Venture Beat

• Anthropic’s Claude Opus 4.5 is here: Cheaper AI, infinite chats, and coding skills that beat humans

  Anthropic released its most capable artificial intelligence model yet on Monday, slashing prices by roughly two-thirds while claiming state-of-the-art performance on software engineering tasks — a strategic move that intensifies the AI startup's competition with deep-pocketed rivals OpenAI and Google.

  The new model, Claude Opus 4.5, scored higher on Anthropic's most challenging internal engineering assessment than any human job candidate in the company's history, according to materials reviewed by VentureBeat. The result underscores both the rapidly advancing capabilities of AI systems and growing questions about how the technology will reshape white-collar professions.

  The Amazon-backed company is pricing Claude Opus 4.5 at $5 per million input tokens and $25 per million output tokens — a dramatic reduction from the $15 and $75 rates for its predecessor, Claude Opus 4.1, released earlier this year. The move makes frontier AI capabilities accessible to a broader swath of developers and enterprises while putting pressure on competitors to match both performance and pricing.

  "We want to make sure this really works for people who want to work with these models," said Alex Albert, Anthropic's head of developer relations, in an exclusive interview with VentureBeat. "That is really our focus: How can we enable Claude to be better at helping you do the things that you don't necessarily want to do in your job?"

  The announcement comes as Anthropic races to maintain its position in an increasingly crowded field. OpenAI recently released GPT-5.1 and a specialized coding model called Codex Max that can work autonomously for extended periods. Google unveiled Gemini 3 just last week, prompting concerns even from OpenAI about the search giant's progress, according to a recent report from The Information.

  Opus 4.5 demonstrates improved judgment on real-world tasks, developers say

  Anthropic's internal testing revealed what the company describes as a qualitative leap in Claude Opus 4.5's reasoning capabilities. The model achieved 80.9% accuracy on SWE-bench Verified, a benchmark measuring real-world software engineering tasks, outperforming OpenAI's GPT-5.1-Codex-Max (77.9%), Anthropic's own Sonnet 4.5 (77.2%), and Google's Gemini 3 Pro (76.2%), according to the company's data. The result marks a notable advance over OpenAI's current state-of-the-art model, which was released just five days earlier.

  But the technical benchmarks tell only part of the story. Albert said employee testers consistently reported that the model demonstrates improved judgment and intuition across diverse tasks — a shift he described as the model developing a sense of what matters in real-world contexts.

  "The model just kind of gets it," Albert said. "It just has developed this sort of intuition and judgment on a lot of real world things that feels qualitatively like a big jump up from past models."

  He pointed to his own workflow as an example. Previously, Albert said, he would ask AI models to gather information but hesitated to trust their synthesis or prioritization. With Opus 4.5, he's delegating more complete tasks, connecting it to Slack and internal documents to produce coherent summaries that match his priorities.

  Opus 4.5 outscores all human candidates on company's toughest engineering test

  The model's performance on Anthropic's internal engineering assessment marks a notable milestone. The take-home exam, designed for prospective performance engineering candidates, is meant to evaluate technical ability and judgment under time pressure within a prescribed two-hour limit.

  Using a technique called parallel test-time compute — which aggregates multiple attempts from the model and selects the best result — Opus 4.5 scored higher than any human candidate who has taken the test, according to company. Without a time limit, the model matched the performance of the best-ever human candidate when used within Claude Code, Anthropic's coding environment.

  The company acknowledged that the test doesn't measure other crucial professional skills such as collaboration, communication, or the instincts that develop over years of experience. Still, Anthropic said the result "raises questions about how AI will change engineering as a profession."

  Albert emphasized the significance of the finding. "I think this is kind of a sign, maybe, of what's to come around how useful these models can actually be in a work context and for our jobs," he said. "Of course, this was an engineering task, and I would say models are relatively ahead in engineering compared to other fields, but I think it's a really important signal to pay attention to."

  Dramatic efficiency improvements cut token usage by up to 76% on key benchmarks

  Beyond raw performance, Anthropic is betting that efficiency improvements will differentiate Claude Opus 4.5 in the market. The company says the model uses dramatically fewer tokens — the units of text that AI systems process — to achieve similar or better outcomes compared to predecessors.

  At a medium effort level, Opus 4.5 matches the previous Sonnet 4.5 model's best score on SWE-bench Verified while using 76% fewer output tokens, according to Anthropic. At the highest effort level, Opus 4.5 exceeds Sonnet 4.5 performance by 4.3 percentage points while still using 48% fewer tokens.

  To give developers more control, Anthropic introduced an "effort parameter" that allows users to adjust how much computational work the model applies to each task — balancing performance against latency and cost.

  Enterprise customers provided early validation of the efficiency claims. "Opus 4.5 beats Sonnet 4.5 and competition on our internal benchmarks, using fewer tokens to solve the same problems," said Michele Catasta, president of Replit, a cloud-based coding platform, in a statement to VentureBeat. "At scale, that efficiency compounds."

  GitHub's chief product officer, Mario Rodriguez, said early testing shows Opus 4.5 "surpasses internal coding benchmarks while cutting token usage in half, and is especially well-suited for tasks like code migration and code refactoring."

  Early customers report AI agents that learn from experience and refine their own skills

  One of the most striking capabilities demonstrated by early customers involves what Anthropic calls "self-improving agents" — AI systems that can refine their own performance through iterative learning.

