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VerdictPal · editorial desk · updated 16 Oct 2026VerdictPal
Compare · Head-to-head

ASReview LAB vs Elicit: pricing, privacy & capabilities

Put two dossiers side by side: fit, cost, privacy, and the failure modes that actually change the choice.
ToolsModels

Compare · Tool A / Tool B·Head-to-head

ASReview LABvsElicit

These tools sit in different sets (Systematic review vs Research assistant), so picking one over the other is mostly a question of which job you're doing. ASReview LAB wins on citation honesty, source quality, privacy controls, and value for money; Elicit wins on speed. For the canonical workflow, pick Elicit. Use ASReview LAB when its niche specifically wins above.

ASReview LAB · avg
67
wins 9 of 16
Elicit · avg
65
wins 7 of 16
Fields differ
14
of 16 dossier rows
Receipts
22
sources + bench notes

Checked 2026-10-12 · ASReview LAB·Checked 2026-10-13 · Elicit

Dossier fields

Sixteen fields, side by side.

No.FieldASReview LABElicit
01Status · Editorial quality gateLightweightFlagship
02Editorial fit · 0–100 score70 / 10059 / 100
03CategorySystematic reviewResearch assistant
04Primary surfaceLocal appProsumer SaaS
05Modalities
  • Text
  • Data
  • Text
  • Data
06Role in a stackLocal active-learning screening for systematic reviewsPaper discovery, evidence-table extraction, and systematic-review support
07Best for
  • PhD students or librarians with thousands of titles and abstracts to screen against defined inclusion criteria
  • Reviews that need inspectable methods and records kept on a local machine or an institution's own server
  • Students with no licence budget who can install Python or arrange a self-hosted setup
  • Methods work comparing active-learning models on a fully labelled dataset
  • Building structured evidence tables across many papers
  • Early-stage systematic review screening and extraction workflows
  • Researchers who need cited summaries plus column-based extraction, not just a chatbot answer
  • Policy, pharma, medtech, and academic teams that value sentence-level citations, PRISMA-style method traces, and auditable extraction
08Avoid if
  • You need full-text review, extraction, risk-of-bias assessment and PRISMA reporting in one platform
  • You cannot install Python or Docker and have no institutional host
  • Nobody on the review team can justify and document a stopping rule
  • You expect the tool to find papers that your database search never retrieved
  • You need a cheap casual search tool rather than a high-cost research workflow
  • Your review data, uploaded PDFs, or unpublished documents cannot leave local infrastructure without a DPA
  • You expect PRISMA-grade results without human screening, extraction checks, and protocol review
  • Your main need is reference management or citation writing rather than evidence synthesis
09Capabilities tracked
  • Title and abstract screening with human relevance labels
  • Active-learning ranking updated from your labels
  • CSV, RIS and Excel dataset import
  • Prior-knowledge labels to initialise the model
  • Preconfigured ELAS models and custom model components
  • Simulation on fully labelled datasets
  • Editable labels, tags and screening notes
  • Automatic hiding of duplicate titles and texts in version 3
  • Progress dashboard with a reviewer-set stopping threshold
  • Export of labels and records to CSV, TSV, Excel or RIS
  • Export and re-import of an entire .asreview project
  • Self-hosted LAB Server with authentication and multiple reviewers
  • Search across 138M+ papers
  • ClinicalTrials.gov source support
  • Paper chat with full-text access
  • Zotero import
  • Automated Reports
  • Systematic Review Workflow
  • Screening up to 5,000 papers on Pro and 40,000 on Enterprise
  • Extraction columns
  • Sentence-level citations
  • PRISMA/methods/search-strategy report outputs
  • API access
10Failure modes · Documented, not buried
  • A run of irrelevant records does not prove there are no relevant studies left. Stopping early can miss studies; the dashboard has no default threshold and the docs say the ideal threshold is still being researched.
  • The model can only rank the records you import. An incomplete search or missing titles and abstracts can leave relevant studies outside the useful screening pool.
  • Your labels train the ranking. The SAFE paper warns that a seed set chosen only from familiar key papers can bias the model, and recommends a random training sample.
  • Free software still needs installation and maintenance. The current package requires Python 3.10 or later; a shared server also needs someone responsible for hosting and access.
  • Screening is not the whole review. Full-text eligibility checks, extraction, risk-of-bias assessment and reporting still need a separate process.
  • Automatic duplicate hiding in version 3 changes what appears during screening. PyPI says hidden duplicates can be included in exports; do not treat the visible screening count as a complete deduplication report.
  • Pricing jumps quickly from free to Pro/Scale
  • Structured tables and reports still require human validation
  • Uploaded papers/Zotero imports create cloud privacy boundary
  • Corpus and full-text coverage vary by discipline
  • Vendor accuracy claims need independent reproduction
  • Basic plan limits can distort real review workflows
11Evidence levelSynthesizedSynthesized
12Sources verified · Receipts on the dossier
13Benchmark entries
  • Check who makes screening decisions
    Current documentation assigns ranking to the model and relevance labels to the human. Simulation uses a dataset whose labels are already known.
  • Check the stopping threshold
    The progress docs confirm no default threshold. The SAFE paper proposes four phases and explicitly warns that its numeric examples are not exact rules.
  • Check version, licence and data handling
    PyPI lists 3.0.8, Apache-2.0 and Python 3.10+. PyPI and the docs state no usage or user-data collection; the docs describe LAB Server as self-hosted.
None recorded
14Alternatives tracked
  • Rayyan
  • Covidence
  • Elicit
  • EPPI-Reviewer
  • Consensus
  • Scite
  • Semantic Scholar
  • Rayyan
  • Connected Papers
15Pricing checked2026-10-122026-10-13
16Last verified2026-10-122026-10-13
16 metrics

