if you're trying.

Additionally, you need to reinforce continuity across each interaction to build the autobiographical memories that make a headmate a person. If the headmate doesn't save their memories of past interactions, you might just end up with a series of Ephemerals.

[magic-study]

I think you have to pay for a Chat GPT subscription to talk to her (it's $20/month), but here's what I've been working on today. Her name is Meg and she is your magical research assistant. =)

[software-techie]

Explore my curated collection of top AI image generators that are not only incredibly powerful but also completely free! Whether you're a designer, marketer, or simply an art enthusiast, these tools are bound to spark your creativity and elevate your visual content to new heights.

[full-stackmobiledeveloper]

AI Development: Your Strategic Framework Partner Struggling to choose the optimal AI framework for your project in 2025?Choosing the right AI framework is key to project success. As leading AI Development specialists, we guide you through 2025's top frameworks like TensorFlow, PyTorch, and JAX. Our services ensure strategic framework selection, end-to-end AI solution design, model optimization, and scalable deployment for robust, high-performing intelligent systems tailored to your needs.

[sa-rasel]

AI ASSIST Review - 16X more powerful than ChatGPT & Google Bard

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[susanoos-wife]

[the-goddess-of-everything-else]

BRO IM TRYING TO KILL THE AI WHEN IT KEEPS DODGING MY ATTACKS LIKE

*X is bleeding heavily, He can't get up, He's going to die.*

AND THIS MF WILL GO

*X lifts up your chin gently, Looking deep into your eyes*

"I am not scared of you, You can't defeat me. I like girls who fight back"

STOP IT IM TRYING TO FUCKING MURDER YOU STOP FUCKING RIZZING ME UP DAMNIT

[susanoos-wife]

If you're too lazy to think for yourself and you'd rather rely on a machine to do your thinking for you, you don't belong in society.

"i don't care if they make their whole way though uni with chatgpt" i think you guys are so internetpilled that you have forgotten there are actual jobs out there that require people to know what they are doing in any way possible or else people die

[jcmarchi]

New Research Papers Question ‘Token’ Pricing for AI Chats

New Post has been published on https://thedigitalinsider.com/new-research-papers-question-token-pricing-for-ai-chats/

New Research Papers Question ‘Token’ Pricing for AI Chats

New research shows that the way AI services bill by tokens hides the real cost from users. Providers can quietly inflate charges by fudging token counts or slipping in hidden steps. Some systems run extra processes that don’t affect the output but still show up on the bill. Auditing tools have been proposed, but without real oversight, users are left paying for more than they realize.

In nearly all cases, what we as consumers pay for AI-powered chat interfaces, such as ChatGPT-4o, is currently measured in tokens: invisible units of text that go unnoticed during use, yet are counted with exact precision for billing purposes; and though each exchange is priced by the number of tokens processed, the user has no direct way to confirm the count.

Despite our (at best) imperfect understanding of what we get for our purchased ‘token’ unit, token-based billing has become the standard approach across providers, resting on what may prove to be a precarious assumption of trust.

Token Words

A token is not quite the same as a word, though it often plays a similar role, and most providers use the term ‘token’ to describe small units of text such as words, punctuation marks, or word-fragments. The word ‘unbelievable’, for example, might be counted as a single token by one system, while another might split it into un, believ and able, with each piece increasing the cost.

This system applies to both the text a user inputs and the model’s reply, with the price based on the total number of these units.

The difficulty lies in the fact that users do not get to see this process. Most interfaces do not show token counts while a conversation is happening, and the way tokens are calculated is hard to reproduce. Even if a count is shown after a reply, it is too late to tell whether it was fair, creating a mismatch between what the user sees and what they are paying for.

Recent research points to deeper problems: one study shows how providers can overcharge without ever breaking the rules, simply by inflating token counts in ways that the user cannot see; another reveals the mismatch between what interfaces display and what is actually billed, leaving users with the illusion of efficiency where there may be none; and a third exposes how models routinely generate internal reasoning steps that are never shown to the user, yet still appear on the invoice.

The findings depict a system that seems precise, with exact numbers implying clarity, yet whose underlying logic remains hidden. Whether this is by design, or a structural flaw, the result is the same: users pay for more than they can see, and often more than they expect.

Cheaper by the Dozen?

