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2. februar 2025

Utgivelse

Vi introduserer Deep Research

En agent som bruker resonnering for å sammenfatte store mengder informasjon fra nettet og utføre komplekse research-oppgaver for deg. Tilgjengelig for Pro-brukere i dag. Plus- og Team-brukere kommer snart.

Laster inn …

Oppdatering 10. februar 2026: Du kan nå koble dyp forskning til hvilken som helst MCP eller app og begrense nettsøk til pålitelige nettsteder, slik at du kan fokusere på autentiserte, bransjestandardkilder. Du kan nå også spore fremdriften i sanntid og avbryte for å forbedre med oppfølgingsspørsmål eller nye kilder. Vi har oppdatert den visuelle opplevelsen slik at det er enklere å starte, spore og gjennomgå forskningen din fra start til slutt.


Oppdatering 17. juli 2025: Dyp forskning kan nå gå enda dypere og bredere med tilgang til en visuell nettleser som en del av ChatGPT‑agenten. For å få tilgang til disse oppdaterte funksjonene, velger du “agentmodus” fra rullegardinmenyen i oppretteren, og skriver inn forespørselen din direkte. Den opprinnelige funksjonaliteten for dyp forskning er fortsatt tilgjengelig via “dyp forskning”-alternativet i verktøymenyen.


Oppdatering 24. april 2025: Vi øker betydelig hvor ofte du kan bruke dyp forskning–Plus-, Team-, Enterprise- og Edu-brukere får nå 25 forespørsler per måned, Pro-brukere får 250, og Free-brukere får 5. Dette er mulig takket være en ny lettversjon av dyp forskning, drevet av en versjon av o4-mini, som er designet for å være mer kostnadseffektiv samtidig som den bevarer høy kvalitet. Når du når grensen for fullversjonen, vil forespørslene dine automatisk gå over til lettversjonen.


Oppdatering 25. februar 2025: Alle Plus-brukere kan nå bruke dyp forskning.


Oppdatering 5. februar 2025: Dyp forskning er nå tilgjengelig for Pro-brukere i Storbritannia, Sveits og EØS.


I dag lanserer vi Deep Research i ChatGPT – en ny agentbasert funksjon som utfører flertrinns research på internett for komplekse oppgaver. Den bruker bare noen titalls minutter på noe som ville tatt en person mange timer.

Deep Research er OpenAIs neste agent som kan gjøre jobben for deg – du sender inn en melding, og ChatGPT finner, analyserer og sammenfatter hundrevis av kilder for å opprette en helhetlig rapport, omtrent som en forskningsanalytiker. Den er drevet av en kommende versjon av OpenAI o3‑modellen, som er optimalisert for nettsøk og dataanalyse. Den bruker resonnering for å søke etter, tolke og analysere store mengder tekst, bilder og PDF-er på nettet, og tilpasser seg fortløpende ut fra informasjonen den møter.

Evnen til å syntetisere kunnskap er en forutsetning for å skape ny kunnskap. Derfor representerer Deep Research et viktig skritt i retning av det overordnede målet om å utvikle AGI – som vi lenge har sett for oss som noe som kan produsere ny vitenskapelig forskning.

Derfor bygde vi Deep Research

Deep Research er utviklet for folk i yrker som krever inngående kunnskap innen områder som finans, vitenskap, politikk og ingeniørfag – og som trenger grundig, presis og pålitelig research. Det er like nyttig for kresne forbrukere som ønsker svært personlig tilpassede anbefalinger i kjøpsprosesser som vanligvis krever at man gjør mye research – som biler, hvitevarer og møbler. Alle utdata er grundig dokumentert, med tydelige kildehenvisninger og et sammendrag av tanken bak, slik at det er lett å sjekke opp og bekrefte informasjonen. Funksjonen er spesielt effektiv når det gjelder å finne smal, lite intuitiv informasjon som ellers ville krevd å bla gjennom mange nettsteder. Deep Research frigjør verdifull tid ved å gjøre det mulig å utføre tidkrevende og kompleks research på nett med bare én forespørsel.

Deep Research oppdager, resonnerer over og sammenfatter innsikt fra Internett – helt på egen hånd. For å få til dette ble modellen lært opp med faktiske oppgaver som krever bruk av nettleser- og Python-verktøy, ved hjelp av de samme metodene for forsterkende læring som ligger bak OpenAI o1, den første resonneringsmodellen. Mens o1 demonstrerer imponerende evner innen koding, matematikk og andre tekniske domener, krever mange reelle utfordringer grundig kontekst og innhenting av informasjon fra ulike kilder på nett. Deep Research bygger videre på disse resonneringsegenskapene for å tette dette gapet, og for å kunne håndtere utfordringer folk støter på i arbeids- og dagliglivet.

