Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Swapping large language models (LLMs) is supposed to be easy, isn’t it? After all, if they all speak “natural language,” switching from GPT-4o to Claude or Gemini should be as simple as changing an API key… right? In reality, each model interprets and responds to prompts differently, making the transition anything but seamless. Enterprise teams who treat model switching as a “plug-and-play” operation often grapple with unexpected regressions: broken outputs, ballooning token costs or shifts in reasoning quality. This story explores the hidden complexities of cross-model migration, from tokenizer quirks and formatting preferences to response structures and context window performance. Based on hands-on comparisons and real-world tests, this guide unpacks what happens when you switch from OpenAI to Anthropic or Google’s Gemini and what your team needs to watch for. Understanding Model Differences Each AI model family has its own strengths and limitations. Some key aspects to consider include: Tokenization variations—Different models use different tokenization strategies, which impact the input prompt length and its total associated cost. Context window differences—Most flagship models allow a context window of 128K tokens; however, Gemini extends this to 1M and 2M tokens. Instruction following – Reasoning models prefer simpler instructions, while chat-style models require clean and explicit instructions.  Formatting preferences – Some models prefer markdown while others prefer XML tags for formatting. Model response structure—Each model has its own style of generating responses, which affects verbosity and factual accuracy. Some models perform better when allowed to “speak freely,” i.e., without adhering to an output structure, while others prefer JSON-like output structures. Interesting research shows the interplay between structured response generation and overall model performance. Migrating from OpenAI to Anthropic Imagine a real-world scenario where you’ve

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI launched two groundbreaking AI models today that can reason with images and use tools independently, representing what experts call a step change in artificial intelligence capabilities. The San Francisco-based company introduced o3 and o4-mini, the latest in its “o-series” of reasoning models, which it claims are its most intelligent and capable models to date. These systems can integrate images directly into their reasoning process, search the web, run code, analyze files, and even generate images within a single task flow. “There are some models that feel like a qualitative step into the future. GPT-4 was one of those. Today is also going to be one of those days,” said Greg Brockman, OpenAI’s president, during a press conference announcing the release. “These are the first models where top scientists tell us they produce legitimately good and useful novel ideas.” How OpenAI’s new models ‘think with images’ to transform visual problem-solving The most striking feature of these new models is their ability to “think with images” — not just see them, but manipulate and reason about them as part of their problem-solving process. “They don’t just see an image — they think with it,” OpenAI said in a statement sent to VentureBeat. “This unlocks a new class of problem-solving that blends visual and textual reasoning.” During a demonstration at the press conference, a researcher showed how o3 could analyze a physics poster from a decade-old internship, navigate its complex diagrams independently, and even identify that the final result wasn’t present in the poster itself. “It must have just read, you know, at least like 10 different papers in a few seconds for me,” Brandon McKenzie, a researcher at OpenAI working on multimodal