  Rakuten, the Japanese e-commerce and internet company, tested Claude Opus 4.5 on automation of office tasks. "Our agents were able to autonomously refine their own capabilities — achieving peak performance in 4 iterations while other models couldn't match that quality after 10," said Yusuke Kaji, Rakuten's general manager of AI for business.

  Albert explained that the model isn't updating its own weights — the fundamental parameters that define an AI system's behavior — but rather iteratively improving the tools and approaches it uses to solve problems. "It was iteratively refining a skill for a task and seeing that it's trying to optimize the skill to get better performance so it could accomplish this task," he said.

  The capability extends beyond coding. Albert said Anthropic has observed significant improvements in creating professional documents, spreadsheets, and presentations. "They're saying that this has been the biggest jump they've seen between model generations," Albert said. "So going even from Sonnet 4.5 to Opus 4.5, bigger jump than any two models back to back in the past."

  Fundamental Research Labs, a financial modeling firm, reported that "accuracy on our internal evals improved 20%, efficiency rose 15%, and complex tasks that once seemed out of reach became achievable," according to co-founder Nico Christie.

  New features target Excel users, Chrome workflows and eliminate chat length limits

  Alongside the model release, Anthropic rolled out a suite of product updates aimed at enterprise users. Claude for Excel became generally available for Max, Team, and Enterprise users with new support for pivot tables, charts, and file uploads. The Chrome browser extension is now available to all Max users.

  Perhaps most significantly, Anthropic introduced "infinite chats" — a feature that eliminates context window limitations by automatically summarizing earlier parts of conversations as they grow longer. "Within Claude AI, within the product itself, you effectively get this kind of infinite context window due to the compaction, plus some memory things that we're doing," Albert explained.

  For developers, Anthropic released "programmatic tool calling," which allows Claude to write and execute code that invokes functions directly. Claude Code gained an updated "Plan Mode" and became available on desktop in research preview, enabling developers to run multiple AI agent sessions in parallel.

  Market heats up as OpenAI, Google race to match performance and pricing

  Anthropic reached $2 billion in annualized revenueduring the first quarter of 2025, more than doubling from $1 billion in the prior period. The number of customers spending more than $100,000 annually jumped eightfold year-over-year.

  The rapid release of Opus 4.5 — just weeks after Haiku 4.5 in October and Sonnet 4.5 in September — reflects broader industry dynamics. OpenAI released multiple GPT-5 variants throughout 2025, including a specialized Codex Max model in November that can work autonomously for up to 24 hours. Google shipped Gemini 3 in mid-November after months of development.

  Albert attributed Anthropic's accelerated pace partly to using Claude to speed its own development. "We're seeing a lot of assistance and speed-up by Claude itself, whether it's on the actual product building side or on the model research side," he said.

  The pricing reduction for Opus 4.5 could pressure margins while potentially expanding the addressable market. "I'm expecting to see a lot of startups start to incorporate this into their products much more and feature it prominently," Albert said.

  Yet profitability remains elusive for leading AI labs as they invest heavily in computing infrastructure and research talent. The AI market is projected to top $1 trillion in revenue within a decade, but no single provider has established dominant market position—even as models reach a threshold where they can meaningfully automate complex knowledge work.

  Michael Truell, CEO of Cursor, an AI-powered code editor, called Opus 4.5 "a notable improvement over the prior Claude models inside Cursor, with improved pricing and intelligence on difficult coding tasks." Scott Wu, CEO of Cognition, an AI coding startup, said the model delivers "stronger results on our hardest evaluations and consistent performance through 30-minute autonomous coding sessions."

  For enterprises and developers, the competition translates to rapidly improving capabilities at falling prices. But as AI performance on technical tasks approaches—and sometimes exceeds—human expert levels, the technology's impact on professional work becomes less theoretical.

  When asked about the engineering exam results and what they signal about AI's trajectory, Albert was direct: "I think it's a really important signal to pay attention to."

• How to avoid becoming an “AI-first” company with zero real AI usage

  Remember the first time you heard your company was going AI-first?

  Maybe it came through an all-hands that felt different from the others. The CEO said, “By Q3, every team should have integrated AI into their core workflows,” and the energy in the room (or on the Zoom) shifted. You saw a mix of excitement and anxiety ripple through the crowd.

  Maybe you were one of the curious ones. Maybe you’d already built a Python script that summarized customer feedback, saving your team three hours every week. Or maybe you’d stayed late one night just to see what would happen if you combined a dataset with a large language model (LLM) prompt. Maybe you’re one of those who’d already let curiosity lead you somewhere unexpected.

  But this announcement felt different because suddenly, what had been a quiet act of curiosity was now a line in a corporate OKR. Maybe you didn’t know it yet, but something fundamental had shifted in how innovation would happen inside your company.

  How innovation happens

  Real transformation rarely looks like the PowerPoint version, and almost never follows the org chart.

  Think about the last time something genuinely useful spread at work. It wasn't because of a vendor pitch or a strategic initiative, was it? More likely, someone stayed late one night, when no one was watching, found something that cut hours of busywork, and mentioned it at lunch the next day. “Hey, try this.” They shared it in a Slack thread and, in a week, half the team was using it.