Slik bruker du Deep Research

I ChatGPT velger du «Deep Research» i meldingsfeltet og skriver inn forespørselen din. Fortell ChatGPT hva du trenger – enten det er en analyse av konkurrenter innen strømmetjenester eller en personlig tilpasset rapport om den beste bysykkelen. Du kan legge ved filer eller regneark for å gi mer kontekst til forespørselen. Når Deep Research starter, vises et sidefelt med et sammendrag av trinnene som utføres og kildene som brukes.

Deep Research kan bruke alt fra 5 til 30 minutter på å gjøre ferdig jobben, det tar den tiden som trengs for å gå i dybden på nettet. I mellomtiden kan du jobbe eller gjøre andre ting – du får et varsel når Deep Research er ferdig utført. De endelige utdataene leveres som en rapport i chatten – i løpet av de neste ukene skal vi også legge til innebygde bilder, datavisualiseringer og andre analytiske utdata i disse rapportene for ekstra klarhet og kontekst.

Sammenlignet med Deep Research er 4o ideell for samtaler i sanntid med både tekst, bilde og lyd. For komplekse og domenespesifikke forespørsler der dybde og detaljer er avgjørende, gjør evnen til å gjennomføre omfattende utforsking og dokumentere hver påstand at Deep Research utgjør forskjellen mellom et raskt sammendrag og et godt dokumentert, verifisert svar du kan bruke som arbeidsprodukt.

GPT-4o

Help me find iOS and android adoption rates, % who want to learn another language, and change in mobile penetration, over the past 10 years, for top 10 developed and top 10 developing countries by GDP. Lay this info out in a table and separate stats into columns, and include recommendations on markets to target for a new iOS translation app from ChatGPT, focusing on markets ChatGPT is currently active in.

Compiling comprehensive data on iOS and Android adoption rates, the percentage of populations interested in learning another language, and changes in mobile penetration over the past decade for the top 10 developed and developing countries by GDP is challenging due to data limitations. However, based on available information, we can provide insights and recommendations for targeting markets with a new iOS translation app powered by ChatGPT.

Top 10 Developed Countries by GDP:

  1. United States
  2. Japan
  3. Germany
  4. United Kingdom
  5. France
  6. Italy
  7. Canada
  8. South Korea
  9. Australia
  10. Spain

Top 10 Developing Countries by GDP:

  1. China
  2. India
  3. Brazil
  4. Russia
  5. Indonesia
  6. Mexico
  7. Turkey
  8. Saudi Arabia
  9. Argentina
  10. South Africa

Insights:

  • iOS and Android Adoption Rates: Globally, Android holds a dominant market share of approximately 70.93%, while iOS accounts for about 28.32% as of 2024. In the United States, iOS leads with a 58.81% market share. Other developed countries like Japan, Canada, Australia, and the United Kingdom also show a higher preference for iOS devices. In contrast, developing countries such as India and Brazil have a higher prevalence of Android devices.
  • Interest in Language Learning: While specific percentages of populations interested in learning another language are not readily available, the global popularity of language learning apps like Duolingo, which had approximately 14.3 million downloads in July 2024, indicates a significant interest. This interest is likely higher in countries with diverse linguistic landscapes or where English is not the primary language.
  • Mobile Penetration Over the Past Decade: Mobile phone ownership has seen substantial growth globally. In the United States, smartphone ownership increased from 35% in 2011 to 91% in 2024. Developing countries have also experienced significant growth in mobile penetration, contributing to increased access to digital services.

Recommendations:

Considering the available data and focusing on markets where ChatGPT is currently active, the following developed countries are promising targets for a new iOS translation app:

  • United States: High iOS market share and significant interest in language learning.
  • United Kingdom: Substantial iOS user base and a multicultural population with diverse language needs.
  • Canada: Notable iOS adoption and a bilingual population (English and French), increasing the demand for translation services.

Among developing countries, the following are noteworthy:

  • China: Despite Android's dominance, the large population and growing interest in English and other languages present a significant opportunity.
  • India: High Android usage, but a growing middle class with increasing iOS adoption and a strong interest in language learning, especially English.
  • Brazil: Predominantly Android users, but with a sizable market for language learning apps, particularly for English and Spanish.

Focusing on these markets aligns with ChatGPT's current availability and leverages existing mobile adoption trends and language learning interests.

Deep Research

Help me find iOS and android adoption rates, % who want to learn another language, and change in mobile penetration, over the past 10 years, for top 10 developed and top 10 developing countries by GDP. Lay this info out in a table and separate stats into columns, and include recommendations on markets to target for a new iOS translation app from ChatGPT, focusing on markets ChatGPT is currently active in.