In association withMicrosoft and NVIDIA Anyone who crammed for exams in college knows that an impressive ability to regurgitate information is not synonymous with critical thinking. The large language models (LLMs) first publicly released in 2022 were impressive but limited—like talented students who excel at multiple-choice exams but stumble when asked to defend their logic. Today’s advanced reasoning models are more akin to seasoned graduate students who can navigate ambiguity and backtrack when necessary, carefully working through problems with a methodical approach. As AI systems thatlearn by mimicking the mechanisms of the human brain continue to advance, we’re witnessing an evolution in models from rote regurgitation to genuine reasoning. This capability marks a new chapter in the evolution of AI—and what enterprises can gain from it. But in order to tap into this enormous potential, organizations will need to ensure they have the right infrastructure and computational resources to support the advancing technology. The reasoning revolution “Reasoning models are qualitatively different than earlier LLMs,” says Prabhat Ram, partner AI/HPC architect at Microsoft, noting that these models can explore different hypotheses, assess if answers are consistently correct, and adjust their approach accordingly. “They essentially create an internal representation of a decision tree based on the training data they’ve been exposed to, and explore which solution might be the best.”
This adaptive approach to problem-solving isn’t without trade-offs. Earlier LLMs delivered outputs in milliseconds based on statistical pattern-matching and probabilistic analysis. This was—and still is—efficient for many applications, but it doesn’t allow the AI sufficient time to thoroughly evaluate multiple solution paths. In newer models, extended computation time during inference—seconds, minutes, or even longer—allows the AI to employ more sophisticated internal reinforcement learning. This opens the door for multi-step problem-solving and more nuanced decision-making.
To illustrate future use cases for reasoning-capable AI, Ram offers the example of a NASA rover sent to explore the surface of Mars. “Decisions need to be made at every moment around which path to take, what to explore, and there has to be a risk-reward trade-off. The AI has to be able to assess, ‘Am I about to jump off a cliff? Or, if I study this rock and I have a limited amount of time and budget, is this really the one that’s scientifically more worthwhile?'” Making these assessments successfully could result in groundbreaking scientific discoveries at previously unthinkable speed and scale. Reasoning capabilities are also a milestone in the proliferation of agentic AI systems: autonomous applications that perform tasks on behalf of users, such as scheduling appointments or booking travel itineraries. “Whether you’re asking AI to make a reservation, provide a literature summary, fold a towel, or pick up a piece of rock, it needs to first be able to understand the environment—what we call perception—comprehend the instructions and then move into a planning and decision-making phase,” Ram explains. Enterprise applications of reasoning-capable AI systems The enterprise applications for reasoning-capable AI are far-reaching. In health care, reasoning AI systems could analyze patient data, medical literature, and treatment protocols to support diagnostic or treatment decisions. In scientific research, reasoning models could formulate hypotheses, design experimental protocols, and interpret complex results—potentially accelerating discoveries across fields from materials science to pharmaceuticals. In financial analysis, reasoning AI could help evaluate investment opportunities or market expansion strategies, as well as develop risk profiles or economic forecasts. Armed with these insights, their own experience, and emotional intelligence, human doctors, researchers, and financial analysts could make more informed decisions, faster. But before setting these systems loose in the wild, safeguards and governance frameworks will need to be ironclad, particularly in high-stakes contexts like health care or autonomous vehicles. “For a self-driving car, there are real-time decisions that need to be made vis-a-vis whether it turns the steering wheel to the left or the right, whether it hits the gas pedal or the brake—you absolutely do not want to hit a pedestrian or get into an accident,” says Ram. “Being able to reason through situations and make an ‘optimal’ decision is something that reasoning models will have to do going forward.” The infrastructure underpinning AI reasoning To operate optimally, reasoning models require significantly more computational resources for inference. This creates distinct scaling challenges. Specifically, because the inference durations of reasoning models can vary widely—from just a few seconds to many minutes—load balancing across these diverse tasks can be challenging. Overcoming these hurdles requires tight collaboration between infrastructure providers and hardware manufacturers, says Ram, speaking of Microsoft’s collaboration with NVIDIA, which brings its accelerated computing platform to Microsoft products, including Azure AI. “When we think about Azure, and when we think about deploying systems for AI training and inference, we really have to think about the entire system as a whole,” Ram explains. “What are you going to do differently in the data center? What are you going to do about multiple data centers? How are you going to connect them?” These considerations extend into reliability challenges at all scales: from memory errors at the silicon level, to transmission errors within and across servers, thermal anomalies, and even data center-level issues like power fluctuations—all of which require sophisticated monitoring and rapid response systems.

By creating a holistic system architecture designed to handle fluctuating AI demands, Microsoft and NVIDIA’s collaboration allows companies to harness the power of reasoning models without needing to manage the underlying complexity. In addition to performance benefits, these types of collaborations allow companies to keep pace with a tech landscape evolving at breakneck speed. “Velocity is a unique challenge in this space,” says Ram. “Every three months, there is a new foundation model. The hardware is also evolving very fast—in the last four years, we’ve deployed each generation of NVIDIA GPUs and now NVIDIA GB200NVL72. Leading the field really does require a very close collaboration between Microsoft and NVIDIA to share roadmaps, timelines, and designs on the hardware engineering side, qualifications and validation suites, issues that arise in production, and so on.” Advancements in AI infrastructure designed specifically for reasoning and agentic models are critical for bringing reasoning-capable AI to a broader range of organizations. Without robust, accessible infrastructure, the benefits of reasoning models will remain relegated to companies with massive computing resources. Looking ahead, the evolution of reasoning-capable AI systems and the infrastructure that supports them promises even greater gains. For Ram, the frontier extends beyond enterprise applications to scientific discovery and breakthroughs that propel humanity forward: “The day when these agentic systems can power scientific research and propose new hypotheses that can lead to a Nobel Prize, I think that’s the day when we can say that this evolution is complete.” To learn more, please read Microsoft and NVIDIA accelerate AI development and performance, watch the NVIDIA GTC AI Conference sessions on demand, and explore the topic areas of Azure AI solutions and Azure AI infrastructure. This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. This content was researched, designed, and written entirely by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