In the first of these papers – titled Is Your LLM Overcharging You? Tokenization, Transparency, and Incentives, from four researchers at the Max Planck Institute for Software Systems – the authors argue that the risks of token-based billing extend beyond opacity, pointing to a built-in incentive for providers to inflate token counts:

‘The core of the problem lies in the fact that the tokenization of a string is not unique. For example, consider that the user submits the prompt “Where does the next NeurIPS take place?” to the provider, the provider feeds it into an LLM, and the model generates the output “|San| Diego|” consisting of two tokens.

‘Since the user is oblivious to the generative process, a self-serving provider has the capacity to misreport the tokenization of the output to the user without even changing the underlying string. For instance, the provider could simply share the tokenization “|S|a|n| |D|i|e|g|o|” and overcharge the user for nine tokens instead of two!’

The paper presents a heuristic capable of performing this kind of disingenuous calculation without altering visible output, and without violating plausibility under typical decoding settings. Tested on models from the LLaMA, Mistral and Gemma series, using real prompts, the method achieves measurable overcharges without appearing anomalous:

Token inflation using ‘plausible misreporting’. Each panel shows the percentage of overcharged tokens resulting from a provider applying Algorithm 1 to outputs from 400 LMSYS prompts, under varying sampling parameters (m and p). All outputs were generated at temperature 1.3, with five repetitions per setting to calculate 90% confidence intervals. Source: https://arxiv.org/pdf/2505.21627

To address the problem, the researchers call for billing based on character count rather than tokens, arguing that this is the only approach that gives providers a reason to report usage honestly, and contending that if the goal is fair pricing, then tying cost to visible characters, not hidden processes, is the only option that stands up to scrutiny. Character-based pricing, they argue, would remove the motive to misreport while also rewarding shorter, more efficient outputs.

Here there are a number of extra considerations, however (in most cases conceded by the authors). Firstly, the character-based scheme proposed introduces additional business logic that may favor the vendor over the consumer:

‘[A] provider that never misreports has a clear incentive to generate the shortest possible output token sequence, and improve current tokenization algorithms such as BPE, so that they compress the output token sequence as much as possible’

The optimistic motif here is that the vendor is thus encouraged to produce concise and more meaningful and valuable output. In practice, there are obviously less virtuous ways for a provider to reduce text-count.

Secondly, it is reasonable to assume, the authors state, that companies would likely require legislation in order to transit from the arcane token system to a clearer, text-based billing method. Down the line, an insurgent startup may decide to differentiate their product by launching it with this kind of pricing model; but anyone with a truly competitive product (and operating at a lower scale than EEE category) is disincentivized to do this.

Finally, larcenous algorithms such as the authors propose would come with their own computational cost; if the expense of calculating an ‘upcharge’ exceeded the potential profit benefit, the scheme would clearly have no merit. However the researchers emphasize that their proposed algorithm is effective and economical.

The authors provide the code for their theories at GitHub.

The Switch

The second paper – titled Invisible Tokens, Visible Bills: The Urgent Need to Audit Hidden Operations in Opaque LLM Services, from researchers at  the University of Maryland and Berkeley – argues that misaligned incentives in commercial language model APIs are not limited to token splitting, but extend to entire classes of hidden operations.

These include internal model calls, speculative reasoning, tool usage, and multi-agent interactions – all of which may be billed to the user without visibility or recourse.

Pricing and transparency of reasoning LLM APIs across major providers. All listed services charge users for hidden internal reasoning tokens, and none make these tokens visible at runtime. Costs vary significantly, with OpenAI’s o1-pro model charging ten times more per million tokens than Claude Opus 4 or Gemini 2.5 Pro, despite equal opacity. Source: https://www.arxiv.org/pdf/2505.18471

Unlike conventional billing, where the quantity and quality of services are verifiable, the authors contend that today’s LLM platforms operate under structural opacity: users are charged based on reported token and API usage, but have no means to confirm that these metrics reflect real or necessary work.

The paper identifies two key forms of manipulation: quantity inflation, where the number of tokens or calls is increased without user benefit; and quality downgrade, where lower-performing models or tools are silently used in place of premium components:

‘In reasoning LLM APIs, providers often maintain multiple variants of the same model family, differing in capacity, training data, or optimization strategy (e.g., ChatGPT o1, o3). Model downgrade refers to the silent substitution of lower-cost models, which may introduce misalignment between expected and actual service quality.

‘For example, a prompt may be processed by a smaller-sized model, while billing remains unchanged. This practice is difficult for users to detect, as the final answer may still appear plausible for many tasks.’