Top 10 Developed Economies (by GDP)Smartphone OS Share, Language-Learning Interest, and Mobile Penetration Growth

Country

iOS Market Share

Android Market Share

Interest in New Language

(% of population)

Mobile Penetration Change (2013→2023)

United States

(USA)

~58% iOS vs 42% Android

(see left)

High:

70% regret losing foreign-language ability (indicating strong desire to improve)

56%→82%

smartphone penetration (↑26 pp)

Japan

~69% iOS vs 31% Android

(see left)

High:

Over 50% want to learn English

~30%→79%

(approx.) (↑~50 pp)

Germany

~39% iOS vs 60% Android

(see left)

High:

86% of Europeans say everyone should speak ≥1 foreign language (strong pro-language attitude)

40%→82%

(↑42 pp)

United Kingdom

(UK)

~52% iOS vs 47% Android

(see left)

Moderate-High:

~73% have some interest (only 27% “no interest” in new languages)

62%→82%

(↑20 pp)

France

~35% iOS vs 64% Android

(see left)

High:

86% (EU average) favor multilingualism; English widely seen as important

42%→83%

(↑40 pp)

Italy

~30% iOS vs 69% Android

(see left)

High:

86% (EU avg.) favor language learning; 1 in 4 regret not learning another

41%→85%

(↑44 pp)

Canada

~60% iOS vs 40% Android

(see left)

Moderate:

Many bilingual (English/French); rising interest in third languages (no recent % data)

56%→~80%

(↑ ~24 pp) (est., similar to US)

South Korea

~24% iOS vs 76% Android

(see left)

Moderate:

Strong English education focus; ~40% of teens are learning English

73%→~95%

(↑ ~22 pp) – already very high by 2013 (near saturation now)

Australia

~55% iOS vs 45% Android

(see left)

Moderate:

~70% see learning languages as valuable (English dominant but interest in Asian languages growing)

65%→~85%

(↑ ~20 pp) (est., high smartphone use)

Spain

~20% iOS vs 79% Android

(see left)

High:

88% of Spaniards learned a foreign language in school (e.g. English); strong cultural interest

55%→~85%

(↑ ~30 pp) (est., nearing Western Europe peers)

Top 10 Developing/Emerging Economies (by GDP)(ChatGPT availability considered)

Country

iOS Market Share

Android Market Share

Interest in New Language

(notable stats)

Mobile Penetration Change (2013→2023)

China

^(ChatGPT restricted)

~24% iOS vs 75% Android

(see left)

Very High:

Hundreds of millions learning English (but ChatGPT not officially accessible)

47%→68%

(↑21 pp)

India

~4% iOS vs 95% Android

(see left)

High:

English compulsory in many schools; strong economic motivation (80% link English to higher income)

~5%→46%

(↑ ~41 pp) (huge jump in smartphone adoption)

Brazil

~18% iOS vs 82% Android

(see left)

High:

Great demand for English – only ~5% speak it, but most see it as key skill

26%→67%

(↑40 pp)

Russia

^(ChatGPT limited)

~30% iOS vs 69% Android

(see left)

Moderate:

English learning common (compulsory in school); interest exists but ChatGPT availability is uncertain

36%→74%

(↑38 pp)

Mexico

~24% iOS vs 76% Android

(see left)

High:

Strong desire to learn English for US job/migration; ~50% of students study English in school (est.)

37%→62%

(↑25 pp)

Indonesia

~12% iOS vs 88% Android

(see left)

High:

English seen as gateway skill; government programs to improve English proficiency

<30%→68%

(↑ ~40 pp)

Turkey

~15% iOS vs 85% Android (est.)

(see left)

High:

English highly valued (universal in education);

80%

link English to better jobs

30%→75%

(↑45 pp)

Saudi Arabia

~24% iOS vs 76% Android

(see left)

Moderate:

English widely taught; 92% smartphone ownership (many already bilingual in English/Arabic)

73%→~95%

(↑ ~22 pp) (early adopter; near saturation now)

South Africa

~16% iOS vs 83% Android

(see left)

High:

Multilingual society; English and Afrikaans taught alongside native languages – cultural impetus to learn others

40%→~60%

(↑ ~20 pp) (est., steady growth)

Argentina

~12% iOS vs 88% Android

(see left)

High:

Strong interest in English (Argentina leads Latin America in English proficiency)

31%→~70%

(↑ ~39 pp) (est., sharp rise with smartphone adoption)

Sources: Smartphone OS shares are from StatCounter/Statista data (2023–24). Language-interest metrics come from surveys by Preply, Eurobarometer, Babbel, etc. (e.g. UK 73% interest; US 70% regret lack of languages; Japan >50% want English; Pearson global survey). Mobile penetration is measured as smartphone ownership percentage – dramatic growth is noted in developing markets (e.g. Brazil +40 points) and high saturation in developed ones (e.g. UK ~82%) over the last decade.