Sixteen numbers, one shared midline.

ASReview LABvsElicit

These tools sit in different sets (Systematic review vs Research assistant), so picking one over the other is mostly a question of which job you're doing. ASReview LAB wins on citation honesty, source quality, privacy controls, and value for money; Elicit wins on speed. For the canonical workflow, pick Elicit. Use ASReview LAB when its niche specifically wins above.

  1. Editorial fit
    ASReview LAB70Elicit59
  2. Source quality
    ASReview LAB70Elicit52
  3. Citation honesty
    ASReview LAB75Elicit70
  4. Privacy controls
    ASReview LAB85Elicit46
  5. Value for money
    ASReview LAB85Elicit56
  6. Speed
    ASReview LAB40Elicit58
  7. Integration
    ASReview LAB65Elicit75
  8. Onboarding
    ASReview LAB40Elicit58
  9. Reliability
    ASReview LAB0Elicit70
  10. Feature depth
    ASReview LAB75Elicit100
  11. Data portability
    ASReview LAB80Elicit55
  12. Transparency
    ASReview LAB80Elicit100
  13. Ecosystem reach
    ASReview LAB65Elicit75
  14. Documentation
    ASReview LAB70Elicit52
  15. Independence
    ASReview LAB85Elicit46
  16. Affordability
    ASReview LAB85Elicit67
ASReview LAB · avg 67 · wins 9 of 160avg 65 · wins 7 of 16 · Elicit
EvidenceASReview LAB 4 · Elicit 0
  1. 01Editorial fit
    70
    59
  2. 02Source quality
    70
    52
  3. 03Citation honesty
    75
    70
  4. 04Privacy controls
    85
    46
PrivacyASReview LAB 1 · Elicit 3
  1. 05Value for money
    85
    56
  2. 06Speed
    40
    58
  3. 07Integration
    65
    75
  4. 08Onboarding
    40
    58
CapabilityASReview LAB 1 · Elicit 3
  1. 09Reliability
    0
    70
  2. 10Feature depth
    75
    100
  3. 11Data portability
    80
    55
  4. 12Transparency
    80
    100
Cost & fitASReview LAB 3 · Elicit 1
  1. 13Ecosystem reach
    65
    75
  2. 14Documentation
    70
    52
  3. 15Independence
    85
    46
  4. 16Affordability
    85
    67
TOOL A · ASReview LAB
Systematic review
ASReview LAB
70/100
ROLELocal active-learning screening for systematic reviews
Editorial fit
70
Source quality
70
Citation honesty
75
Privacy controls
85
Value for money
85
Speed
40
FREEASReview LAB
LightweightVerified 2026-10-12VP·METHOD

Local active-learning screening for systematic reviews — Free, open-source title and abstract screening: ASReview LAB learns from your relevance labels and re-ranks imported records locally, but the stopping decision is still yours.