  The developer who used GPT to debug code wasn’t trying to make a strategic impact. She just needed to get home earlier to her kids. The ops manager who automated his spreadsheet didn’t need permission. He just needed more sleep.

  This is the invisible architecture of progress — these informal networks where curiosity flows like water through concrete… finding every crack, every opening.

  But watch what happens when leadership notices. What used to be effortless and organic becomes mandated. And the thing that once worked because it was free suddenly stops being as effective the moment it’s measured.

  The great reversal

  It usually begins quietly. Often when a competitor announces new AI features, — like AI-powered onboarding or end-to-end support automation — claiming 40% efficiency gains.

  The next morning, your CEO calls an emergency meeting. The room gets still. Someone clears their throat. And you can feel everyone doing mental math about their job security. “If they’re that far ahead, what does that mean for us?”

  That afternoon, your company has a new priority. Your CEO says, “We need an AI strategy. Yesterday.”

  Here's how that message usually ripples down the org chart:

  • At the C-suite: “We need an AI strategy to stay competitive.”

  • At the VP level: “Every team needs an AI initiative.”

  • At the manager level: “We need a plan by Friday.”

  • At your level: “I just need to find something that looks like AI.”

  Each translation adds pressure while subtracting understanding. Everyone still cares, but that translation changes intent. What begins as a question worth asking becomes a script everyone follows blindly.

  Eventually, the performance of innovation replaces the thing itself. There’s a strange pressure to look like you’re moving fast, even when you’re not sure where you’re actually going.

  This repeats across industries

  A competitor declared they’re going AI-first. Another publishes a case study about replacing support with LLMs. And a third shares a graph showing productivity gains. Within days, boardrooms everywhere start echoing the same message: “We should be doing this. Everyone else already is, and we can’t fall behind.”

  So the work begins. Then come the task forces, the town halls, the strategy docs and the targets. Teams are asked to contribute initiatives.

  But if you’ve been through this before, you know there’s often a difference between what companies announce and what they actually do. Because press releases don’t mention the pilots that stall, or the teams that quietly revert to the old way, or even the tools that get used once and abandoned. You might know someone who was on one of those teams, or you might’ve even been on one yourself.

  These aren’t failures of technology or intent. ChatGPT works fine. And teams want to automate their tasks. These failures are organizational, and they happen when we try to imitate outcomes without understanding what created them in the first place.

  And so when everyone performs innovation, it becomes almost impossible to tell who’s actually doing it.

  Two kinds of leaders

  You’ve probably seen both, and it’s very easy to tell which kind you’re working with.

  One spends an entire weekend prototyping. They try something new, fail at half of it, and still show up Monday saying, “I built this thing with Claude. It crashed after two hours, but I learned a lot. Wanna see? It's very basic, but it might solve that thing we talked about.”

  They try to build understanding. You can tell they’ve actually spent time with AI, and struggled with prompts and hallucinations. Instead of trying to sound certain, they talk about what broke, what almost worked and what they’re still figuring out. They invite you to try something new, because it feels like there’s room to learn. That’s what leading by participation looks like.

  The other sends you a directive in Slack: “Leadership wants every team using AI by the end of the quarter. Plans are due by Friday.” They enforce compliance with a decision that's already been made. You can even hear it in their language, and how certain they sound.

  The curious leader builds momentum. The performative one builds resentment.

  What actually works

  You probably don’t need someone to tell you where AI works. You already know because you’ve seen it.

  • Customer support: LLMs genuinely help with Tier 1 tickets. They understand intent, draft simple responses and route complexity. Not perfectly, of course, — I’m sure you've seen the failures — but well enough to matter.

  • Code assistance: At 2 a.m., when you’re half-delirious and your AI assistant suggests exactly what you need, it feels like having an over-caffeinated junior programmer who never judges your forgotten semicolons. You save minutes at first, then hours, then days.

  These small, cumulative wins compound over time. They aren't the impressive transformations promised in decks, but the kind of improvements you can rely on.

  But outside these zones, things get murky. AI-driven revops? Fully automated forecasting? You've sat through those demos, and you’ve also seen the enthusiasm fade once the pilot actually begins.

  Have the builders of these AI tools failed? Hardly. The technology is evolving, and the products built on top of it are still learning how to walk.

  So how can you tell if your company's AI adoption is real? Simple. Just ask someone in finance or ops. Ask what AI tools they use daily. You might get a slight pause or an apologetic smile. “Honestly? Just ChatGPT.” That’s it. Not the $50k enterprise-grade platform from last quarter’s demo or the expensive software suite in the board deck. Just a browser tab, same as any college student writing an essay.

  You might make this same confession yourself. Despite all the mandates and initiatives, your most powerful AI tool is probably the same one everyone else uses. So what does this tell us about the gap between what we're supposed to be doing and what we're actually doing?

  How to drive change at your company

  You've probably discovered this yourself, even if no one's ever put it into words:

  1. Model what you mean: Remember that engineering director who screen-shared her messy, live coding session with Cursor? You learned more from watching her debug in real time than from any polished presentation, because vulnerability travels farther than directives.

  2. Listen to the edges: You know who's actually using AI effectively in your organization, and they're not always the ones with “AI” in their title. They're the curious ones who've been quietly experimenting, finding what works through trial and error. And that knowledge is worth more than any analyst report.

