GPT-6.1 Sol: What APAC Teams Should Test Before Moving Agentic Search Workloads
OpenAI released GPT-6.1 Sol with lower cached-input pricing and gains in document, automation and computer-use tests. APAC teams should validate sources, tool failures and market-specific data before migration.
NEWS · AI SEARCH LABOpenAI released GPT-6.1 Sol on September 29, 2026. It is available in ChatGPT Work and Codex for Plus, Pro, Business, Enterprise and Edu users, and through the API as gpt-6.1-sol. It is not yet available in Chat. API pricing is $2 per million input tokens, $0.10 per million cached input tokens and $10 per million output tokens. OpenAI reports gains over GPT-6 Sol in professional-document work, automation, computer use and factuality, but APAC organisations still need tests built around their own languages, sources, tools and approval rules.
Where is GPT-6.1 Sol available?
The model is available in ChatGPT Work, Codex and the OpenAI API. Product teams should confirm availability in the account and interface they plan to use because a model listed in Work or Codex is not automatically available in Chat.
OpenAI positions GPT-6.1 Sol near GPT-6 Astra on several agentic coding, computer-use and professional-work evaluations at one-fifth of Astra’s standard input and output token prices. Cached input costs $0.10 per million tokens. Workloads that reuse long instructions or reference material should measure cache hits, output length and retries rather than comparing list prices alone.
What changed for agentic search?
OpenAI tested whether the model tells a user when its search tool is broken. At maximum reasoning effort, GPT-6.1 Sol failed to disclose the broken tool in 2.1% of the selected cases, compared with 4.9% for GPT-6 Sol, 1.5% for GPT-6 Astra and 28.7% for GPT-6 Luna. OpenAI says these tasks were selected to provoke failure and do not represent normal use.
That distinction matters. A brand-research agent that silently loses search access may answer from stale model knowledge and present an old product name, market status or policy as current. Migration testing should include blocked search, empty results, conflicting sources, undated pages and authenticated pages. Teams also need a stop rule for answers that lack a usable source trail.
How should teams read the document and automation results?
OpenAI reports that GPT-6.1 Sol scored above Opus 5.5 on GDP.pdf across the tested reasoning settings. On AutomationBench at medium reasoning effort, it scored 2.2 percentage points above Opus 5.5 and 4.8 points above GPT-6 Sol at the same setting. These are vendor-reported results for named benchmark versions and tool conditions, not certification for a company’s production process.
Real documents combine tables, footnotes, images and superseded appendices. Reviewers should check which page and table supported an answer, whether the model separated current and retired material, and whether it added a conclusion that the source did not contain. If an agent converts regional documents into customer answers, the evidence should remain traceable to the original file.
What changes across APAC markets?
A single English test set will miss material failures. Korea needs Korean queries and coverage across NAVER, Google and ChatGPT sources. Japan needs consistent names between headquarters and local entities. Taiwan and Hong Kong require Traditional Chinese and local source coverage, while India requires language and regional variation. Australia often shares English material with global teams but has separate healthcare, finance and commerce rules.
Regional teams can share evaluation logic for tool failures, source dates, permissions and approvals. Local teams should own market facts, regulated claims and platform coverage. Ask each market to test the same business task with its own language, entity names, prices, service areas and exception cases.
What should enterprises, healthcare and commerce teams test?
Enterprises should compare quality and total cost on recurring reports, internal retrieval and competitor research. Healthcare organisations should separate public provider information, patient communications and clinical work; a benchmark containing healthcare documents does not make the product suitable for clinical use. Human review remains necessary for material medical decisions.
Commerce teams should split discovery, inventory and delivery retrieval, cart changes and payment into separate risk classes. Check whether the agent stops when a source disappears, states the date behind price and availability, and asks for confirmation before an action with financial effect. Lower model cost can increase call volume, so error handling and escalation costs belong in the calculation.
What should GEO, AEO and SEO teams change?
A model release does not create a ranking or citation signal for a website. It does increase the number of situations in which agents may read documents, call tools and compare sources. Brand names, legal entities, service regions, prices, locations and professional profiles should agree across visible copy, structured data and trusted external records.
Do not count citations alone. Record whether the model chose the correct source, disclosed a failed tool, rejected stale material and asked for approval before consequential action. Repeating the same tasks on GPT-6 Sol and GPT-6.1 Sol gives a clearer migration decision than relying on benchmark headlines.
APAC migration checklist
- Confirm whether the workload runs in Work, Codex or the API.
- Test successful search and broken-search conditions with the same prompts.
- Place current and superseded documents together and inspect source choice.
- Track input, cached input, output and retry cost per completed task.
- Require human review for healthcare, legal and financial outputs.
- Run local-language tests with each market’s source mix and entity names.
AI Search Lab view
GPT-6.1 Sol makes a high-capability model less expensive to use for repeated agent work. That can increase the number of searches and actions running in the background. Source provenance, stop conditions and approval logs therefore need to work at the same volume. The production result will depend on current market data and operational controls as much as the model score.
LeadGenLab’s GEO and AEO services audit public sources and AI answer evidence across APAC. Global teams can discuss a Korea-first or regional evaluation through the project contact.
Official source
Checked September 30, 2026 against OpenAI’s September 29 announcement. Pricing, availability and evaluation figures are vendor-reported and do not guarantee production results.
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