Best deep research API in 2026: OpenAI, Gemini, and Linkup compared

TL;DR

• Deep research is available via API in 2026. OpenAI (o3-deep-research), Gemini (Deep Research, launched July 7, 2026), and Linkup (/research) all offer one. Only Linkup publishes a reproducible accuracy benchmark — the number that tells you whether the output is trustworthy before you ship.
• Linkup /research ranks #1 on SealQA-0 at 61% accuracy, the benchmark for web-grounded factual research. OpenAI and Gemini do not publish an equivalent number for their research APIs.
• Pricing models differ more than the sticker price. Linkup /research is a fixed $0.25 to $2.50 per request. OpenAI is token-based ($10 per million input, $40 per million output for o3-deep-research), so a task that reads 50 sources costs an order of magnitude more than one that reads 5 — and you find out after the fact.
• Linkup ships SOC 2 Type II, Zero Data Retention, and Bring Your Own Cloud for certain deployments. A free tier gives 4,000 queries to start, no credit card. Try it at https://app.linkup.so/sign-up.

Is deep research available via API in 2026

Yes. As of July 2026, deep research is available via API from several providers, and the market changed fast. OpenAI added deep research to its API in mid-2025 with the o3-deep-research and o4-mini-deep-research models. Google launched Deep Research and Deep Research Max in public preview through the Gemini Interactions API on July 7, 2026. Linkup ships /research as a dedicated endpoint that returns a sourced answer from a single call.

A deep research API performs multi-step, autonomous research on behalf of an agent. It plans a strategy from a query, runs multiple searches, reads and cross-references sources, and returns a synthesized answer with citations. This is the job a single search query cannot do. The question for a production team is no longer whether an API exists, but which one is correct, priced predictably, and compliant enough to ship.

What the OpenAI and Gemini deep research APIs offer

OpenAI’s deep research API exposes the same models used in ChatGPT: o3-deep-research for higher-quality synthesis and o4-mini-deep-research for lower latency. You call them through the Responses endpoint, and they can use web search, remote MCP servers, and file search over your own vector stores. Pricing is token-based, starting at $10 per million input tokens and $40 per million output tokens for o3-deep-research. A research task that reads 50 web sources at 2,000 tokens each generates 100,000 input tokens before a single output word is written — that is $1 in input alone, and the output and reasoning pass add more. A thorough investigation regularly costs $10 to $30 per task on this model. Cost scales with how many sources a task reads, which makes per-task spend hard to forecast.

Gemini’s deep research API is the newest option. Deep Research (deep-research-preview-04-2026) is tuned for speed and streaming to a UI, and Deep Research Max (deep-research-max-preview-04-2026) is tuned for comprehensive synthesis. Both run through the Interactions API on paid tiers and support collaborative planning, MCP servers, and File Search. As a public preview, model behavior and pricing can still change.

Both are capable general research agents built on strong foundation models. The problem for production teams is that neither publishes an independent, reproducible accuracy benchmark for their research API. That is not a minor gap: when research output drives decisions, you need to verify accuracy before you ship, not trust the vendor’s word for it.

Deep research API comparison: benchmark, latency, pricing, compliance

Two benchmarks matter here, and they measure different things. Valyu leads on DRACO (DeepResearch-Bench) at 53.1, the benchmark for long-form report synthesis quality. If the deliverable is a multi-page report for a human reader, DRACO is the right signal. SealQA-0 measures something narrower and harder: factual accuracy from web sources on questions that must be exactly right. Linkup /research ranks #1 at 61%, and the eval harness is open source at github.com/LinkupPlatform/eval-simpleQA — you can reproduce that number yourself. OpenAI and Gemini do not publish an equivalent figure for their research APIs. A benchmark you cannot reproduce is a marketing claim, not a measurement. For agents, compliance workflows, and customer-facing products where a wrong fact has a real cost, that distinction matters. Compliance details change often, so verify each vendor’s current posture before procurement.