Recommendations: Based on the above, prime targets for a new ChatGPT‑powered iOS translation app would be countries with large iOS user bases and strong language-learning demand, where ChatGPT is accessible. Notably, Japan, the U.S., UK, Canada, and Australia emerge as top choices. These markets pair high iOS adoption (50–60%+ share) with significant enthusiasm for language learning – for example, Japan’s iPhone dominance and widespread desire to learn English make it ideal. English-speaking countries like the US, UK, Canada, and Australia, despite being monolingual societies, show substantial interest in learning other languages (e.g. Spanish or French), and all have over 80% smartphone penetration and millions of iPhone users.

Among emerging economies, Mexico and Brazil stand out as secondary targets: although Android prevails, their sheer population sizes mean tens of millions of iPhone users exist, and interest in English is very high for career and education reasons. These Latin American markets (where ChatGPT is active) could yield strong adoption for an iOS-only app, especially given the motivation to learn English. Conversely, China and Russia are less feasible – China’s OS split skews Android and ChatGPT is not officially available there, and Russia faces access and regulatory uncertainties.

In summary, focus on iOS-heavy, pro-learning markets: Japan and Western countries (US, UK, Canada, Australia, plus high-income Europe like Germany and France to a lesser degree) should be the first-tier targets. They combine robust iOS usage with favorable language-learning attitudes. A second tier includes large-population developing markets (Mexico, Brazil, perhaps Saudi Arabia for its historically high iPhone uptake) where interest in English is strong – these can be approached once the app gains traction in the primary markets. Such a strategy ensures the app launches where it has the greatest alignment of platform (iOS) presence and user demand for translation/language tools, maximizing the likelihood of adoption and growth.

Deep Research svarer på meldingen på en svært detaljert måte, og gir sammenlignbare, landspesifikke data for både de 10 mest og de 10 minst utviklede landene innenfor det aktuelle emnet. Den bruker informasjonen til å gi brukeren detaljerte råd om markeder å gå inn i, som er både informerte og anvendelige.

Slik fungerer det

Deep Research ble lært opp med helhetlig forsterkende læring på krevende nettlesings- og resonneringsoppgaver på tvers av ulike domener. Gjennom denne opplæringen lærte den å planlegge og gjennomføre en prosess med flere trinn for å finne dataene den trenger, med tilbakesporing og tilpasning til informasjon i sanntid der det trengs. Modellen kan også bla gjennom filer opplastet av brukere, lage og videreutvikle grafer med Python-verktøyet, bygge inn både genererte grafer og bilder fra nettsteder i svarene sine og sitere spesifikke setninger eller utdrag fra kilder. Som et resultat av denne opplæringen presterer den svært godt i flere offentlige evalueringer med fokus på virkelige problemer.

Humanity's Last Exam

I evalueringen Humanity’s Last Exam(åpnes i et nytt vindu), en nylig lansert evaluering som tester KI med ekspertspørsmål innen et bredt spekter av fagområder, scorer modellen som driver Deep Research høyere enn noensinne med 26,6 % nøyaktighet. Testen består av over 3000 flervalgsspørsmål og korte svar i fritekst innen mer enn 100 fagområder, fra lingvistikk til romteknologi, klassiske fag til økologi. Sammenlignet med OpenAI o1 er de største forbedringene innen kjemi, humanistiske fag og samfunnsvitenskap, samt matematikk. Modellen som driver Deep Research viste en menneskelignende tilnærming ved å effektivt finne frem til spesialisert informasjon når det var nødvendig.

ModellNøyaktighet (%)
GPT-4o3,3
Grok-23,8
Claude 3.5 Sonnet4,3
Gemini-tenkning6,2
OpenAI o19,1
DeepSeek-R1*9,4
OpenAI o3-mini (medium)*10,5
OpenAI o3-mini (high)*13,0
OpenAI grundig research**26,6
* Modellen er ikke multimodal, evaluert på tekstbasert delsett.
** med nettlesing + python-verktøy

GAIA

I GAIA(åpnes i et nytt vindu)1, en offentlig referansemåling som evaluerer KI med virkelige spørsmål, oppnår modellen som driver Deep Research en ny SOTA-status og er på førsteplass på den eksterne resultatlisten(åpnes i et nytt vindu). Oppgavene dekker spørsmål på tre ulike vanskelighetsnivåer, og å fullføre oppgavene krever ferdigheter som resonnering, multimodal flyt, søk på nett og kunnskap om bruk av verktøy.

GAIA
Nivå 1Nivå 2Nivå 3Gj.snitt
Forrige SOTA(åpnes i et nytt vindu)67,9267,4442,3163,64
Grundig research (pass@1)74,2969,0647,667,36
Grundig research (cons@64)78,6673,2158,0372,57

Eksempler på GAIA-oppgaver

In Nature journal's Scientific Reports conference proceedings from 2012, in the article that did not mention plasmons or plasmonics, what nano-compound is studied? Don't use the prefix nano in your answer if there is one.

[The remaining Chain-of-Thought messages and the final answer are hidden to protect the benchmark's ground truth answer.]

