OpenAI Deep Research API 与 SciSpace API 科研场景对比（2026）

Choose SciSpace for a product whose core output is scientific evidence: papers, extraction tables, screening decisions, and cited reviews delivered as files. Its API is in early access and isn't open yet; request access through the "Request SciSpace API access" form.

Choose OpenAI for configurable research inside a broader application, particularly when you already maintain retrieval and evidence storage. Its documented replacement supports web search, file search, function calling, and structured outputs.

This openai deep research api comparison separates workflow fit from answer quality. No authenticated comparative runs were performed, so the five-question suite below is an evaluation specification, not a benchmark.

Is the OpenAI Deep Research API still available?

OpenAI shut down o3-deep-research and o4-mini-deep-research on July 23, 2026, naming gpt-5.6-sol as their replacement. This article uses that replacement through Responses, not the retired model IDs.

The OpenAI deep research API documentation retains historical examples and warns that changing the model ID alone is not a complete migration. Review tools, request fields, and output handling.

OpenAI Deep Research API vs SciSpace: side-by-side

The distinction is configurable research infrastructure versus a literature-specific workflow. OpenAI provides search and generation components; SciSpace accepts a research brief and plans the discovery, reading, extraction, and synthesis.

Dimension	OpenAI Responses replacement	SciSpace API
Coverage	Web search and configured file search	SciSpace's 280M+ paper index, Google Scholar, PubMed, arXiv; tasks can add trial registries, Google Patents, grant databases and 77 science databases
One submission	Response object; optional background execution	One task; links to every file the agent created plus any text answer
Citations	URL, title, and text-location annotations	Papers with full metadata; citation-checked report sections
Extraction	Structured outputs; application-designed workflow	Extraction tables with AI-filled columns from full text; filter, sort, combine
Review workflow	Configure prompts, tools, and application state	Systematic review: screening of up to 3,000 papers, extraction, PRISMA flow diagram, report

OpenAI's model, background, and web-search documentation support the OpenAI column. These are interface differences, not evidence that one provider produces more accurate answers.

OpenAI configurable research compared with the SciSpace early-access workflow for multi-source literature discovery and evidence extraction

How do you compare the same five research questions?

Send identical questions, publication windows, and evidence requirements to both implementations. Keep the output contract fixed even when the underlying search tools differ.

The table specifies what each answer must contain. The table does not represent an observed response.

Research question	Required evidence from both implementations
Does retrieval augmentation improve factual accuracy in knowledge-intensive NLP?	Tasks, baselines, metrics, and an extracted evidence table
Which methods reduce hallucination in scientific summarization?	Methods, evaluations, failure cases, and an extracted evidence table
How do graph neural networks compare with transformers for molecular property prediction?	Datasets, splits, metrics, leakage controls, and an extracted evidence table
Which interventions improve reproducibility in computational science?	Study designs, measured outcomes, inclusion reasoning, and an extracted evidence table
What evidence supports machine-learning approaches to battery state-of-health estimation?	Battery chemistry, test conditions, validation, study limitations, and an extracted evidence table

Avoid grading prose alone. For molecular prediction, for example, a polished conclusion without dataset-split details fails the extraction requirement.

Shared research brief

For each run, set question to the research question and requirements to its required-evidence cell. Send the same resulting prompt to both implementations.

That instruction makes missing information visible rather than encouraging invented values. For a real evaluation, retain the exact prompt, provider configuration, raw response, and reviewer corrections.

OpenAI: submit a research request

This preserves the original Python HTTP approach while omitting checkpoint and retry helpers. It combines web search with background execution.

Poll GET /v1/responses/{response_id} until completion, handling terminal failures separately. Parse message output items rather than assuming the first item contains the answer.

SciSpace: what the same request looks like

Send the same prompt to the SciSpace Task API as one research task in plain language. The agent plans the work and picks its tools; the plan and progress stream live, and the result comes back as links to the files it created plus any text answer.

One task covers search, reading, extraction and writing; you do not orchestrate separate search, reading and synthesis pipelines.

What does a product built on each API return?

These are response structures, not fabricated answers to the five questions.

Product element	OpenAI response	SciSpace thread
Answer	`output[]` message content with `output_text`	Supporting text answer from the Task API
Citation cards	`annotations[]` with `url_citation`	Cited papers in the answer and report
Bibliographic details	Enrich URL citations when needed	Search API papers carry their relevant metadata
Evidence grid	Extract and persist an application table	Extraction table with AI-filled columns
Report download	Implement the desired export workflow	Links to Markdown, Word, PowerPoint, or LaTeX compiled to PDF with BibTeX

OpenAI documents its output-item structure and URL citation annotations. Keep citations visible and clickable when displaying web-derived answers.

With SciSpace, ask for the extraction table in the original task. To add a field later, such as validation setting, send a follow-up in the same thread.

Illustrative response structures comparing OpenAI URL citations with SciSpace paper metadata and extracted evidence tables

How should you evaluate the outputs?