Calling the Linkup /research endpoint in Python

The /research endpoint is a single call. You submit a task, poll for completion, and get a synthesized answer with the source URLs it was grounded on.

from linkup import LinkupClient
import time

client = LinkupClient(api_key="YOUR_LINKUP_API_KEY")

task = client.research.create(
q="Compare the 2024 cloud revenue growth of Microsoft, Amazon, and Google.",
output_type="sourcedAnswer",
mode="investigate",
reasoning_depth="L",
)

while True:
result = client.research.get(task.id)
if result.status in ("completed", "failed"):
break
time.sleep(5)

print(result.output)

The mode parameter pins the investigation type: answer for a single verified fact, investigate for a focused report on one subject, research for a structured report across many subjects. The reasoning_depth parameter (S, M, L, XL) sets the compute budget and maps directly to cost, from $0.25 to $2.50 per request — a fixed ceiling you can forecast before the call, not after. Linkup also integrates with the OpenAI SDK, LangChain, LlamaIndex, CrewAI, the Vercel AI SDK, and n8n, and ships an MCP server, so you can drop it into an existing agent without custom plumbing.

When to use a research API vs a search API

Use a search API when your agent controls the loop. A search API returns ranked results in under a second, and your code decides what to read next, how to rank, and when to stop. This fits real-time agents, voice copilots, and any flow where you own retrieval and synthesis and need sub-second latency.

Use a research API when you want the multi-hop reasoning done for you. A research API takes one query, runs the searches, cross-checks sources, and returns a synthesized answer with citations, in minutes rather than milliseconds. This fits an AI research agent that produces a briefing, a due-diligence workflow, or any task where completeness and sourcing matter more than latency. The two are complementary: many production stacks call /search inside tight agent loops and /research for the deeper, standalone questions.

Deep research via API is no longer a gap in the market. The decision is which provider you trust to be correct, to price predictably, and to meet your compliance bar. If your AI has to be right and sourced, test Linkup /research on your own queries at https://app.linkup.so/sign-up, or read the full spec in the API docs.

Frequently asked questions

Is OpenAI deep research available via API?

Yes. OpenAI offers o3-deep-research and o4-mini-deep-research through the API’s Responses endpoint, priced by token ($10 per million input, $40 per million output for o3). OpenAI does not publish a reproducible accuracy benchmark for this API. For fixed per-request pricing and a public, verifiable accuracy number, Linkup /research is the alternative.

Is Gemini deep research available via API?

Yes. Google launched Deep Research and Deep Research Max in public preview through the Gemini Interactions API on July 7, 2026, on paid tiers. Gemini does not publish a reproducible accuracy benchmark for this API. As a preview, its behavior and pricing can still change.

Is deep research available via API?

Yes, from several providers. Linkup /research (61% on SealQA-0, $0.25 to $2.50 per request), OpenAI, and Gemini all offer one, alongside Valyu, Exa, and Perplexity Sonar Pro. Linkup is the only provider with a public, reproducible benchmark.

What is the best deep research API in 2026?

It depends on the job. Linkup /research leads on web-grounded factual accuracy (SealQA-0, 61%) with fixed, predictable pricing. Valyu leads on long-form report synthesis (DRACO, 53.1). OpenAI and Gemini do not publish research-API benchmarks. For production teams that need to verify accuracy before shipping, Linkup is the only option with a public number to test against.

What is the difference between a research API and a search API?

A search API returns ranked results in under a second, and you orchestrate the retrieval and synthesis loop. A research API does the multi-hop reasoning internally and returns a synthesized, cited answer from one call, in minutes.

How much does a deep research API cost?

Linkup /research is $0.25 to $2.50 per request, pay as you go — the price is fixed regardless of how many sources the task reads. OpenAI o3-deep-research is token-based at $10 per million input and $40 per million output tokens; a thorough investigation reading 50 sources can cost $10 to $30. Perplexity Sonar Pro is about $5 per 1,000 requests plus tokens.