At long last, OpenAI has released GPT-5. The new system abandons the distinction between OpenAI’s flagship models and its o series of reasoning models, automatically routing user queries to a fast nonreasoning model or a slower reasoning version. It is now available to everyone through the ChatGPT web interface—though nonpaying users may need to wait a few days to gain full access to the new capabilities.  It’s tempting to compare GPT-5 with its explicit predecessor, GPT-4, but the more illuminating juxtaposition is with o1, OpenAI’s first reasoning model, which was released last year. In contrast to GPT-5’s broad release, o1 was initially available only to Plus and Team subscribers. Those users got access to a completely new kind of language model—one that would “reason” through its answers by generating additional text before providing a final response, enabling it to solve much more challenging problems than its nonreasoning counterparts. Whereas o1 was a major technological advancement, GPT-5 is, above all else, a refined product. During a press briefing, Sam Altman compared GPT-5 to Apple’s Retina displays, and it’s an apt analogy, though perhaps not in the way that he intended. Much like an unprecedentedly crisp screen, GPT-5 will furnish a more pleasant and seamless user experience. That’s not nothing, but it falls far short of the transformative AI future that Altman has spent much of the past year hyping. In the briefing, Altman called GPT-5 “a significant step along the path to AGI,” or artificial general intelligence, and maybe he’s right—but if so, it’s a very small step. Take the demo of the model’s abilities that OpenAI showed to MIT Technology Review in advance of its release. Yann Dubois, a post-training lead at OpenAI, asked GPT-5 to design a web application that would help his partner learn French so that she could communicate more easily with his family. The model did an admirable job of following his instructions and created an appealing, user-friendly app. But when I gave GPT-4o an almost identical prompt, it produced an app with exactly the same functionality. The only difference is that it wasn’t as aesthetically pleasing.
Some of the other user-experience improvements are more substantial. Having the model rather than the user choose whether to apply reasoning to each query removes a major pain point, especially for users who don’t follow LLM advancements closely.  And, according to Altman, GPT-5 reasons much faster than the o-series models. The fact that OpenAI is releasing it to nonpaying users suggests that it’s also less expensive for the company to run. That’s a big deal: Running powerful models cheaply and quickly is a tough problem, and solving it is key to reducing AI’s environmental impact. 
OpenAI has also taken steps to mitigate hallucinations, which have been a persistent headache. OpenAI’s evaluations suggest that GPT-5 models are substantially less likely to make incorrect claims than their predecessor models, o3 and GPT-4o. If that advancement holds up to scrutiny, it could help pave the way for more reliable and trustworthy agents. “Hallucination can cause real safety and security issues,” says Dawn Song, a professor of computer science at UC Berkeley. For example, an agent that hallucinates software packages could download malicious code to a user’s device. GPT-5 has achieved the state of the art on several benchmarks, including a test of agentic abilities and the coding evaluations SWE-Bench and Aider Polyglot. But according to Clémentine Fourrier, an AI researcher at the company HuggingFace, those evaluations are nearing saturation, which means that current models have achieved close to maximal performance.  “It’s basically like looking at the performance of a high schooler on middle-grade problems,” she says. “If the high schooler fails, it tells you something, but if it succeeds, it doesn’t tell you a lot.” Fourrier said she would be impressed if the system achieved a score of 80% or 85% on SWE-Bench—but it only managed a 74.9%.  Ultimately, the headline message from OpenAI is that GPT-5 feels better to use. “The vibes of this model are really good, and I think that people are really going to feel that, especially average people who haven’t been spending their time thinking about models,” said Nick Turley, the head of ChatGPT. Vibes alone, however, won’t bring about the automated future that Altman has promised. Reasoning felt like a major step forward on the way to AGI. We’re still waiting for the next one.

After literally years of hype and speculation, OpenAI has officially launched a new lineup of large language models (LLMs), all different-sized variants of GPT-5, the long-awaited predecessor to its GPT-4 model from March of 2023, nearly 2.5 years ago. The company is rolling out four distinct versions of the model — GPT-5, GPT-5 Mini, GPT-5 Nano, and GPT-5 Pro — to meet varying needs for speed, cost, and computational depth.GPT-5 will soon be powering ChatGPT exclusively and replace all other models going forward for its 700 million weekly users, though ChatGPT Pro subscribers ($200) month can still select older models for the next 60 days.As per rumors and reports, OpenAI has replaced the previous system of having users switch the underlying model powering ChatGPT with an automatic router that decides to engage a special “GPT-5 thinking” mode with “deeper reasoning” that takes longer to respond on harder queries, or uses the regular GPT-5 or mini models for simpler queries.