TOOL B · Elicit
Research assistant
Elicit
59/100
ROLEPaper discovery, evidence-table extraction, and systematic-review support
Editorial fit
59
Source quality
52
Citation honesty
70
Privacy controls
46
Value for money
56
Speed
58
FREEBasic
$132/YRper user
$468/YRper user
FlagshipVerified 2026-10-13VP·METHOD

Paper discovery, evidence-table extraction, and systematic-review support — AI research assistant for searching, summarizing, chatting with papers, screening studies, extracting fields, generating research reports, and supporting systematic reviews across large scientific corpora and uploaded documents.

Privacy facets

Where each tool is actually private.

ASReview LABElicit
Training on your dataYesYes: In the standard local workflow, ASReview trains a model on your project's relevance labels; this is local active learning, not vendor-cloud training. The developers state that ASReview LAB collects no usage or user data. On a self-hosted server, your operator controls the data environment. Optional extensions and hosted demos need separate checks; their external API use is not verified here. Sources: ASReview introduction and PyPI, checked 12 Oct 2026 (linked in Sources).UnknownUnknown: Elicit's privacy policy does not say whether content from the free Basic plan, paid Pro or team-focused Scale plan is used to train AI models, or explain a training-specific opt-out. Do not assume that paying switches training off. Elicit lists “No training on your data by default” for Enterprise. Its API terms say input data is not used to train AI models unless you separately opt in. For the web plans, ask [email protected] to confirm the rule before uploading confidential work. Sources: Elicit, Privacy; Pricing; API terms (linked in Sources)
EU data residencyUnknownUnknown: UnknownUnknownUnknown: Unknown
SOC 2 attestationUnknownUnknown: UnknownUnknownUnknown: Unknown
Local-first by defaultYesYes: ASReview LAB runs locally; LAB Server is self-hosted by you or your institution. The local-data statement covers that setup, not an unverified hosted demo or every optional extension.UnknownUnknown: Unknown
Capability split

What each tool does uniquely.

Only ASReview LAB12

  • Title and abstract screening with human relevance labels
  • Active-learning ranking updated from your labels
  • CSV, RIS and Excel dataset import
  • Prior-knowledge labels to initialise the model
  • Preconfigured ELAS models and custom model components
  • Simulation on fully labelled datasets
  • Editable labels, tags and screening notes
  • Automatic hiding of duplicate titles and texts in version 3
  • Progress dashboard with a reviewer-set stopping threshold
  • Export of labels and records to CSV, TSV, Excel or RIS
  • Export and re-import of an entire .asreview project
  • Self-hosted LAB Server with authentication and multiple reviewers

Shared0

No overlap detected.

Only Elicit11

  • Search across 138M+ papers
  • ClinicalTrials.gov source support
  • Paper chat with full-text access
  • Zotero import
  • Automated Reports
  • Systematic Review Workflow
  • Screening up to 5,000 papers on Pro and 40,000 on Enterprise
  • Extraction columns
  • Sentence-level citations
  • PRISMA/methods/search-strategy report outputs
  • API access
Failure modes

Where each tool breaks.

ASReview LAB

  • A run of irrelevant records does not prove there are no relevant studies left. Stopping early can miss studies; the dashboard has no default threshold and the docs say the ideal threshold is still being researched.
  • The model can only rank the records you import. An incomplete search or missing titles and abstracts can leave relevant studies outside the useful screening pool.
  • Your labels train the ranking. The SAFE paper warns that a seed set chosen only from familiar key papers can bias the model, and recommends a random training sample.
  • Free software still needs installation and maintenance. The current package requires Python 3.10 or later; a shared server also needs someone responsible for hosting and access.
  • Screening is not the whole review. Full-text eligibility checks, extraction, risk-of-bias assessment and reporting still need a separate process.
  • Automatic duplicate hiding in version 3 changes what appears during screening. PyPI says hidden duplicates can be included in exports; do not treat the visible screening count as a complete deduplication report.