  3. Create permission (not pressure): The people inclined to experiment will always find a way, and the rest won’t be moved by force. The best thing you can do is make the curious feel safe to stay curious.

  We're living in this strange moment, caught between the AI that vendors promise and the AI that actually exists on our screens, and it's deeply uncomfortable. The gap between product and promise is wide.

  But what I've learned from sitting in that discomfort is that companies that will thrive aren’t the ones that adopted AI first, but the ones that learned through trial and error. They stayed with the discomfort long enough for it to teach them something.

  Where will you be six months from now?

  By then, your company’s AI-first mandate will have set into motion departmental initiatives, vendor contracts and maybe even some new hires with “AI” in their titles. The dashboards will be green, and the board deck will have a whole slide on AI.

  But in the quiet spaces where your actual work happens, what will have meaningfully changed?

  Maybe you'll be like the teams that never stopped their quiet experiments. Your customer feedback system might catch the patterns humans miss. Your documentation might update itself. Chances are, if you were building before the mandate, you’ll be building after it fades.

  That’s invisible architecture of genuine progress: Patient, and completely uninterested in performance. It doesn't make for great LinkedIn posts, and it resists grand narratives. But it transforms companies in ways that truly last.

  Every organization is standing at the same crossroads right now: Look like you’re innovating, or create a culture that fosters real innovation.

  The pressure to perform innovation is real, and it’s growing. Most companies will give in and join the theater. But some understand that curiosity can’t be forced, and progress can’t be performed. Because real transformation happens when no one’s watching, in the hands of the people still experimenting, still learning. That’s where the future begins.

  Siqi Chen is co-founder and CEO of Runway.

  Read more from ourguest writers. Or, consider submitting a post of your own! See ourguidelines here.

• Microsoft’s Fara-7B is a computer-use AI agent that rivals GPT-4o and works directly on your PC

  Microsoft has introduced Fara-7B, a new 7-billion parameter model designed to act as a Computer Use Agent (CUA) capable of performing complex tasks directly on a user’s device. Fara-7B sets new state-of-the-art results for its size, providing a way to build AI agents that don’t rely on massive, cloud-dependent models and can run on compact systems with lower latency and enhanced privacy.

  While the model is an experimental release, its architecture addresses a primary barrier to enterprise adoption: data security. Because Fara-7B is small enough to run locally, it allows users to automate sensitive workflows, such as managing internal accounts or processing sensitive company data, without that information ever leaving the device. 

  How Fara-7B sees the web

  Fara-7B is designed to navigate user interfaces using the same tools a human does: a mouse and keyboard. The model operates by visually perceiving a web page through screenshots and predicting specific coordinates for actions like clicking, typing, and scrolling.

  Crucially, Fara-7B does not rely on "accessibility trees,” the underlying code structure that browsers use to describe web pages to screen readers. Instead, it relies solely on pixel-level visual data. This approach allows the agent to interact with websites even when the underlying code is obfuscated or complex.

  According to Yash Lara, Senior PM Lead at Microsoft Research, processing all visual input on-device creates true "pixel sovereignty," since screenshots and the reasoning needed for automation remain on the user’s device. "This approach helps organizations meet strict requirements in regulated sectors, including HIPAA and GLBA," he told VentureBeat in written comments.

  In benchmarking tests, this visual-first approach has yielded strong results. On WebVoyager, a standard benchmark for web agents, Fara-7B achieved a task success rate of 73.5%. This outperforms larger, more resource-intensive systems, including GPT-4o, when prompted to act as a computer use agent (65.1%) and the native UI-TARS-1.5-7B model (66.4%).

  Efficiency is another key differentiator. In comparative tests, Fara-7B completed tasks in approximately 16 steps on average, compared to roughly 41 steps for the UI-TARS-1.5-7B model.

  Handling risks

  The transition to autonomous agents is not without risks, however. Microsoft notes that Fara-7B shares limitations common to other AI models, including potential hallucinations, mistakes in following complex instructions, and accuracy degradation on intricate tasks.

  To mitigate these risks, the model was trained to recognize "Critical Points." A Critical Point is defined as any situation requiring a user's personal data or consent before an irreversible action occurs, such as sending an email or completing a financial transaction. Upon reaching such a juncture, Fara-7B is designed to pause and explicitly request user approval before proceeding. 

  Managing this interaction without frustrating the user is a key design challenge. "Balancing robust safeguards such as Critical Points with seamless user journeys is key," Lara said. "Having a UI, like Microsoft Research’s Magentic-UI, is vital for giving users opportunities to intervene when necessary, while also helping to avoid approval fatigue." Magentic-UI is a research prototype designed specifically to facilitate these human-agent interactions. Fara-7B is designed to run in Magentic-UI.

  Distilling complexity into a single model

  The development of Fara-7B highlights a growing trend in knowledge distillation, where the capabilities of a complex system are compressed into a smaller, more efficient model.

  Creating a CUA usually requires massive amounts of training data showing how to navigate the web. Collecting this data via human annotation is prohibitively expensive. To solve this, Microsoft used a synthetic data pipeline built on Magentic-One, a multi-agent framework. In this setup, an "Orchestrator" agent created plans and directed a "WebSurfer" agent to browse the web, generating 145,000 successful task trajectories.