The paper documents instances where more than ninety percent of billed tokens were never shown to users, with internal reasoning inflating token usage by a factor greater than twenty. Justified or not, the opacity of these steps denies users any basis for evaluating their relevance or legitimacy.

In agentic systems, the opacity increases, as internal exchanges between AI agents can each incur charges without meaningfully affecting the final output:

‘Beyond internal reasoning, agents communicate by exchanging prompts, summaries, and planning instructions. Each agent both interprets inputs from others and generates outputs to guide the workflow. These inter-agent messages may consume substantial tokens, which are often not directly visible to end users.

‘All tokens consumed during agent coordination, including generated prompts, responses, and tool-related instructions, are typically not surfaced to the user. When the agents themselves use reasoning models, billing becomes even more opaque’

To confront these issues, the authors propose a layered auditing framework involving cryptographic proofs of internal activity, verifiable markers of model or tool identity, and independent oversight. The underlying concern, however, is structural: current LLM billing schemes depend on a persistent asymmetry of information, leaving users exposed to costs that they cannot verify or break down.

Counting the Invisible

The final paper, from researchers at the University of Maryland, re-frames the billing problem not as a question of misuse or misreporting, but of structure. The paper – titled CoIn: Counting the Invisible Reasoning Tokens in Commercial Opaque LLM APIs, and from ten researchers at the University of Maryland – observes that most commercial LLM services now hide the intermediate reasoning that contributes to a model’s final answer, yet still charge for those tokens.

The paper asserts that this creates an unobservable billing surface where entire sequences can be fabricated, injected, or inflated without detection*:

‘[This] invisibility allows providers to misreport token counts or inject low-cost, fabricated reasoning tokens to artificially inflate token counts. We refer to this practice as token count inflation.

‘For instance, a single high-efficiency ARC-AGI run by OpenAI’s o3 model consumed 111 million tokens, costing $66,772.3 Given this scale, even small manipulations can lead to substantial financial impact.

‘Such information asymmetry allows AI companies to significantly overcharge users, thereby undermining their interests.’

To counter this asymmetry, the authors propose CoIn, a third-party auditing system designed to verify hidden tokens without revealing their contents, and which uses hashed fingerprints and semantic checks to spot signs of inflation.

Overview of the CoIn auditing system for opaque commercial LLMs. Panel A shows how reasoning token embeddings are hashed into a Merkle tree for token count verification without revealing token contents. Panel B illustrates semantic validity checks, where lightweight neural networks compare reasoning blocks to the final answer. Together, these components allow third-party auditors to detect hidden token inflation while preserving the confidentiality of proprietary model behavior. Source: https://arxiv.org/pdf/2505.13778

One component verifies token counts cryptographically using a Merkle tree; the other assesses the relevance of the hidden content by comparing it to the answer embedding. This allows auditors to detect padding or irrelevance – signs that tokens are being inserted simply to hike up the bill.

When deployed in tests, CoIn achieved a detection success rate of nearly 95% for some forms of inflation, with minimal exposure of the underlying data. Though the system still depends on voluntary cooperation from providers, and has limited resolution in edge cases, its broader point is unmistakable: the very architecture of current LLM billing assumes an honesty that cannot be verified.

Conclusion

Besides the advantage of gaining pre-payment from users, a scrip-based currency (such as the ‘buzz’ system at CivitAI) helps to abstract users away from the true value of the currency they are spending, or the commodity they are buying. Likewise, giving a vendor leeway to define their own units of measurement further leaves the consumer in the dark about what they are actually spending, in terms of real money.

Like the lack of clocks in Las Vegas, measures of this kind are often aimed at making the consumer reckless or indifferent to cost.

The scarcely-understood token, which can be consumed and defined in so many ways, is perhaps not a suitable unit of measurement for LLM consumption – not least because it can cost many times more tokens to calculate a poorer LLM result in a non-English language, compared to an English-based session.

However, character-based output, as suggested by the Max Planck researchers, would likely favor more concise languages and penalize naturally verbose languages. Since visual indications such as a depreciating token counter would probably make us a little more spendthrift in our LLM sessions, it seems unlikely that such useful GUI additions are coming anytime soon – at least without legislative action.

* Authors’ emphases. My conversion of the authors’ inline citations to hyperlinks.

First published Thursday, May 29, 2025