Review anonymized answers before revealing the provider. Score:

Relevance: Does each paper address the question?
Citation support: Does the source support the associated claim?
Extraction fidelity: Are values, units, and qualifiers preserved?
Coverage: Which papers from a human-curated reference set are missing?
Uncertainty: Is missing evidence distinguished from a negative result?

Count incomplete jobs and manual corrections, not just successful answers. A workflow advantage does not establish an accuracy advantage; the latter needs measured results from your workload.

OpenAI Deep Research API pricing: what do 1,000 calls cost?

Define a call here as one submitted research job, not one search invocation. A job can make multiple searches.

On October 21, 2026, GPT-5.6 Sol lists short-context rates of $4 per million input tokens and $20 per million output tokens. Web search is $10 per 1,000 tool calls, with search-content tokens charged at model rates.

A hypothetical job using 20,000 total billable input tokens, 5,000 total billable output tokens, and 10 searches would be:

Input: $0.08.
Output: $0.10.
Searches: $0.10.
Total: $0.28 per job, or $280 for 1,000 jobs.

This calculation excludes caching, cache writes, additional tools, and surcharges; it is not measured usage. OpenAI lists the model's Free tier as unsupported, and effective request and token limits depend on the account.

How do you migrate parameters and application state?

Treat switching as an adapter change, not a base-URL replacement. OpenAI's Responses and background documentation define the corresponding OpenAI fields.

Concept	OpenAI	SciSpace
Start	`POST /v1/responses`	Send a research task to the Task API
Brief	`input`	The task in plain language
Research configuration	`model` and tool definitions	None needed; the agent picks its tools
Streaming	`stream: true`	Plan steps and the answer stream live
Answer parser	Output text and annotations	Links to created files plus any text answer

Follow-ups continue in the same thread, so users can refine a review without restating the question. Keep provider state behind your own job ID. The SciSpace agent does not pause for user input, so design for streamed progress and follow-ups rather than approval steps.

When should you choose OpenAI instead?

Choose OpenAI when research is one component of a broader assistant and your team wants direct control over tools and output schemas. GPT-5.6 Sol supports function calling, structured outputs, web search, and file search.

It is also the practical option of these two for an immediate integration without waiting for SciSpace access. If you already own literature ingestion and screening infrastructure, replacing it may offer less value than retaining your existing workflow.

The SciSpace API for scientific research products

OpenAI's Responses replacement gives you web search, file search and structured outputs, but literature retrieval, screening, extraction tables and bibliography handling stay application work you build around it. This is a workflow comparison, not a measured accuracy claim.

The SciSpace API puts the full SciSpace Agent behind one call. It searches, reads, analyses and writes, and delivers the result as ready-to-use files.

API	What you send	What you get back
Search API	A research question in plain language, such as "retrieval augmentation and factual accuracy in knowledge-intensive NLP"	A list of papers, each with all its relevant metadata, from one search across SciSpace's 280M+ paper index plus Google Scholar, PubMed and arXiv
Task API	A research task, such as one of the five evaluation questions with its required evidence table, a systematic review, or a cited report	Links to every file the agent created (Markdown, Word, PowerPoint and more) plus any supporting text answer; the plan and progress stream live, and follow-ups continue in the same thread

What this adds on top of OpenAI's research tools

Pure agentic API: describe the task in plain language and the agent picks the tools (search, screening, extraction, analysis, writing). One integration instead of stitching together a search API, a PDF parser, a trials database and an LLM.
Scholarly sources by default: papers with full metadata from SciSpace's 280M+ paper index plus Google Scholar, PubMed and arXiv.
Evidence beyond papers: one task can also search 11 national and regional clinical-trial registries plus the WHO registry, Google Patents, grant databases and 77 public science databases such as PubChem, ChEMBL and UniProt.
Systematic review: research questions, screening of up to 3,000 papers, extraction, a PRISMA flow diagram and a report.
Finished files from one run: Markdown, Word, PowerPoint, or a LaTeX report compiled to PDF with its BibTeX.
Live streaming: each plan step and the answer stream as they are written; follow-ups continue in the same thread.

When to use which

Choose OpenAI when research is one part of a broader assistant and you want direct control over tools and output schemas, or when you need to integrate now.
Choose SciSpace when the product's core output is scientific evidence: papers, extraction tables, screened reviews and cited reports.

What it doesn't do

No paywalled papers: it reads your own PDFs plus 50M+ open-access full texts.
Not a web search API: for news, documentation or general web pages, OpenAI's web search is the better tool.
Only the sources listed here: it does not query Semantic Scholar, Scopus, Web of Science or Lens.

The SciSpace API is in early access; it isn't open yet. At launch it's open to anyone: register on SciSpace and start, with no sales call. Every account gets $5 in free API credits, enough to try both APIs on real questions. The API is separate from SciSpace consumer plans; no consumer plan includes API usage.

Frequently asked questions

Last checked

October 21, 2026. OpenAI claims were checked against official documentation. SciSpace claims describe the API in early access and may change before launch. No live comparative benchmark was performed.

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OpenAI Deep Research API vs SciSpace API for Science (2026)

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