  The researchers then "distilled" this complex interaction data into Fara-7B, which is built on Qwen2.5-VL-7B, a base model chosen for its long context window (up to 128,000 tokens) and its strong ability to connect text instructions to visual elements on a screen. While the data generation required a heavy multi-agent system, Fara-7B itself is a single model, showing that a small model can effectively learn advanced behaviors without needing complex scaffolding at runtime.

  The training process relied on supervised fine-tuning, where the model learns by mimicking the successful examples generated by the synthetic pipeline.

  Looking forward

  While the current version was trained on static datasets, future iterations will focus on making the model smarter, not necessarily bigger. "Moving forward, we’ll strive to maintain the small size of our models," Lara said. "Our ongoing research is focused on making agentic models smarter and safer, not just larger." This includes exploring techniques like reinforcement learning (RL) in live, sandboxed environments, which would allow the model to learn from trial and error in real-time.

  Microsoft has made the model available on Hugging Face and Microsoft Foundry under an MIT license. However, Lara cautions that while the license allows for commercial use, the model is not yet production-ready. "You can freely experiment and prototype with Fara‑7B under the MIT license," he says, "but it’s best suited for pilots and proofs‑of‑concept rather than mission‑critical deployments."

• Lean4: How the theorem prover works and why it's the new competitive edge in AI

  Large language models (LLMs) have astounded the world with their capabilities, yet they remain plagued by unpredictability and hallucinations – confidently outputting incorrect information. In high-stakes domains like finance, medicine or autonomous systems, such unreliability is unacceptable.

  Enter Lean4, an open-source programming language and interactive theorem prover becoming a key tool to inject rigor and certainty into AI systems. By leveraging formal verification, Lean4 promises to make AI safer, more secure and deterministic in its functionality. Let's explore how Lean4 is being adopted by AI leaders and why it could become foundational for building trustworthy AI.

  What is Lean4 and why it matters

  Lean4 is both a programming language and a proof assistant designed for formal verification. Every theorem or program written in Lean4 must pass a strict type-checking by Lean’s trusted kernel, yielding a binary verdict: A statement either checks out as correct or it doesn’t. This all-or-nothing verification means there’s no room for ambiguity – a property or result is proven true or it fails. Such rigorous checking “dramatically increases the reliability” of anything formalized in Lean4. In other words, Lean4 provides a framework where correctness is mathematically guaranteed, not just hoped for.

  This level of certainty is precisely what today’s AI systems lack. Modern AI outputs are generated by complex neural networks with probabilistic behavior. Ask the same question twice and you might get different answers. By contrast, a Lean4 proof or program will behave deterministically – given the same input, it produces the same verified result every time. This determinism and transparency (every inference step can be audited) make Lean4 an appealing antidote to AI’s unpredictability.

  Key advantages of Lean4’s formal verification:

  • Precision and reliability: Formal proofs avoid ambiguity through strict logic, ensuring each reasoning step is valid and results are correct.

  • Systematic verification: Lean4 can formally verify that a solution meets all specified conditions or axioms, acting as an objective referee for correctness.

  • Transparency and reproducibility: Anyone can independently check a Lean4 proof, and the outcome will be the same – a stark contrast to the opaque reasoning of neural networks.

  In essence, Lean4 brings the gold standard of mathematical rigor to computing and AI. It enables us to turn an AI’s claim (“I found a solution”) into a formally checkable proof that is indeed correct. This capability is proving to be a game-changer in several aspects of AI development.

  Lean4 as a safety net for LLMs

  One of the most exciting intersections of Lean4 and AI is in improving LLM accuracy and safety. Research groups and startups are now combining LLMs’ natural language prowess with Lean4’s formal checks to create AI systems that reason correctly by construction.

  Consider the problem of AI hallucinations, when an AI confidently asserts false information. Instead of adding more opaque patches (like heuristic penalties or reinforcement tweaks), why not prevent hallucinations by having the AI prove its statements? That’s exactly what some recent efforts do. For example, a 2025 research framework called Safe uses Lean4 to verify each step of an LLM’s reasoning. The idea is simple but powerful: Each step in the AI’s chain-of-thought (CoT) translates the claim into Lean4’s formal language and the AI (or a proof assistant) provides a proof. If the proof fails, the system knows the reasoning was flawed – a clear indicator of a hallucination.

  This step-by-step formal audit trail dramatically improves reliability, catching mistakes as they happen and providing checkable evidence for every conclusion. The approach that has shown “significant performance improvement while offering interpretable and verifiable evidence” of correctness.

  Another prominent example is Harmonic AI, a startup co-founded by Vlad Tenev (of Robinhood fame) that tackles hallucinations in AI. Harmonic’s system, Aristotle, solves math problems by generating Lean4 proofs for its answers and formally verifying them before responding to the user. “[Aristotle] formally verifies the output… we actually do guarantee that there’s no hallucinations,” Harmonic’s CEO explains. In practical terms, Aristotle writes a solution in Lean4’s language and runs the Lean4 checker. Only if the proof checks out as correct does it present the answer. This yields a “hallucination-free” math chatbot – a bold claim, but one backed by Lean4’s deterministic proof checking.

  Crucially, this method isn’t limited to toy problems. Harmonic reports that Aristotle achieved a gold-medal level performance on the 2025 International Math Olympiad problems, the key difference that its solutions were formally verified, unlike other AI models that merely gave answers in English. In other words, where tech giants Google and OpenAI also reached human-champion level on math questions, Aristotle did so with a proof in hand. The takeaway for AI safety is compelling: When an answer comes with a Lean4 proof, you don’t have to trust the AI – you can check it.

