What is the best AI model for web research?

As of 19 July 2026, **GPT-5.6 Sol is the strongest defensible default model for difficult web research**, particularly when it is paired with web search, citations, and an agent loop that can issue multiple queries.\n\nOpenAI reports that GPT-5.6 Sol scores **90.4% on BrowseComp as a single agent and 92.2% with its multi-agent configuration**, the strongest published results in OpenAI's launch comparison. That makes it the best general recommendation for hard-to-find, multi-step web research.\n\nThis is not a guarantee that every answer will be correct. Research quality also depends on the search engine, source selection, date filtering, citation mapping, conflict detection, and whether the system distinguishes evidence from model inference. **Gemini with Google Search grounding** is a strong alternative when native Google Search integration, multilingual search, and automatically returned citation metadata are particularly important.

A research system is more than a language model. It normally contains:\n\n1. a query planner;\n2. one or more search tools;\n3. page or document retrieval;\n4. source-quality and freshness checks;\n5. extraction and note-taking;\n6. synthesis;\n7. citations linked to individual claims;\n8. conflict and uncertainty handling.\n\nBrowseComp was designed to test whether an agent can locate hard-to-find information that may require browsing many websites. It contains 1,266 problems and is substantially more demanding than basic fact retrieval. OpenAI's current GPT-5.6 results make Sol the strongest public default for this class of task.\n\nThe multi-agent result should be interpreted carefully. Parallel search agents can explore different hypotheses, sources, or search terms at the same time, but they also increase cost and can reproduce the same error across several branches. A final synthesiser still needs to compare evidence, reject weak sources, and avoid treating agreement between copied claims as independent corroboration.\n\nGoogle's Gemini API provides native Google Search grounding. The API can generate searches, process live results, and return inline citation annotations associated with specific text spans. That integration may be more valuable than a small model-quality difference for applications that need direct search metadata or broad multilingual coverage.\n\nRegardless of model, a trustworthy research workflow should:\n\n- prefer primary and authoritative sources;\n- record publication date and event date separately;\n- preserve the exact URLs and access date;\n- distinguish quoted evidence from paraphrase and inference;\n- identify when multiple pages repeat one original source;\n- search for contradictory evidence;\n- avoid citing a source that does not support the attached claim;\n- state when evidence is insufficient;\n- re-check volatile claims shortly before publication.\n\nFor TRACE, the best implementation is not merely “ask GPT-5.6 to research.” It is a governed process in which the model proposes searches and synthesis while the platform stores the source set, claim-to-source links, dates, and review state.

- [OpenAI — GPT-5.6](https://openai.com/index/gpt-5-6/) — reports GPT-5.6 Sol's single-agent and multi-agent BrowseComp results and its web-research capabilities. - [OpenAI — BrowseComp](https://openai.com/index/browsecomp/) — defines the 1,266-question benchmark for finding difficult information through web browsing. - [Google — Grounding with Google Search](https://ai.google.dev/gemini-api/docs/google-search) — documents live Google Search grounding, generated queries, search-result processing, and inline citation annotations. - [Google — Prompt design strategies](https://ai.google.dev/gemini-api/docs/prompting-strategies) — recommends search grounding when obscure or recent information may be required. - [Anthropic — How we built our multi-agent research system](https://www.anthropic.com/engineering/multi-agent-research-system) — describes a production research architecture using parallel agents and the coordination and reliability problems it introduces.