Science

Published
7 August 2025

Authors
The Perch Team

Our new Perch model helps conservationists analyze audio faster to protect endangered species, from Hawaiian honeycreepers to coral reefs.One of the ways scientists protect the health of our planet’s wild ecosystems is by using microphones (or underwater hydrophones) to collect vast amounts of audio dense with vocalizations from birds, frogs, insects, whales, fish and more. These recordings can tell us a lot about the animals present in a given area, along with other clues about the health of that ecosystem. Making sense of so much data, however, remains a massive undertaking.Today, we are releasing an update to Perch, our AI model designed to help conservationists analyze bioacoustic data. This new model has better state-of-the-art off-the-shelf bird species predictions than the previous model. It can better adapt to new environments, particularly underwater ones like coral reefs. It’s trained on a wider range of animals, including mammals, amphibians and anthropogenic noise — nearly twice as much data in all, from public sources like Xeno-Canto and iNaturalist. It can disentangle complex acoustic scenes over thousands or even millions of hours of audio data. And it’s versatile, able to answer many different kinds of questions, from “how many babies are being born” to “how many individual animals are present in a given area.”In order to help scientists protect our planet’s ecosystems, we’re open sourcing this new version of Perch and making it available on Kaggle.

Perch not only recognizes the sound of bird species. Our new model was trained on a wider range of animals including mammals, amphibians and anthropogenic noise.

Success Stories: Perch in the FieldSince it was first launched in 2023, the initial version of Perch has already been downloaded over 250,000 times and its open-source solutions are now well-integrated into tools for working biologists. For example, Perch’s vector search library is now part of Cornell’s widely-used BirdNet Analyzer.In addition, Perch is helping BirdLife Australia and the Australian Acoustic Observatory build classifiers for a number of unique Australian species. For example, our tools enabled the discovery of a new population of the elusive Plains Wanderer.

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“This is an incredible discovery – acoustic monitoring like this will help shape the future of many endangered bird species.”

Paul Roe, Dean Research, James Cook University, Australia

Recent work has also found that the earlier version of Perch can be used to identify individual birds and track bird abundance, potentially reducing the need for catch-and-release studies to monitor populations.Finally, biologists from the LOHE Bioacoustics Lab at the University of Hawaiʻi have used it to monitor and protect populations of honeycreepers, which are important to Hawaiian mythology and face extinction from the threat of avian malaria spread by non-native mosquitoes. Perch helped the LOHE Lab find honeycreeper sounds nearly 50x faster than their usual methods, enabling them to monitor more species of honeycreeper over greater areas. We expect the new model will further accelerate these efforts.

Untangling the Planet’s PlaylistThe Perch model can predict which species are present in a recording, but that’s only part of the story: We also provide open-source tools that allow scientists to quickly build new classifiers starting from a single example and monitor species for which there is scarce training data or for very specific sounds like juvenile calls. Given one example of a sound, vector search with Perch surfaces the most similar sounds in a dataset. A local expert can then mark the search results as relevant or irrelevant to train a classifier.Together, this combination of vector search and active learning with a strong embedding model is called agile modeling. Our recent paper–”The Search for Squawk: Agile Modeling in Bioacoustics”–shows that this method works across birds and coral reefs, allowing the creation of high quality classifiers in under an hour.Looking ahead: the future of bioacousticsTogether, our models and methods are helping maximize the impact of conservation efforts, leaving more time and resources for meaningful, on-the-ground work. From the forests of Hawaiʻi to the reefs of the ocean, the Perch project showcases the profound impact we can have when we apply our technical expertise to the world’s most pressing challenges. Every classifier built and every hour of data analyzed brings us closer to a world where the soundtrack of our planet is one of rich, thriving biodiversity.