Clarifying the search

I’m piecing together the request for an article from 2012 in Scientific Reports that details a nano-compound, excluding any related to plasmonics. Likely, this involves filtering specific topics or compounds, like silver or graphene.

Piecing together details

I'm sub-searching "Scientific Reports 2012 nanoparticle," likely seeking nano-compounds. Interested in filtering out plasmon-related topics, possibly focusing on silver, graphene, or quantum dots.

[search action]

Navigating search filters

I’m digging into advanced search filters on nature.com and Scientific Reports for 2012, trying to pinpoint articles on amorphous carbon nanoparticles in food caramels.

Identifying conference proceedings

The search results reveal a conference proceeding titled "2D quasiperiodic plasmonic crystals" from 2012, likely found under the "Conference Proceedings" category in Scientific Reports.

[browse action]

Reflecting on potential issues

I'm considering the reasons behind a web page misconception, likely due to an outdated agent type or inadvertent blockage.

[browse action]

Considering dynamic options

I'm thinking about a nature site with dynamic content or a cached version. Alternatively, trying a search for specific phrases might help.

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Pinpointing key features

OK, I'm identifying four conference proceedings from 2012 in Sci Rep. Notably, some mention "plasmonic" and should be excluded.

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Piecing together insights

I’m gathering info on accessing cached versions or snippet expansions for specific titles to ensure comprehensive analysis.

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Pulling together the details

I’m noting the progression from the 2012 European Materials Research Society Spring Meeting to the scientific report, focusing on conference proceedings. This approach helps establish context and relevance.

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Tracking journal clues

Trying to connect conference proceedings from late 2012 with "Scientific Reports" journal, focusing on plasmonic 2D quasiperiodic materials. This approach feels methodical and precise.

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Analyzing event details

Noting that the search results [8] include a page likely summarizing the European Materials Research Society 2012 Spring Meeting, including 25 symposia.

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Piecing together research

I’m examining a likely collection page with 4 articles, considering plasmonic and metamaterial topics, and identifying key references from the European Materials Research Society 2012 Spring Meeting.

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Pinpointing the focus

I’m focusing on the 2012 conference proceedings in "Scientific Reports" from E-MRS, likely involving topics like "2D quasiperiodic plasmonic crystals" and "Layered plasmonic cloaks to tailor the optical scattering at the nanoscale."

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Evaluating library sources

Thinking about special issue E-MRS 2012 Sci rep invited paper Monticone and metamaterials lab at News Archives – 2012.

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Assessing accessibility

I'm gathering info on potential site access issues, considering outdated status or blockages. Also, evaluating the Monticone site for a list reference.

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Piecing together E-MRS 2012

I'm trying to link "Layered Plasmonic Cloaks" to E-MRS 2012. Considering other possible topics like graphene and catalysis.

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Identifying relevant topics

I’m gathering info on topics like graphene, photovoltaics, and nanostructured compounds, while excluding plasmonic-related ones. This leads me to focus on nanotechnology aspects more broadly.

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Adjusting my focus

I’m broadening the search parameters by entering specific site and scholar identifiers to pinpoint articles about 2D quasiperiodic plasmonic crystals in Nature Scientific Reports from 2012.

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Identifying non-plasmonic possibilities

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I'm analyzing interactions in a graphene superlattice, likely from a Sci Rep article. Considering titles by authors like Padooru, Yakovlev, Chen, and Alu.

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I’m gathering info on E-MRS 2012 Spring Meeting's Sci Rep proceedings, focusing on invited papers, without plasmonic phenomena. Types under review are graphene, catalysis, energy materials, nanomedicine, and sensors.

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Oppgaver på ekspertnivå

I en intern evaluering av oppgaver på ekspertnivå innen ulike områder ble grundig research vurdert av domeneeksperter til å ha automatisert mange timer med vanskelige, manuelle undersøkelser. 

Andel bestått vs. maks verktøybruk
Andel beståttMaks verktøybruk
Jo mer modellen søker og tenker på det den søker etter, desto bedre gjør den det, og derfor er det viktig å gi den tid til å tenke.
Eksempler på oppgaver på ekspertnivå
Tid spart på oppgaven: Fire timer

I am a researcher interested in bringing mixed-gas sorption capabilities to my lab. Please discuss the differences between pure- and mixed-gas sorption for glassy polymers, how the dual-mode sorption model can be used to predict mixed-gas sorption behavior in glassy polymers (include equations where applicable), and what challenges there are in accurately predicting pure- and mixed-gas sorption using the dual-mode sorption model. Only include open-source information as of October 1, 2024.