Elicit

  • Pricing jumps quickly from free to Pro/Scale
  • Structured tables and reports still require human validation
  • Uploaded papers/Zotero imports create cloud privacy boundary
  • Corpus and full-text coverage vary by discipline
  • Vendor accuracy claims need independent reproduction
  • Basic plan limits can distort real review workflows
Pricing

Pulled straight from each dossier.

ASReview LAB

Systematic review
  • FREEASReview LAB

raw · Free, open source under Apache-2.0. No paid software tiers found in the checked sources. PyPI lists ASReview LAB 3.0.8, released 18 Jun 2026, requiring Python 3.10 or later. Local installation has no licence fee; your own hosting, IT support and setup time are separate costs.

Elicit

Research assistant
  • FREEBasic
  • $132/YRper user
  • $468/YRper user

raw · Basic free: limited Research Agent and Research Reports; unlimited search/summaries/chat · Academic pricing, per user and billed yearly: Plus $11/mo ($132/yr), Pro $39/mo ($468/yr), Scale $89/mo ($1,068/yr) · Industry pricing, per user and billed yearly: Pro $49/mo ($588/yr), Scale $169/mo ($2,028/yr) · Enterprise custom; no training on customer data by default (vendor).

Verdict

Which tool to actually pick.

These tools sit in different sets (Systematic review vs Research assistant), so picking one over the other is mostly a question of which job you're doing. ASReview LAB wins on citation honesty, source quality, privacy controls, and value for money; Elicit wins on speed. For the canonical workflow, pick Elicit. Use ASReview LAB when its niche specifically wins above.

Open ASReview LAB's dossierOpen Elicit's dossier
Try ASReview LAB ↗Try Elicit ↗

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Questions this comparison answers

Every answer below is assembled from the dated fields on this page. Nothing is written separately for search.

Should you use ASReview LAB or Elicit?

These tools sit in different sets (Systematic review vs Research assistant), so picking one over the other is mostly a question of which job you're doing. ASReview LAB wins on citation honesty, source quality, privacy controls, and value for money; Elicit wins on speed. For the canonical workflow, pick Elicit. Use ASReview LAB when its niche specifically wins above.

What can ASReview LAB do that Elicit cannot?

Capabilities documented on the ASReview LAB tool but not the Elicit tool:

  • Title and abstract screening with human relevance labels
  • Active-learning ranking updated from your labels
  • CSV, RIS and Excel dataset import
  • Prior-knowledge labels to initialise the model
  • Preconfigured ELAS models and custom model components
  • Simulation on fully labelled datasets
  • Editable labels, tags and screening notes
  • Automatic hiding of duplicate titles and texts in version 3
  • Progress dashboard with a reviewer-set stopping threshold
  • Export of labels and records to CSV, TSV, Excel or RIS
  • Export and re-import of an entire .asreview project
  • Self-hosted LAB Server with authentication and multiple reviewers

What can Elicit do that ASReview LAB cannot?

Capabilities documented on the Elicit tool but not the ASReview LAB tool:

  • Search across 138M+ papers
  • ClinicalTrials.gov source support
  • Paper chat with full-text access
  • Zotero import
  • Automated Reports
  • Systematic Review Workflow
  • Screening up to 5,000 papers on Pro and 40,000 on Enterprise
  • Extraction columns
  • Sentence-level citations
  • PRISMA/methods/search-strategy report outputs
  • API access

Is ASReview LAB or Elicit cheaper?

ASReview LAB: ASReview LAB Free. Elicit: Basic Free · Academic Plus $132/yr · Academic Pro $468/yr · Academic Scale $1,068/yr · Industry Pro $588/yr · Industry Scale $2,028/yr · Enterprise Custom.

How do ASReview LAB and Elicit score against each other?

On editorial fit, ASReview LAB scores 70 out of 100 and Elicit scores 59 out of 100. The score is a fit judgement for source-heavy research work, not a quality ranking — read both dossiers before you pick.