The only office within the US State Department that monitors foreign disinformation is about to be eliminated, two State Department officials have told MIT Technology Review. The Counter Foreign Information Manipulation and Interference (R/FIMI) Hub is a small office in the State Department’s Office of Public Diplomacy that tracks and counters foreign disinformation campaigns.  In shutting R/FIMI, the department’s controversial acting undersecretary, Darren Beattie, is delivering a major win to conservative critics who have alleged that it censors conservative voices. Created at the end of 2024, it was reorganized from the Global Engagement Center, a larger office with a similar mission that had long been criticized by conservatives who claimed that, despite its international mission, it was censoring American conservatives. In 2023, Elon Musk called the center the “worst offender in US government censorship [and] media manipulation” and a “threat to our democracy.”  The culling of the office will leave the State Department without a way to actively counter the increasingly sophisticated disinformation campaigns from foreign governments like those of Russia, Iran, and China. The office could be shuttered as soon as today, according to sources at the State Department who spoke with MIT Technology Review.
Censorship claims For years, conservative voices both in and out of government have accused Big Tech of censoring conservative views—and they often charged R/FIMI’s predecessor office, the Global Engagement Center (GEC), with enabling such censorship.  GEC had its roots as the Center for Strategic Counterterrorism Communications (CSCC), created by an Obama-era executive order, but shifted its mission to fight propaganda and disinformation from foreign governments and terrorist organizations in 2016, becoming the Global Engagement Center. It was always explicitly focused on the international information space. It shut down last December after a measure to reauthorize its $61 million budget was blocked by Republicans in Congress, who accused it of helping Big Tech censor American conservative voices. 
R/FIMI had a similar goal of fighting foreign disinformation, but it was smaller: The office had a $51.9 million budget and a staff that, by mid-April, was down to just 40 employees, from 125 at GEC. Sources say that those employees will be put on administrative leave and terminated within 30 days.  With the change in administrations, R/FIMI had never really gotten off the ground. Beattie, a controversial pick for undersecretary—he was fired as a speechwriter during the first Trump administration for attending a white nationalism conference, has suggested that the FBI organized the January 6 attack on Congress, and has said that it’s not worth defending Taiwan from China—had instructed the few remaining staff to be “pencils down,” one State Department official told me, meaning to pause in their work.  The administration’s executive order on “countering censorship and restoring freedom of speech” reads like a summary of conservative accusations against GEC: “Under the guise of combatting “misinformation,” “disinformation,” and “malinformation,” the Federal Government infringed on the constitutionally protected speech rights of American citizens across the United States in a manner that advanced the Government’s preferred narrative about significant matters of public debate.  Government censorship of speech is intolerable in a free society.”
In 2023, the Daily Wire, founded by the conservative media personality Ben Shapiro, was one of two media outlets that sued GEC for allegedly infringing on the company’s First Amendment rights by funding two nonprofit organizations, the London-based Global Disinformation Index and New York–based NewsGuard, that had labeled the Daily Wire “unreliable,” “risky,” and/or (per GDI) susceptible to foreign disinformation. The lawsuit alleged that this amounted to censorship by “starving them of advertising revenue and reducing the circulation of their reporting and speech.” Those projects were not funded by GEC. In 2022, the Republican attorneys general of Missouri and Louisiana named GEC among the federal agencies that, they alleged, were pressuring social networks to censor conservative views. Though the case eventually made its way to the Supreme Court, which found no First Amendment violations, a lower court had already removed GEC’s name from the list of defendants, ruling there was “no evidence” that GEC’s communications with the social media platforms had gone beyond “educating the platforms on ‘tools and techniques used by foreign actors.’” The stakes The GEC—and now R/FIMI—was targeted as part of a wider campaign to shut down groups accused of being “weaponized” against conservatives.  Conservative critics railing against what they have called a “disinformation-industrial complex” have also taken aim at the Department of Homeland Security’s Cybersecurity and Infrastructure Security Agency (CISA) and the Stanford Internet Observatory, a prominent research group that conducted widely cited research on the flows of disinformation during elections. 