Learn more

AcknowledgementsThis research was developed by the Perch team: Bart van Merriënboer, Jenny Hamer, Vincent Dumoulin, Lauren Harrell, and Tom Denton, and Otilia Stretcu from Google Research. We also thank our collaborators Amanda Navine and Pat Hart at the University of Hawaiʻi, and Holger Klinck, Stefan Kahl and the BirdNet team at the Cornell Lab of Ornithology. And all our friends and collaborators whom we would have written about in this blog post if only we had another thousand words.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Five ways that AI is learning to improve itself Last week, Mark Zuckerberg declared that Meta aims to achieve smarter-than-human AI. He seems to have a recipe for achieving that goal, and the first ingredient is human talent: Zuckerberg has reportedly tried to lure top researchers to Meta Superintelligence Labs with nine-figure offers.  The second ingredient is AI itself.  Zuckerberg recently said on an earnings call that Meta will focus on building self-improving AI—systems that can bootstrap themselves to higher and higher levels of performance. He hopes to tap into a very real trend. Here are five ways that AI is already making itself better.
—Grace Huckins
The greenhouse gases we’re not accounting for Back in 2021, climate scientists noticed that levels of methane had soared in the atmosphere the previous year, rising at the fastest rate on record despite the global covid-19 lockdowns. Researchers eventually spotted a clear pattern: Methane emissions had increased sharply across the tropics, where wetlands were growing wetter and warmer.The findings offer one of the clearest cases so far where climate change itself is driving additional greenhouse-gas emissions from natural systems, triggering a feedback effect that threatens to produce more warming, more emissions, and on and on.  There’s now a major endeavor underway to better track and understand what’s going on. Read our story about it. —James Temple This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here. The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 The Trump administration’s punishing new tariffs have come into effect And prices are already climbing. (NYT $)+ Economists fear the US economy is poised to shrink. (WP $)+ Sweeping tariffs could threaten the US manufacturing rebound. (MIT Technology Review) 2 Sections of the US Constitution have been deleted onlinePassages about Congress’ powers and citizens’ unlawful detention have been scrubbed from the US government’s website. (TechCrunch)+ The Library of Congress blamed a coding error. (Ars Technica) 3 China is fighting a mosquito-borne virusIt’s deploying drones to search for standing water where the insects lay eggs. (AP News)+ Chikungunya virus is rarely fatal, but can cause fever and joint pain. (CNN)+ Authorities are taking a leaf out of their covid-fighting playbooks. (NYT $) 4 US federal agencies will have access to ChatGPT Enterprise For the grand sum of $1 a year. (Ars Technica)+ It won’t use workers’ data to train ChatGPT, apparently. (Bloomberg $)+ The news comes after major AI firms were greenlit as federal vendors. (Engadget) 5 Chinese drug discovery startups are striking deals with Big PharmaWestern pharmaceutical giants are confident they can deliver. (Rest of World)+ An AI-driven “factory of drugs” claims to have hit a big milestone. (MIT Technology Review) 6 Is it possible to build truly green AI data centers?The tech industry appears pretty hooked on fossil fuels. (FT $)+ We did the math on AI’s energy footprint. Here’s the story you haven’t heard. (MIT Technology Review) 7 The US is increasingly reliant on private companies for weather dataExperts are wary about losing access to vital tools. (Undark)+ How US research cuts are threatening crucial climate data. (MIT Technology Review)
8 Genetic factors could contribute to the risk of developing chronic fatigue syndromeIt’s the first robust evidence that genetics play a role. (New Scientist $) 9 An experimental pill is showing weight-loss promiseObese participants in Eli Lilly’s trial lost more than 12% of their body weight. (Wired $)+ We’re learning more about what weight-loss drugs do to the body. (MIT Technology Review)
10 Finding a job online is a nightmareSome companies are going back to basics to find the best recruits. (WSJ $) Quote of the day “We didn’t vote for ChatGPT.” —Virginia Dignum, a professor of responsible artificial intelligence at Sweden’s Umeå University, criticizes the country’s prime minister, Ulf Kristersson for admitting he regularly consults AI tools, the Guardian reports.
One more thing Why AI could eat quantum computing’s lunchTech companies have been funneling billions of dollars into quantum computers for years. The hope is that they’ll be a game changer for fields as diverse as finance, drug discovery, and logistics.But while the field struggles with the realities of tricky quantum hardware, another challenger is making headway in some of these most promising use cases. AI is now being applied to fundamental physics, chemistry, and materials science in a way that suggests quantum computing’s purported home turf might not be so safe after all. Read the full story. —Edd Gent

