Cebra vs ScreenplayIQ

Side-by-side comparison of features, pricing, and ratings

Analysis reviewed Live tool data as of 2026-10-10
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At a glance

DimensionCebraScreenplayIQ
Target UserNeuroscientists, ML researchersScreenwriters, producers, studio execs
Core FunctionContrastive learning embeddings for neural/behavioral time-seriesAI screenplay analysis with box office prediction
Key FeatureMulti-session support, DeepLabCut integration, GPU accelerationBox office performance prediction, PitchTrailer integration
IntegrationsDeepLabCut, PyTorch, scikit-learn, matplotlib, plotly, DockerPitchTrailer
Best ForCalcium imaging, electrophysiology, behavioral video dataFeature film scripts under 150 pages
Cebra
Cebra

Open-source Python library for interpretable neural embeddings from behavioral and neural time series.

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ScreenplayIQ
ScreenplayIQ

AI screenplay analysis on your draft — logline, comps, character emotional journey charts, and inline proofread fixes

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Pricing
Free
Paid
Plans
—
~$24 TV / ~$38 Feature (based on page length)
~$48 TV / ~$78 Feature (based on page length)
~$60 TV / ~$98 Feature (based on page length)
~$24 TV / ~$38 Feature
Popularity
3 views
7.5k views
Skill Level
Advanced
Intermediate
API Available
Platforms
API
WebAPI
Categories
📊 Data & Analytics🔬 Research & Education
📖 Fiction & Screenwriting
Features
Self-supervised contrastive learning for neural time series
Supervised embedding using auxiliary behavioral labels
Hybrid hypothesis- and discovery-driven analysis modes
Consistency metrics for comparing latent spaces across sessions
k-nearest neighbor decoding from learned embeddings
Supports calcium imaging and electrophysiology datasets
Embeddings for 2-photon and Neuropixels recordings
Uses DeepLabCut pose estimates and DINO video frame features as labels
scikit-learn-compatible estimator API
Time-series attribution maps (AISTATS 2025)
Multi-session and multi-species dataset support
GPU-accelerated training via PyTorch
Built-in embedding plots with matplotlib and plotly
Installable via conda, pip, or Docker
Colab demo notebooks in the documentation
Generate a logline from a full screenplay draft
Surface comparable films and TV shows for market fit
Break a script into structured story points
Produce a full synopsis without reading the whole draft
Write character synopses for the full cast
Create visual representations of characters
Chart each character's emotional journey across the script
Deliver a full assessment analysis of the draft
Break down the story's themes
Proofread report with inline highlights on every issue
Recommend a fix for each flagged issue
Build a custom analysis package outside the presets
Price analysis by page length, quoted separately for TV and features
Store uploaded scripts in encrypted, siloed storage
WGA-compliant handling of submitted scripts
Integrations
DeepLabCut
PyTorch
scikit-learn
matplotlib
plotly
Docker
conda
pip
GitHub
Google Colab

What real users say: Cebra vs ScreenplayIQ

Not marketing copy and not our opinion — a structured sweep of public discussion (reviews, forums, communities and video comments), showing what people praise and what they complain about for each tool.

Cebra

3 mentions across 2 sources · 50% positive — mixed (averaged across 2 sources)

Hacker News, Lemmy

What users praise

  • • Self-supervised contrastive learning reveals structure in neural time-series.
  • • Supports both hypothesis-driven and discovery-driven embedding modes.
  • • Uses behavioral labels to create interpretable, consistent embeddings.
  • • scikit-learn-style API simplifies integration with existing Python pipelines.

What frustrates them

  • • Almost no community validation or user testimonials available.
  • • Steep learning curve for those without neuroscience background.
  • • Unclear support for non-neural time-series data types.
  • • No integrations with cloud platforms or MLOps tools listed.

Researched Jul 3, 2026

ScreenplayIQ

No verifiable community signal. We scanned public discussion on Oct 7, 2026 and found posts matching the name “ScreenplayIQ”, but could not establish that they are about this product rather than something else sharing its name. Rather than publish a score built on the wrong subject, we publish none.

Who should pick which

  • Screenwriter seeking market feedback
    Pick: ScreenplayIQ

    ScreenplayIQ provides box office prediction, structural beat sheets, and genre classification tailored for feature film scripts.

  • Neuroscientist analyzing neural recordings
    Pick: Cebra

    Cebra handles calcium imaging and electrophysiology data with contrastive learning, integrated with DeepLabCut and scikit-learn.

  • Producer evaluating script slate
    Pick: ScreenplayIQ

    Pro/Studio plans provide comparative market data, collaborative workspace, and API for batch analysis.

  • ML researcher exploring time-series embeddings
    Pick: Cebra

    Cebra offers self-supervised and hybrid modes, consistency metrics, and GPU-accelerated training via PyTorch.

  • Hobbyist writer with limited budget
    Pick: ScreenplayIQ

    Free tier allows 1 script analysis per month, sufficient for occasional feedback without cost.

Frequently Asked Questions

Can ScreenplayIQ analyze TV pilots or short films?

No, it is designed for feature films only (under 150 pages).

Does Cebra require coding?

Yes, Cebra is a Python library; users need programming experience.

What data types does Cebra support?

Calcium imaging, electrophysiology, behavioral videos, and pose tracking (via DeepLabCut).

Is ScreenplayIQ's box office prediction reliable?

It uses comparative market data; results are indicative, not guarantees.

Does Cebra offer a GUI?

No, it is code-only; it integrates with matplotlib/plotly for plotting.

Can I collaborate with my team in ScreenplayIQ?

Yes, Studio plan supports up to 5 users in a collaborative workspace.

Is Cebra free for commercial use?

Yes, it is MIT-licensed; free for academic and commercial use.

Does ScreenplayIQ integrate with other screenwriting tools?

It integrates with PitchTrailer; no direct integration with Final Draft or others mentioned.

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Last reviewed: July 3, 2026