Deep Research Max Preview (Apr-21-2026)
Deep Research Max Preview: Uncover deep insights fast.
A tradução deste card ainda não está disponível; parte do conteúdo aparece em inglês.
- Janela de contexto (tokens)
- 131.072
Deep Research Max Preview excels at deep analysis and synthesis of information. When you need to go beyond surface-level answers, this model can process large amounts of text to extract key insights, identify patterns, and construct detailed reports. It is ideal for tasks that require critical thinking and evidence-based reasoning.
Use this model when you need to analyze research papers, compare multiple viewpoints, or generate a structured summary of a complex topic. It is built to handle nuanced questions that require pulling together information from different parts of a document or across multiple documents.
What makes Deep Research Max Preview distinctive is its ability to maintain context over long inputs and produce structured, citation-ready outputs. It is designed for depth, not speed, making it the go-to choice for serious research and analysis tasks.
- Analyzing research papers
- Comparing multiple documents
- Generating detailed reports
- Synthesizing complex topics
- Extracting key insights from long texts
Combines information from multiple sources into a coherent analysis.
Maintains accuracy and coherence across very long documents.
Produces well-organized reports with clear citations and summaries.
- Provide clear, specific questions for the most focused analysis.
- Upload full documents rather than excerpts for best context.
- Use bullet points or numbered lists in your prompt to guide the output structure.
- Review the generated report for accuracy, as it may infer connections not explicitly stated.
- May be slower than standard models due to its deep processing.
- Can occasionally over-summarize, losing subtle details.
- Best performance requires clear, well-structured input documents.
Analyze this collection of scientific papers on CRISPR technology and produce a comprehensive report covering history, current applications, and ethical considerations.
Based on these industry reports, create a detailed competitive landscape analysis for the electric vehicle market, including key players, market share, and emerging trends.
Synthesize the provided literature on remote work productivity into a structured review with thematic sections, key studies, and recommendations for future research.
A number that sets the random starting point for generation; using the same seed and inputs will produce the same output.
Nucleus sampling: a lower value makes the model consider only the most likely words, leading to more predictable text.
The maximum length of the generated response, in tokens (roughly 0.75 words per token).
Controls randomness: lower values make output more focused and deterministic, higher values make it more creative.
Encourages the model to talk about new topics by penalizing words that have already been used.
Reduces repetition by penalizing words that have already appeared in the text.
Os preços exibidos são o que você paga na Muitto. Sem surpresas.
What kind of documents can I use with this model?
You can use any text-based documents, such as PDFs, articles, or reports.
How is this different from a standard chat model?
It is optimized for deep analysis and synthesis of long texts, not for casual conversation.
Can I ask follow-up questions based on the analysis?
Yes, you can ask follow-up questions to refine or expand on the generated report.