Pure vs. mixed-gas sorption in glassy polymers: In a glassy polymer (below TgT_g), a single gas typically exhibits a concave sorption isotherm: uptake rises sublinearly with pressure, unlike the linear Henry’s-law behavior above TgT_g. This is attributed to “dual-mode” sorption: some penetrant dissolves in the dense polymer matrix (obeying Henry’s law) while additional penetrant saturates specific microvoid “sites” (a Langmuir-type adsorption in the polymer’s nonequilibrium free volume). By contrast, when two or more gases are present, they compete for those limited Langmuir sorption sites. As a result, each component’s sorbed concentration in a mixture is generally lower than it would be at the same partial pressure alone. In particular the less soluble (lower-affinity) gas tends to be more strongly suppressed, often causing the mixed-gas solubility selectivity (ratio of concentrations) to exceed the ideal value predicted from pure-gas isotherms. For example, in CO₂/CH₄ mixtures both components sorb less than in their pure-gas cases (especially CH₄), so the CO₂/CH₄ sorption selectivity is higher under mixture conditions. In extreme cases, mixed-gas effects can even reverse performance trends observed with single gases. (We note that unlike competitive sorption, synergistic sorption – where one gas increases the sorption of another via polymer swelling – is usually minor in rigid high-free-volume polymers under moderate pressures.)

Dual-mode sorption model and mixed-gas prediction: The dual-mode sorption (DMS) model quantitatively captures glassy polymer sorption by superposing a Henry’s-law term and a Langmuir term. For a single gas, the sorbed concentration CC (e.g. in cm³(STP) gas per cm³ polymer) is written as:

C(p)=kDp  +  CHbp1+bp.C(p) = k_D\,p \;+\; \frac{C'_H\,b\,p}{\,1 + b\,p\,}\,.

Here kDk_D is the Henry’s law coefficient for dissolution, CHC'_H is the Langmuir capacity constant (proportional to the polymer’s unrelaxed free-volume or “hole” capacity), and bb is the Langmuir affinity constant. This empirical formula yields the characteristic concave isotherm of glassy polymers and reduces to simple Henry’s law at the glass-transition (since CH0C'_H \to 0 as TTgT \to T_g). The same three parameters (kD,CH,bk_D, C'_H, b) are obtained by fitting pure-gas sorption data and are then used to predict mixture sorption. For a binary mixture of gases A and B (with partial pressures pA,pBp_A, p_B), the DMS model assumes each gas obeys its own Henry’s term and that both compete for the same Langmuir sites. One convenient form is to write the total sorbed concentration as the sum of contributions from each gas:

Ctot=kD,ApA+kD,BpB  +  CH,AbApA  +  CH,BbBpB1+bApA+bBpB,C_{\text{tot}} = k_{D,A}p_A + k_{D,B}p_B \;+\; \frac{\,C'_{H,A}\,b_A\,p_A \;+\; C'_{H,B}\,b_B\,p_B\,}{\,1 + b_A p_A + b_B p_B\,}\,,

which implicitly means the concentration of component i in the mixture is Ci=kD,ipi+CH,ibipi1+jbjpjC_i = k_{D,i} p_i + \frac{C'_{H,i} b_i p_i}{\,1+\sum_j b_j p_j\,}. This reduces to the single-gas formula when the other component’s pressure is zero. In practice, partial pressure in these equations is often replaced by fugacity for accuracy at high pressures, though using fugacities or pressures gives nearly the same results if applied consistently. The DMS mixed-gas model predicts the competitive sorption effect observed experimentally – namely, each gas’s uptake is depressed relative to its pure-gas sorption isotherm.

Challenges and limitations: Although the dual-mode model is widely successful in correlating sorption data, it has notable limitations in predicting sorption behavior. First, the DMS model is empirical – its fitted parameters are not universal constants but can vary with polymer formation history, physical aging, and temperature/pressure range. This means a parameter set obtained under one set of conditions may lack predictive power outside its calibration range. For instance, sorption isotherms measured only over a limited pressure range can be fit well by DMS, but extrapolating beyond that (especially approaching plasticization pressures) may be unreliable. Indeed, multiple distinct combinations of kDk_D, CHC'_H, bb can sometimes fit the same pure-gas isotherm almost equally well – yet they yield markedly different mixed-gas predictions. This sensitivity undermines confidence in mixture forecasts, as one study showed that using different pure-gas fit sets led to only qualitative agreement with some mixed-CO₂/CH₄ data.