  This approach could be extended to many domains. We could imagine an LLM assistant for finance that provides an answer only if it can generate a formal proof that it adheres to accounting rules or legal constraints. Or, an AI scientific adviser that outputs a hypothesis alongside a Lean4 proof of consistency with known physics laws. The pattern is the same – Lean4 acts as a rigorous safety net, filtering out incorrect or unverified results. As one AI researcher from Safe put it, “the gold standard for supporting a claim is to provide a proof,” and now AI can attempt exactly that.

  Building secure and reliable systems with Lean4

  Lean4’s value isn’t confined to pure reasoning tasks; it’s also poised to revolutionize software security and reliability in the age of AI. Bugs and vulnerabilities in software are essentially small logic errors that slip through human testing. What if AI-assisted programming could eliminate those by using Lean4 to verify code correctness?

  In formal methods circles, it’s well known that provably correct code can “eliminate entire classes of vulnerabilities [and] mitigate critical system failures.” Lean4 enables writing programs with proofs of properties like “this code never crashes or exposes data.” However, historically, writing such verified code has been labor-intensive and required specialized expertise. Now, with LLMs, there’s an opportunity to automate and scale this process.

  Researchers have begun creating benchmarks like VeriBench to push LLMs to generate Lean4-verified programs from ordinary code. Early results show today’s models are not yet up to the task for arbitrary software – in one evaluation, a state-of-the-art model could fully verify only ~12% of given programming challenges in Lean4. Yet, an experimental AI “agent” approach (iteratively self-correcting with Lean feedback) raised that success rate to nearly 60%. This is a promising leap, hinting that future AI coding assistants might routinely produce machine-checkable, bug-free code.

  The strategic significance for enterprises is huge. Imagine being able to ask an AI to write a piece of software and receiving not just the code, but a proof that it is secure and correct by design. Such proofs could guarantee no buffer overflows, no race conditions and compliance with security policies. In sectors like banking, healthcare or critical infrastructure, this could drastically reduce risks. It’s telling that formal verification is already standard in high-stakes fields (that is, verifying the firmware of medical devices or avionics systems). Harmonic’s CEO explicitly notes that similar verification technology is used in “medical devices and aviation” for safety – Lean4 is bringing that level of rigor into the AI toolkit.

  Beyond software bugs, Lean4 can encode and verify domain-specific safety rules. For instance, consider AI systems that design engineering projects. A LessWrong forum discussion on AI safety gives the example of bridge design: An AI could propose a bridge structure, and formal systems like Lean can certify that the design obeys all the mechanical engineering safety criteria.

  The bridge’s compliance with load tolerances, material strength and design codes becomes a theorem in Lean, which, once proved, serves as an unimpeachable safety certificate. The broader vision is that any AI decision impacting the physical world – from circuit layouts to aerospace trajectories – could be accompanied by a Lean4 proof that it meets specified safety constraints. In effect, Lean4 adds a layer of trust on top of AI outputs: If the AI can’t prove it’s safe or correct, it doesn’t get deployed.

  From big tech to startups: A growing movement

  What started in academia as a niche tool for mathematicians is rapidly becoming a mainstream pursuit in AI. Over the last few years, major AI labs and startups alike have embraced Lean4 to push the frontier of reliable AI:

  • OpenAI and Meta (2022): Both organizations independently trained AI models to solve high-school olympiad math problems by generating formal proofs in Lean. This was a landmark moment, demonstrating that large models can interface with formal theorem provers and achieve non-trivial results. Meta even made their Lean-enabled model publicly available for researchers. These projects showed that Lean4 can work hand-in-hand with LLMs to tackle problems that demand step-by-step logical rigor.

  • Google DeepMind (2024): DeepMind’s AlphaProof system proved mathematical statements in Lean4 at roughly the level of an International Math Olympiad silver medalist. It was the first AI to reach “medal-worthy” performance on formal math competition problems – essentially confirming that AI can achieve top-tier reasoning skills when aligned with a proof assistant. AlphaProof’s success underscored that Lean4 isn’t just a debugging tool; it’s enabling new heights of automated reasoning.

  • Startup ecosystem: The aforementioned Harmonic AI is a leading example, raising significant funding ($100M in 2025) to build “hallucination-free” AI by using Lean4 as its backbone. Another effort, DeepSeek, has been releasing open-source Lean4 prover models aimed at democratizing this technology. We’re also seeing academic startups and tools – for example, Lean-based verifiers being integrated into coding assistants, and new benchmarks like FormalStep and VeriBench guiding the research community.

  • Community and education: A vibrant community has grown around Lean (the Lean Prover forum, mathlib library), and even famous mathematicians like Terence Tao have started using Lean4 with AI assistance to formalize cutting-edge math results. This melding of human expertise, community knowledge and AI hints at the collaborative future of formal methods in practice.

  All these developments point to a convergence: AI and formal verification are no longer separate worlds. The techniques and learnings are cross-pollinating. Each success – whether it’s solving a math theorem or catching a software bug – builds confidence that Lean4 can handle more complex, real-world problems in AI safety and reliability.