In the spring of 2021, climate scientists were stumped.  The global economy was just emerging from the covid-19 lockdowns, but for some reason the levels of methane—a greenhouse gas emitted mainly through agriculture and fossil-fuel production—had soared in the atmosphere the previous year, rising at the fastest rate on record. Researchers around the world set to work unraveling the mystery, reviewing readings from satellites, aircraft, and greenhouse-gas monitoring stations. They eventually spotted a clear pattern: Methane emissions had increased sharply across the tropics, where wetlands were growing wetter and warmer.  That created the ideal conditions for microbes that thrive in anaerobic muck, which gobbled up more of the carbon-rich organic matter and spat out more methane as a by-product. (Reduced pollution from nitrogen oxides, which help to break down methane in the atmosphere, also likely played a substantial role.)
The findings offer one of the clearest cases so far where climate change itself is driving additional greenhouse-gas emissions from natural systems, triggering a feedback effect that threatens to produce more warming, more emissions, and on and on.  There are numerous additional ways this is happening or soon could, including wildfires and thawing permafrost. These are major emissions sources that aren’t included in the commitments nations have made under the Paris climate agreement—and climate risks that largely aren’t accounted for in the UN Intergovernmental Panel on Climate Change’s most recent warming scenarios.
Spark Climate Solutions (not to be confused with this newsletter) hopes to change that.The San Francisco nonprofit is launching what’s known as a model intercomparison project, in which different research teams run the same set of experiments on different models across a variety of emissions scenarios to determine how climate change could play out. This one would specifically explore how a range of climate feedback effects could propel additional warming, additional emissions, and additional types of feedback. “These increased emissions from natural sources add to human emissions and amplify climate change,” says Phil Duffy, chief scientist at Spark Climate Solutions, who previously served as climate science advisor to President Joe Biden. “And if you don’t look at all of them together, you can’t quantify the strength of that feedback effect.” Other participants in the effort will include scientists at the Environmental Defense Fund, Stanford University, the Woodwell Climate Research Center, and other institutions in Europe and Australia, according to Spark Climate Solutions. The nonprofit hopes to publish the findings in time for them to be incorporated into the UN climate panel’s seventh major assessment report, which is just getting underway, to help ensure that these dangers are more fully represented. That, in turn, would give nations a more accurate sense of the world’s carbon budgets, or the quantity of greenhouse gases they can produce before the planet reaches temperatures 1.5 °C or  2 °C over preindustrial levels.  But one thing is already clear: Since the current scenarios don’t fully account for these feedback effects, the world will almost certainly warm faster than is now forecast, which underscores the importance of carrying out this exercise.  Scientists at EDF, Woodwell and other institutions found that fires in the world’s northernmost forests, thawing permafrost and warming tropical wetlands could together push the planet beyond 2 °C years faster, eliminating up to a quarter of the time left before the world passes the core goal of the Paris agreement, in a paper under review.  Earlier this year, Spark Climate Solutions set up a broader program to advance research and awareness of what’s known as warming-induced emissions, which will launch additional collaborations similar to the modeling intercomparison project.   The goal of the program and the research project is “to really mainstream the inclusion of this topic in climate science and climate policy, and to drive research around climate solutions,” says Ben Poulter, who leads the program at Spark Climate Solutions and was previously a scientist at the NASA Goddard Space Flight Center.

Spark notes that warming temperatures could also release more carbon dioxide from the oceans, in a process known as outgassing; additional carbon dioxide and nitrous oxide, a potent greenhouse gas that also depletes the protective ozone layer, from farmland; more carbon dioxide and methane from wildfires; and still more of all three of these gases as permafrost thaws. The ground remains frozen year round across a vast expanse of the Northern Hemisphere, creating a frosty underground storehouse from Alaska to Siberia that’s packed with twice as much carbon as the atmosphere. But as it thaws, it starts to decompose and release greenhouse gases, says Susan Natali, an Arctic climate scientist focused on permafrost at Woodwell. A study published in Nature in January noted that 30% of the world’s Arctic–Boreal Zone has already flipped from a carbon sink to a carbon source, when wildfires, thawing permafrost and other factors are taken into account. Despite these increasing risks, only a minority of the models that fed into the UN climate panel’s last major report incorporated the feedback effects of thawing permafrost. And the emissions risks still weren’t fully accounted for because these ecosystems are difficult to monitor and model, Natali says.Among the complexities: Wildfires, which are themselves hard to predict, can accelerate thawing. It’s also hard to foresee which regions will grow drier or wetter, which determines whether they release mostly methane or carbon dioxide—and those gases have very different warming effects over different time periods. There are counterbalancing effects that must be taken into account as well—for instance, as carbon-absorbing plants replace ice and snow in certain areas. Natali says improving our understanding of these complex feedback effects is essential to understanding the dangers we face. “It’s going to mean additional costs to human health, human life,” she says. “We want people to be safe—and it’s very hard to do that if you don’t know what’s coming and you’re not prepared for it.” This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.

But despite achieving technical benchmarks on par with OpenAI’s other powerful proprietary AI model offerings, the broader AI developer and user community’s initial response has so far been all over the map. If this release were a movie premiering and being graded on Rotten Tomatoes, we’d be looking at a near 50% split, based on my observations. First some background: OpenAI has released these two new text-only language models (no image generation or analysis) both under the permissive open source Apache 2.0 license — the first time since 2019 (before ChatGPT) that the company has done so with a cutting-edge language model. The entire ChatGPT era of the last 2.7 years has so far been powered by proprietary or closed-source models, ones that OpenAI controlled and that users had to pay to access (or use a free tier subject to limits), with limited customizability and no way to run them offline or on private computing hardware.