Quick wins for a faster PC:
Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →iTechGuides is reader-supported. When you buy through links on our site, we may earn an affiliate commission. As an Amazon Associate I earn from qualifying purchases. Learn more
Choose Semantic Scholar when your agent needs structured scholarly records—papers, authors, citations, references, and literature discovery. Choose Valyu when it needs a broader workflow that can search web and specialist sources, extract supplied pages, synthesize sourced answers, or produce a multi-step research report. They solve different jobs, so neither is a universal winner; for overlapping tasks, test both against the same queries and quality criteria.
How do the APIs differ?
Semantic Scholar: a scholarly literature graph
Semantic Scholar’s Academic Graph API is organized around scholarly publication data: paper and author records, citations, venues, and related fields. Its documented operations include relevance-based paper search, title matching, paper and author lookup, batch operations, and citation or reference data. Search supports plain-text queries and filters such as year, open-access PDF availability, publication type, venue, and field of study. The cited paper-search endpoint documents up to 1,000 relevance-ranked results. For larger-scale workloads, Semantic Scholar points to bulk search and its Datasets API.
The API overview displayed publisher-reported counts of 214 million papers, 2.49 billion citations, and 79 million authors when checked in 2026. These are Semantic Scholar’s page-displayed figures, not independently audited measurements. See the Semantic Scholar API overview and the Academic Graph API reference for current scope and endpoint details.
Semantic Scholar also documents separate API families for related-paper recommendations and downloadable datasets. Its tutorial distinguishes Academic Graph for scholarly records, Recommendations for finding related papers, and Datasets for downloading data to host and query locally. Local datasets can suit repeated or large scholarly queries, but require the operator to handle hosting and maintenance. See the official API tutorial.
#1 Best Overall
Valyu: search, extraction, answers, and research workflows
Valyu’s official documentation lists Search, Contents, Answer, and DeepResearch APIs. Their documented roles are, respectively, searching sources, extracting content from supplied URLs, generating search-grounded answers, and running an asynchronous research task that returns a report. The documentation also shows Python and JavaScript SDKs and hosted MCP access. Valyu describes coverage across web and specialist areas including academic, financial, biomedical, legal, and economic sources; availability depends on the data source and plan.
See Valyu’s API documentation and its APIs product page for current capabilities and source coverage. Broader coverage does not by itself establish that a particular source is available to your account or that its results will be best for your questions.
Rank #2
- Used Book in Good Condition
Which API should your agent call?
| Agent task | Better first candidate | Why |
|---|---|---|
| Find scholarly papers and retrieve paper metadata | Semantic Scholar | Its Academic Graph is built around scholarly records and documents paper search and details. |
| Resolve a known paper title or follow citation and reference links | Semantic Scholar | Title matching and citation/reference fields are documented; verify the exact endpoint’s fields and limits. |
| Retrieve author records or find related papers | Semantic Scholar | Author operations and a separate Recommendations API are documented. |
| Search across the open web and specialist domains | Valyu | Its materials describe cross-domain search; actual sources and access vary. |
| Extract and structure content from supplied URLs | Valyu | The Contents API is specifically documented for URL extraction. |
| Return an answer synthesized from retrieved sources | Valyu | The Answer API combines search and synthesis; your agent should still inspect evidence and citations. |
| Produce a multi-step research report | Valyu | DeepResearch is documented as an asynchronous workflow with report outputs. |
| Run large scholarly queries locally or repeatedly | Semantic Scholar Datasets may fit | Datasets can be downloaded for local hosting and custom queries, with the accompanying ingestion and maintenance work. |
This routing is an inference from each provider’s documented product scope, not a controlled head-to-head test. If a task spans both scholarly graph retrieval and broader web research, route calls by task or compare the candidates on the overlapping portion of the workload.
What should you compare before integrating?
- Corpus and domain scope: Does the API cover the source types your agent must use, and are those sources available under your account or plan?
- Data shape: Do you need structured paper, author, and citation fields, or extracted page content that your agent can interpret?
- Workflow: Is the requirement search, extraction, answer synthesis, or a complete multi-step report?
- Controls and limits: Check filters, batch support, result ceilings, authentication requirements, and rate limits for the endpoints you will call.
- Cost model: Determine whether usage is billed per source or retrieval, successful URL, token, task, or another unit that applies to your expected request mix.
- Evidence quality: Check whether the returned material supports the answer and whether your agent can inspect and present its sources clearly.
What do access and pricing look like?
Semantic Scholar access
Semantic Scholar says most endpoints are public without authentication, though they may be throttled, while certain endpoints require an API key. Its overview recommends sending a key and states an introductory keyed rate of 1 request per second across endpoints. Treat this as the current documented guidance, not a throughput or uptime guarantee; check the API overview for current access terms before designing a production integration.
Valyu pricing
Valyu’s pricing page describes pay-as-you-go and monthly-credit plans, $10 in signup credits, and different source access by plan. The page distinguishes Search charges by retrieval/source, Contents charges per successful URL plus AI processing, Answer charges as search costs plus token charges, and DeepResearch charges per task. It lists a Search retrieval range of $0.50–$30 CPM (cost per thousand), depending on source, and DeepResearch task prices of $0.10–$15. Displayed monthly plans include $29/month for $50 credits, $89/month for $130 credits, and $449/month for $750 credits. These are vendor-posted prices and plan details that can change; verify them on Valyu’s pricing page before estimating a budget or purchasing.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can you evaluate them on your workload?
A small, representative test set can reveal whether documented scope translates into useful results for your agent. Include a known-paper lookup, broad scholarly discovery, a current cross-domain fact, extraction from a supplied URL, and a question that requires synthesis across sources. Use identical prompts and criteria where the APIs overlap; do not treat differences in product scope as a failed test when an API does not offer the requested workflow.
- Define the expected evidence. For each query, specify acceptable source domains, fields, freshness, and what counts as a supported answer.
- Run equivalent tasks. Keep queries and prompts identical for comparable capabilities, and note when an API’s documented design does not cover a task.
- Score usefulness and traceability. Record whether the needed source exists, whether evidence supports the answer, and whether citations or records are inspectable.
- Measure operational behavior. Track latency, errors, rate limiting, and the billable units actually triggered by each request pattern.
- Estimate realistic volume. Apply observed usage to your expected monthly query mix, including extraction, tokens, or research tasks where relevant.
Valyu’s product page publishes vendor benchmark claims including 94% SimpleQA precision and 79% FreshQA accuracy, and reports 72.7% for DeepResearch Heavy on a comparison it says used the benchmark’s 100 tasks. These figures are Valyu-published context, not an independent comparison with Semantic Scholar or a prediction of performance on your workload. Check the product page for the benchmark conditions and comparison entries before relying on them.
Quick Recap
Best Value
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