CISA’s former director, Chris Krebs, was personally targeted in an April 9 White House memo, while in response to the criticism and millions of dollars of legal fees, Stanford University shuttered the Stanford Internet Observatory ahead of the 2024 presidential elections.   But this targeting comes at a time when foreign disinformation campaigns—especially by Russia, China, and Iran—have become increasingly sophisticated.  According to one estimate, Russia spends $1.5 billion per year on foreign influence campaigns. In 2022, the Islamic Republic of Iran Broadcasting, that country’s primary foreign propaganda arm, had a $1.26 billion budget. And a 2015 estimate suggests that China spent up to $10 billion per year on media targeting non-Chinese foreigners—a figure that has almost certainly grown. In September 2024, the Justice Department indicted two employees of RT, a Russian state-owned propaganda agency, in a $10 million scheme to create propaganda aimed at influencing US audiences through a media company that has since been identified as the conservative Tenet Media.  The GEC was one effort to counter such campaigns. Some of its recent projects have included developing AI models to detect memes and deepfakes and exposing Russian propaganda efforts to influence Latin American public opinion against the war in Ukraine.  By law, the Office of Public Diplomacy has to provide Congress with 15-day advance notice of any intent to reassign any funding allocated by Congress over $1 million. Congress then has time to respond, ask questions, and challenge the decisions—though to judge from its record with other unilateral executive-branch decisions to gut government agencies, it is unlikely to do so. 

VoicePatrol is unveiling its real-time AI voice protection technology for game studios to make gaming communities safer.

The company said it has a straightforward, effective approach to real-time voice protection. Partnering with studios like Trass Games, creators of Yeeps: Hide & Seek, VoicePatrol was created to make gaming communities safer without the corporate jargon, the company said.

In the world of multiplayer gaming, unchecked extreme toxicity isn’t just annoying—it’s a direct hit to player retention and revenue. VoicePatrol tackles this head-on by identifying and addressing severe incidents within 30 seconds of their onset, ensuring a better experience for everyone involved. The integration is so seamless that developers can get it up and running in just four minutes.

VoicePatrol analytics screen.

Jack Southard, CEO of Trass Games, said in a statement, ““Working with VoicePatrol has felt more like collaborating with a dev team than a vendor. They moved fast, adjusted to our dev team’s systems, and helped our human moderators stay focused on the stuff that matters.”

VoicePatrol’s Founders have made sure to steer clear of traditional “moderation” tactics that feel more likeoverbearing surveillance. As Matei Trebien, CEO of VoicePatrol and machine learning engineer, said in a statement, “The word ‘moderation’ has come to mean control—an overcorrection that often stifles genuine interaction.”

He added, “What we’re focused on is protection: acting quickly on the most serious threats without policing every conversation or every friendly ‘f**k you.’ It’s about creating a better experience for players, not micromanaging them.”

VoicePatrol is also budget-friendly, priced at just eight cents per audio-hour, allowing studios of all sizes to afford top-tier voice protection and 100% audio coverage without breaking the bank, the company said.

VoicePatrol said the impact for studios using VoicePatrol speaks for itself: ● 55% reduction in severe incidents within the first two weeks.● 24% decrease in player churn after one quarter.● 11% revenue boost after one quarter, thanks to new revenue previously-missed because of severetoxicity.

VoicePatrol focuses on outcomes that matter—helping studios reduce harmful behavior, improve retention, and build stronger player communities. The company has eight employees and it has raised “millions” of dollars — but it isn’t disclosing the exact amount yet.

VoicePatrol dashboard

The Menlo Park, California-based company started in 2023, worked on the tech for a year and then launched its first product in the second quarter of 2024.

VoicePatrol said its differentiators are its four-minute integration time, compared to competitors who can spend days or weeks.

The company said it is its customers’ first line of defense, with automated actions taking place within 30 seconds of the offense, with 100% audio coverage. Incidents are available for review by the studio’s human moderators also in less than 30 seconds. Voice Patrol said competitors rely primarily on customers’ human moderators to act; can auto-act in more than 75 seconds only on a subset of detected violations.

The company said that all our customers, small or large, can afford to monitor 100% of their audio traffic. The firm said that is impossible for competitors, where customers are forced to be selective with how much data they send and have to accept they will have missed incidents regardless.

VoicePatrol custom moderation tools.

“We focus only on the most severe toxic and illegal incidents. Customers complained about competitors being overly-censorious,” the company said.