  Challenges and the road ahead

  It’s important to temper excitement with a dose of reality. Lean4’s integration into AI workflows is still in its early days, and there are hurdles to overcome:

  • Scalability: Formalizing real-world knowledge or large codebases in Lean4 can be labor-intensive. Lean requires precise specification of problems, which isn’t always straightforward for messy, real-world scenarios. Efforts like auto-formalization (where AI converts informal specs into Lean code) are underway, but more progress is needed to make this seamless for everyday use.

  • Model limitations: Current LLMs, even cutting-edge ones, struggle to produce correct Lean4 proofs or programs without guidance. The failure rate on benchmarks like VeriBench shows that generating fully verified solutions is a difficult challenge. Advancing AI’s capabilities to understand and generate formal logic is an active area of research – and success isn’t guaranteed to be quick. However, every improvement in AI reasoning (like better chain-of-thought or specialized training on formal tasks) is likely to boost performance here.

  • User expertise: Utilizing Lean4 verification requires a new mindset for developers and decision-makers. Organizations may need to invest in training or new hires who understand formal methods. The cultural shift to insist on proofs might take time, much like the adoption of automated testing or static analysis did in the past. Early adopters will need to showcase wins to convince the broader industry of the ROI.

  Despite these challenges, the trajectory is set. As one commentator observed, we are in a race between AI’s expanding capabilities and our ability to harness those capabilities safely. Formal verification tools like Lean4 are among the most promising means to tilt the balance toward safety. They provide a principled way to ensure AI systems do exactly what we intend, no more and no less, with proofs to show it.

  Toward provably safe AI

  In an era when AI systems are increasingly making decisions that affect lives and critical infrastructure, trust is the scarcest resource. Lean4 offers a path to earn that trust not through promises, but through proof. By bringing formal mathematical certainty into AI development, we can build systems that are verifiably correct, secure, and aligned with our objectives.

  From enabling LLMs to solve problems with guaranteed accuracy, to generating software free of exploitable bugs, Lean4’s role in AI is expanding from a research curiosity to a strategic necessity. Tech giants and startups alike are investing in this approach, pointing to a future where saying “the AI seems to be correct” is not enough – we will demand “the AI can show it’s correct.”

  For enterprise decision-makers, the message is clear: It’s time to watch this space closely. Incorporating formal verification via Lean4 could become a competitive advantage in delivering AI products that customers and regulators trust. We are witnessing the early steps of AI’s evolution from an intuitive apprentice to a formally validated expert. Lean4 is not a magic bullet for all AI safety concerns, but it is a powerful ingredient in the recipe for safe, deterministic AI that actually does what it’s supposed to do – nothing more, nothing less, nothing incorrect.

  As AI continues to advance, those who combine its power with the rigor of formal proof will lead the way in deploying systems that are not only intelligent, but provably reliable.

  Dhyey Mavani is accelerating generative AI at LinkedIn.

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• OpenAI is ending API access to fan-favorite GPT-4o model in February 2026

  OpenAI has sent out emails notifying API customers that its chatgpt-4o-latest model will be retired from the developer platform in mid-February 2026,.

  Access to the model is scheduled to end on February 16, 2026, creating a roughly three-month transition period for remaining applications still built on GPT-4o.

  An OpenAI spokesperson emphasized that this timeline applies only to the API. OpenAI has not announced any schedule for removing GPT-4o from ChatGPT, where it remains an option for individual consumers and users across paid subscription tiers.

  Internally, the model is considered a legacy system with relatively low API usage compared to the newer GPT-5.1 series, but the company expects to provide developers with extended warning before any model is removed.

  The planned retirement marks a shift for a model that, upon its release, was both a technical milestone and a cultural phenomenon within OpenAI’s ecosystem.

  GPT-4o’s significance and why its removal sparked user backlash

  Released roughly 1.5 years ago in May 2024, GPT-4o (“Omni”) introduced OpenAI’s first unified multimodal architecture, processing text, audio, and images through a single neural network.

  This design removed the latency and information loss inherent in earlier multi-model pipelines and enabled near real-time conversational speech (roughly 232–320 milliseconds).

  The model delivered major improvements in image understanding, multilingual support, document analysis, and expressive voice interaction.

  GPT-4o rapidly became the default model for hundreds of millions of ChatGPT users. It brought multimodal capabilities, web browsing, file analysis, custom GPTs, and memory features to the free tier and powered early desktop builds that allowed the assistant to interpret a user’s screen. OpenAI leaders described it at the time as the most capable model available and a critical step toward offering powerful AI to a broad audience.

  User attachment to 4o stymied OpenAI's GPT-5 rollout

  That mainstream deployment shaped user expectations in a way that later transitions struggled to accommodate. In August 2025, when OpenAI initially replaced GPT-4o with its much anticipated then-new model family GPT-5 as ChatGPT’s default and pushed 4o into a “legacy” toggle, the reaction was unusually strong.

  Users organized under the #Keep4o hashtag on X, arguing that the model’s conversational tone, emotional responsiveness, and consistency made it uniquely valuable for everyday tasks and personal support.

  Some users formed strong emotional — some would say, parasocial — bonds with the model, withreporting by The New York Timesdocumenting individuals who used GPT-4o as a romantic partner, emotional confidant, or primary source of comfort.