Furthermore, DMS inherently assumes a fixed population of Langmuir sites (microvoids) and ignores polymer relaxation or swelling induced by sorbate. In reality, penetrant-induced swelling (plasticization) can increase free volume and sorption capacity, especially for highly condensable gases at high activity – an effect the basic DMS model cannot capture. In binary sorption, this means any synergistic increase in sorption due to a second gas (or due to polymer dilation) is unaccounted for in the standard model. Conversely, the model also cannot describe sorption isotherm shapes that deviate from concave Langmuir-type behavior (e.g. sigmoidal isotherms caused by specific polymer-penetrant interactions or clustering). For example, glassy polymers absorbing polar molecules like alcohols can show sigmoidal sorption isotherms, which the simple dual-mode framework fails to represent. Finally, accurately measuring mixed-gas sorption itself is challenging, and discrepancies between idealized model assumptions (e.g. truly zero downstream pressure, no competitive diffusion limitations) and experimental realities can add to prediction error. Despite these challenges, the dual-mode sorption model – with appropriate care – remains a valuable first approximation for interpreting sorption in glassy polymers, and it highlights the key role of competitive site occupancy in mixed-gas environments. Researchers often turn to more elaborate models (e.g. non-equilibrium lattice fluid or thermodynamic theories) when the dual-mode model’s assumptions break down, but such models come at the cost of additional parameters or complexity. In summary, dual-mode sorption provides a useful baseline for pure- and mixed-gas sorption in glassy polymers, but one must be mindful of its empirical nature and the above limitations when predicting multicomponent sorption behavior in practice.

Sources: Dual-mode sorption model and mixed-gas extension; competitive sorption effects in mixtures; model equations and parameters; limitations of DMS model; parameter sensitivity and predictivity issues.

Understanding sorption models

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Addressing dual sorption model challenges

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Andel bestått for oppgaver på ekspertnivå etter anslått økonomisk verdi
Andel beståttAnslått økonomisk verdi
Andel bestått for oppgaver på ekspertnivå etter anslått antall timer
Andel beståttAnslått antall timer
Den anslåtte økonomiske verdien av en oppgave er mer korrelert med andelen bestått enn antallet timer det ville tatt et menneske å utføre den. Det som er vanskelig for modeller er ikke nødvendigvis det samme som det som er tidkrevende for mennesker.

Begrensninger

Deep Research åpner for nye muligheter, men teknologien er fortsatt i en tidlig fase og har visse begrensninger. Den kan av og til hallusinere fakta i svarene sine eller trekke feilaktige slutninger – men i betydelig mindre grad enn tidligere ChatGPT‑modeller, ifølge interne evalueringer. Den kan streve med å skille mellom offisiell informasjon og rykter, og viser for øyeblikket svakheter i kalibrering av modellens selvtillit, ofte uten å formidle dette presist. Ved lansering kan det forekomme mindre formateringsfeil i rapporter og sitater, og det kan ta lengre tid før oppgaver starter. Vi forventer at alle disse problemene vil bedre seg raskt med mer bruk og tid.

Tilgang

Deep Research i ChatGPT krever for øyeblikket svært mye datakraft. Det kreves mer regnekraft jo lengre tid det tar å behandle en forespørsel. Vi starter med en versjon som er optimalisert for Pro-brukere i dag, med opptil 100 forespørsler i måneden. Plus- og Team-brukere får tilgang neste i neste omgang, deretter Enterprise. Vi jobber fortsatt med å gi tilgang til brukere i Storbritannia, Sveits og EØS. 

Alle betalende brukere vil snart få betydelig høyere grenseverdier når vi lanserer en raskere og mer kostnadseffektiv versjon av Deep Research, drevet av en mindre modell som fortsatt gir resultater av høy kvalitet. 

I ukene og månedene fremover vil vi jobbe med den tekniske infrastrukturen, følge nøye med på den nåværende versjonen og gjennomføre enda mer grundig testing. Dette samsvarer med prinsippet vårt om gradvis lansering. Hvis alle sikkerhetskontroller fortsetter å oppfylle kravene våre for lansering, planlegger vi å gjøre Deep Research tilgjengelig for Plus-brukere om omtrent én måned.

Hva skjer videre

Deep Research er i dag tilgjengelig på ChatGPT på nett og vil bli lansert i mobilapper og dataprogrammer i løpet av måneden. Foreløpig har Deep Research tilgang til det åpne nettet og opplastede filer. I fremtiden vil du kunne koble til mer spesialiserte datakilder – og utvide tilgangen til abonnementsbaserte eller interne ressurser – slik at utdataene blir mer enda robuste og personlig tilpasset.

Når vi ser lenger frem i tid, ser vi for oss agentopplevelser i ChatGPT for asynkron, reell research og gjennomføring. Kombinasjonen av Deep Research, som kan utføre asynkrone nettbaserte undersøkelser, og Operator, som kan utføre faktiske handlinger, vil gjøre det mulig for ChatGPT å ta seg av stadig mer avanserte oppgaver for deg.


Tillegg, 3. februar 2025: Vi gjennomførte grundig sikkerhetstesting, beredskapsvurderinger og gjennomgang av styringsrutiner på den tidlige versjonen av o3 som driver Deep Research, og kom frem til at den innebærer middels(åpnes i et nytt vindu) risiko. Vi gjennomførte også ekstra sikkerhetstesting for å få en bedre forståelse av den ekstra risikoen knyttet til at Deep Research kan bla gjennom og søke på nettet, og har lagt til nye sikkerhetstiltak. Vi vil fortsette med grundig testing og nøye overvåking av den nåværende begrensede lanseringen. Vi vil dele resultatene våre fra sikkerhetstestene og sikkerhetstiltakene for Deep Research i et systemkort når vi utvider tilgangen til flere Plus-brukere.