And it said it respects privacy, saying it receives only the audio, timestamps and a random unique device ID. If a piece of audio is not flagged for toxicity it’s instantly discarded from our system so that even the firm can’t access it. Competitors keep all data.

404-GEN today announced it has become the first decentralized 3D model generation platform to integrate with Unity.

The integration with Unity, platform to create and grow games and interactive experiences, enables developers or even players to generate models directly from the Bittensor mainnet within the Unity environment. The company targets next-gen content creators, not necessarily professionals, to bring their creative ideas to life.

“These people are not necessarily professionals or working in triple-A environments, but they still have very creative ideas. I want to bring those to life, and that’s really what we’re trying to do,” said Ben James, CEO of 404-GEN, a startup based in Switzerland, in an interview with GamesBeat.

Now available as a Verified Solution in the Unity Asset Store, this integration introduces a new form of 3D representation based on Gaussian Splatting, a technique that brings decentralized content creation to Unity users, James said.

404-GEN uses a network of miners to produce the best 3D image.

As you can see from the images, Gaussian Splatts take a scan of an area and then generate a point cloud that can reproduce the place in 3D. It’s a computer graphics technique that creates photorealistic 3D scenes from a set of images or videos. It does so by using blending millions of tiny, translucent ellipsoids (Gaussian splats) to reproduce the scene. These splats — often used in reconstructing digital twins of real places — have data about the position, color, size, and transparency, which blend together to create a realistic 3D model.

Unity’s technical team rigorously evaluated the 404-Gen technology and recommended several enhancements, most notably flagging redeveloping certain AI dependencies to ensure commercial viability of the produced models.

In addition to these improvements, 404-GEN introduced a “digital handshake” to streamline both the creative and legal processes for developers, ensuring that models generated by decentralized miners are ready for commercial use. These improvements not only refine the technical framework but also facilitate a more seamless, one-click integration, making it accessible to developers across various skill levels.

“This integration is an important step for our ecosystem. It enables creators to work directly with decentralized 3D generation tools within the Unity editor, leveling the playing field for independent developers and designers,” said Ben James, founder of 404-GEN, in a statement. “Our technology bridges traditional game development and decentralized networks, offering a practical solution that meets the commercial standards expected in the industry.”

The company has nine employees.

By lowering technical barriers, the 404-GEN integration promotes experimentation with innovative 3D modeling techniques. This move highlights a growing openness among established Web2 companies to collaborate with decentralized subnets on Bittensor, ultimately broadening creative opportunities in game development and digital design.

Origins

Ben James is CEO of 404-GEN

The 404 name comes from the error when you hit a bad website. James was a fan of crypto and he got excited about the space. He read white papers and thought about how to incentivize different parties in a blockchain network the right way. And he thought of using the tech to democratize content creation.

James founded 404-GEN a couple of years ago with a focus on 3D imagery and AI. There are other competitors out there, but James believes the company is unique because of its decentralized model.

“When we generate 3d models, actually the code that we write as a company is really code that’s oriented around choosing the best 3D model for a particular prompt, whether it’s an image or a text prompt, in real time, algorithmically, so, using things like visual language models or different types of multimodal models to do that,” he said. “That means that there’s no one size fit all fits all solution here. There is no single foundation model, but, rather, what we rely on is the community of building on top of open source models. These are state of the art open source models in order to find the best solutions depending on what a particular user is looking for.”

The company is also unique in its focus on Gaussian Splats to do the 3D representation. The firm has nine employees now.

“We think that especially within the AI and the machine learning spaces, this has a tremendous potential trajectory, and within the gaming industry, it would allow things like lightweight three assets, so you can have more unique assets in a single scene,” James said. “You can convert them into meshes, which is the industry standard. But everything we generate is generated as a Gaussian splat.”

You can find 404-GEN media on the Unity store.

James said the decentralized nature of the platform allows for real-time, algorithmic generation of 3D models using visual language models and multimodal models. The company relies on a community of builders contributing to open-source models to find the best solutions for users.

With the decentralized model, the user’s prompts are sent off to miners in the ecosystem, and they generate multiple competing models. The company uses algorithms to choose the best model for the user, and high-quality models that are not selected are saved in an open-source dataset. The company incentivizes the miners with tokens, with the highest reward going to the one with the best results.

While the company is producing high-quality work, James believes that AI should augment the intuition of designers and artists, making their roles more creative.