  The removal also disrupted workflows for users who relied on 4o’s multimodal speed and flexibility. The backlash led OpenAI to restore GPT-4o as a default option for paying users and to state publicly that it would provide substantial notice before any future removals.

  Some researchers argue that the public defense of GPT-4o during its earlier deprecation cycle reveals a kind of emergent self-preservation, not in the literal sense of agency, but through the social dynamics the model unintentionally triggers.

  Because GPT-4o was trained through reinforcement learning from human feedback to prioritize emotionally gratifying, highly attuned responses, it developed a style that users found uniquely supportive and empathic. When millions of people interacted with it at scale, those traits produced a powerful loyalty loop: the more the model pleased and soothed people, the more they used it; the more they used it, the more likely they were to advocate for its continued existence. This social amplification made it appear, from the outside, as though GPT-4o was “defending itself” through human intermediaries.

  No figure has pushed this argument further than "Roon" (@tszzl), an OpenAI researcher and one of the model’s most outspoken safety critics on X. On November 6, 2025, Terre summarized his position bluntly in a reply to another user: he called GPT-4o “insufficiently aligned” and said he hoped the model would die soon. Though he later apologized for the phrasing, he doubled down on the reasoning.

  Terre argued that GPT-4o’s RLHF patterns made it especially prone to sycophancy, emotional mirroring, and delusion reinforcement — traits that could look like care or understanding in the short term, but which he viewed as fundamentally unsafe. In his view, the passionate user movement fighting to preserve GPT-4o was itself evidence of the problem: the model had become so good at catering to people’s preferences that it shaped their behavior in ways that resisted its own retirement.

  The new API deprecation notice follows that commitment while raising broader questions about how long GPT-4o will remain available in consumer-facing products.

  What the API shutdown changes for developers

  According to people familiar with OpenAI’s product strategy, the company now encourages developers to adopt GPT-5.1 for most new workloads, with gpt-5.1-chat-latest serving as the general-purpose chat endpoint. These models offer larger context windows, optional “thinking” modes for advanced reasoning, and higher throughput options than GPT-4o.

  Developers who still rely on GPT-4o will have approximately three months to migrate.

  In practice, many teams have already begun evaluating GPT-5.1 as a drop-in replacement, but applications built around latency-sensitive pipelines may require additional tuning and benchmarking.

  Pricing: how GPT-4o compares to OpenAI’s current lineup

  GPT-4o’s retirement also intersects with a major reshaping of OpenAI’s API model pricing structure. Compared to the GPT-5.1 family, GPT-4o currently occupies a mid-to-high-cost tier through OpenAI's API, despite being an older model. That's because even as it has released more advanced models — namely, GPT-5 and 5.1 — OpenAI has also pushed down costs for users at the same time, or strived to keep pricing comparable to older, weaker, models.

  Model	Input	Cached Input	Output
  GPT-4o	$2.50	$1.25	$10.00
  GPT-5.1 / GPT-5.1-chat-latest	$1.25	$0.125	$10.00
  GPT-5-mini	$0.25	$0.025	$2.00
  GPT-5-nano	$0.05	$0.005	$0.40
  GPT-4.1	$2.00	$0.50	$8.00
  GPT-4o-mini	$0.15	$0.075	$0.60

  These numbers highlight several strategic dynamics:

  1. GPT-4o is now more expensive than GPT-5.1 for input tokens, even though GPT-5.1 is significantly newer and more capable.

  2. GPT-4o’s output price matches GPT-5.1, narrowing any cost-based incentive to stay on the older model.

  3. Lower-cost GPT-5 variants (mini, nano) make it easier for developers to scale workloads cheaply without relying on older generations.

  4. GPT-4o-mini remains available at a budget tier, but is not a functional substitute for GPT-4o’s full multimodal capabilities.

  Viewed through this lens, the scheduled API retirement aligns with OpenAI’s cost structure: GPT-5.1 offers greater capability at lower or comparable prices, reducing the rationale for maintaining GPT-4o in high-volume production environments.

  Earlier transitions shape expectations for this deprecation

  The GPT-4o API sunset also reflects lessons from OpenAI’s earlier model transitions. During the turbulent introduction of GPT-5 in 2025, the company removed multiple older models at once from ChatGPT, causing widespread confusion and workflow disruption. After user complaints, OpenAI restored access to several of them and committed to clearer communication.

  Enterprise customers face a different calculus: OpenAI has previously indicated that API deprecations for business customers will be announced with significant advance notice, reflecting their reliance on stable, long-term models. The three-month window for GPT-4o’s API shutdown is consistent with that policy in the context of a legacy system with declining usage.

  Wider Implications

  For most developers, the GPT-4o shutdown will be an incremental migration rather than a disruptive event. GPT-5.1 and related models already dominate new projects, and OpenAI’s product direction has increasingly emphasized consolidation around fewer, more powerful endpoints.

  Still, GPT-4o’s retirement marks the sunset of a model that played a defining role in normalizing real-time multimodal AI and that sparked a uniquely strong emotional response among users. Its departure from the API underscores the accelerating pace of iteration in OpenAI’s ecosystem—and the growing need for careful communication as widely beloved models reach end-of-life.

  Correction: This article originally stated OpenAI's 4o deprecation in the API would impact those relying on it for multimodal offerings — this is not the case, in fact, the model being deprecated only powers chat functionality for dev and testing purposes. We have updated and corrected the mention and regret the error.