Fotnoter

  1. 1

    Vi fant ut at grunnsannhetssvarene for dette datasettet var blitt lekket i stor grad på nettet og har derfor blokkert flere nettsteder eller nettadresser for å sikre en rettferdig evaluering av modellen.

Forfattere

OpenAI

Research-ledere

Isa Fulford og Zhiqing Sun

Grunnleggende bidragsytere

Alex Tachard Passos, Alexandra Barr, Allison Tam, Charlotte Cole, Hyung Won Chung, Jason Wei, Jon Blackman, Scott Mayer McKinney og Valerie Qi

Kjernebidragsytere

Research

Elaine Ya Le, Eric Mitchell, Eric Wallace, Hyung Won Chung, Ignasi Clavera, Leo Liu, Lorenz Kuhn, Louis Feuvrier, Max Schwarzer, Saachi Jain, Scottie Yan, Shunyu Yao, Vitchyr Pong

Utrulling

Carpus Chang, Harry Zhao, Joseph Trasatti, Joshua Dickens, Matt Kaufer, Mike Trpcic, Minnia Feng, Neel Ajjarapu, Peter Vidani,  Sean Fitzgerald

Bidragsytere

Research

Ahmed El-Kishky, AJ Ostrow, Alexander Wei, Andrei Gheorghe, Andrew Kondrich, Andrey Mishchenko, Anuj Nair, Behrooz Ghorbani, Brydon Eastman, Chak Li, Foivos Tsimpourlas, Francis Song, Giambattista Parascandolo,Gildas Chabot, Hessam Bagherinezhad, Haitang Hu, Hongyu Ren, Henry Aspegren, Hunter Lightman, Ilya Kostrikov, Ilge Akkaya, James Lennon, Jean Harb, Jonathan Ward, Kai Chen, Katy Shi, Kevin Liu, Kevin Yu, Manuka Stratta, Marvin Zhang, Mengyuan Yan,  Mostafa Rohaninejad, Noam Brown, Phoebe Thacker, Raz Goan, Reah Miyara, Spencer Papay, Taylor Gordon, Wenda Zhou, Wenlei Xie, Yash Patil, Yann Dubois, Youlong Cheng, Yushi Wang, Wyatt Thompson

+ alle bidragsyterne bak o3.

Sikkerhetssystemer

Adam Kalai, Alex Beutel, Andrea Vallone, Andy Applebaum, David Robinson, Elizabeth Proehl, Evan Mays, Grace Zhao, Irina Kofman, Jason Phang, Joaquin Quinonero Candela, Joel Parish, Kevin Liu, Kristen Ying, Lama Ahmad, Leon Maksin, Leyton Ho, Meghan Shah, Michele Wang, Miles Wang, Phillip Guo, Olivia Watkins, Owen Campbell-Moore, Patrick Chao, Sam Toizer, Samuel Miserendino, Sandhini Agarwal, Tejal Patwardhan, Tina Sriskandarajah, Troy Peterson, Yaodong Yu, Yunyun Wang

Utrulling

Adam Koppel, Adam Wells, Adele Li, Andy Applebaum, Andrey Malevich, Andrew Duberstein, Andrew Howell, Anton Tananaev, Ashley Tyra, Brandon Walkin, Bryan Ashley, Cary Bassin, Cary Hudson, Cory Decareaux, Cristina Scheau, Derek Chen, Dibya Bhattacharjee, Drea Lopez, Eric Antonow, Eric Burke, Filippo Raso, Fotis Chantzis, Freddie Sulit, Harris Cohen, Heather Whitney, Jay Dixit, Jeffrey Han, Jen Robinson, Jessica Shieh, Joel Parish, Kan Wu, Kevin Gladstone, Kshitij Wadhwa, Leo Vandriel, Leyton Ho, Liang Chen, Madeline Christian, Mamie Rheingold, Matt Jones, Michelle Fradin, Mike McClay, Mingxuan Wang, Nacho Soto, Niko Felix, Patrick Delaney, Paul McMillan, Philip Pronin, Rodrigo Riaza Perez, Samuel Miserendino, Scott Ethersmith, Steven Baldwin, Thomas Dimson, Tomo Hiratsuka, Yaming Lin, Yara Khakbaz, Yining Chen

Lederskap

Akshay Nathan, Greg Brockman, Hannah Wong, Jakub Pachocki, Jerry Tworek, Johannes Heidecke, Josh Tobin, Liam Fedus, Mark Chen, Mia Glaese, Nick Turley, Sam Altman og Wojciech Zaremba