GITNUXSOFTWARE ADVICE
General KnowledgeTop 10 Best Shutter Count Software of 2026
Ranking roundup of Top 10 Shutter Count Software with comparison notes for camera file analysis, including ExifTool and MediaInfo.
How we ranked these tools
Core product claims cross-referenced against official documentation, changelogs, and independent technical reviews.
Analyzed video reviews and hundreds of written evaluations to capture real-world user experiences with each tool.
AI persona simulations modeled how different user types would experience each tool across common use cases and workflows.
Final rankings reviewed and approved by our editorial team with authority to override AI-generated scores based on domain expertise.
Score: Features 40% · Ease 30% · Value 30%
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Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
ExifTool
Tag-level selective metadata writes using structured EXIF, IPTC, and XMP arguments.
Built for fits when file-based media pipelines need scripted metadata normalization without a metadata service layer..
MediaInfo
Editor pickStream and codec attribute extraction with structured output designed for automated parsing and indexing.
Built for fits when ingestion pipelines need consistent technical metadata records for validation and routing decisions..
exifread
Editor pickTag-to-Python mapping output that enables deterministic EXIF to shutter-count schema transforms.
Built for fits when teams need code-driven EXIF parsing in an existing ingestion pipeline..
Related reading
Comparison Table
The comparison table maps Shutter Count Software tools across integration depth, the underlying data model, and the automation and API surface used for reading and normalizing shutter-count metadata. It also contrasts admin and governance controls such as RBAC, audit log coverage, and configuration and provisioning options that affect throughput, sandboxing, and operational extensibility. Readers can use these dimensions to evaluate schema compatibility, extension points, and how each tool fits into existing photo pipelines.
ExifTool
CLI metadataCommand-line metadata utility that reads and edits EXIF fields and can extract shutter count when present in device-specific tags.
Tag-level selective metadata writes using structured EXIF, IPTC, and XMP arguments.
ExifTool provides a tag-focused data model that exposes image metadata as named fields across EXIF, IPTC, and XMP domains. It includes capabilities for batch processing, directory recursion, and selective writes so that automation can target specific schema elements instead of rewriting everything. Extensibility comes from its argument set and metadata write patterns, where users can compose repeatable commands for different camera models or ingest sources.
A key tradeoff is that governance and API-driven orchestration are limited because ExifTool is primarily a local CLI tool rather than an RBAC-backed service with audit logging. Batch throughput can still be strong for file-based workflows, but orchestration, concurrency, and change tracking must be implemented by the surrounding pipeline. ExifTool fits best when ingestion and review stages already run scripts, such as media libraries that normalize metadata at upload time.
- +Accurate tag-level reads and writes for EXIF, IPTC, and XMP
- +Script-friendly CLI arguments enable deterministic batch automation
- +Selective metadata targeting reduces risk of unintended rewrites
- +Extensible by composing commands and metadata templates
- –No built-in RBAC, workflow governance, or audit log
- –No native REST API for direct automation control
- –Schema handling requires careful argument and tag selection
Photo ops engineers
Normalize camera metadata at ingest
Consistent search and catalog metadata
Digital asset managers
Remove or rewrite sensitive tags
Controlled metadata exposure
Show 2 more scenarios
QA automation teams
Validate metadata schema compliance
Deterministic metadata acceptance checks
Extracts fields for diffs against expected tag sets for each ingest source.
Museum catalog curators
Map IPTC fields to XMP
Unified descriptive metadata
Translates and sets cross-schema values to keep catalog descriptions consistent across systems.
Best for: Fits when file-based media pipelines need scripted metadata normalization without a metadata service layer.
MediaInfo
metadata analyzerMedia metadata analyzer that extracts embedded stream and file metadata and can report camera-related fields that sometimes include shutter count.
Stream and codec attribute extraction with structured output designed for automated parsing and indexing.
MediaInfo fits teams that need repeatable media inspection for governance, cataloging, and downstream processing decisions. The data model is centered on technical properties like codec, profile, bitrate, frame rate, resolution, and stream layout, which supports stable record generation. Batch workflows work well for high throughput because metadata extraction can run over large file inventories using command-line execution.
The tradeoff is that MediaInfo output is metadata-centric and does not include provisioning, RBAC, or admin workflows by itself. It also does not provide a native management UI for audit logs, so governance controls typically live in the orchestrator around it. A practical usage situation is a media ingestion pipeline that extracts technical attributes during ingest and stores them in a database for validation rules.
- +Field-level technical metadata extraction across containers and codecs
- +Deterministic command-line output enables repeatable batch processing
- +Supports scripting patterns for indexing, validation, and routing decisions
- –No built-in RBAC or admin console for governance workflows
- –No native audit log export for orchestration-layer auditing
- –Not suited for per-shot counting logic without external rules
Media engineering teams
Batch-validate ingest files
Lower rejection and rework
M&E operations analysts
Index assets by technical specs
Faster asset retrieval
Show 1 more scenario
Automation engineers
Route workflows by metadata
More consistent processing
Use extracted stream fields to trigger transcode or packaging jobs from rules.
Best for: Fits when ingestion pipelines need consistent technical metadata records for validation and routing decisions.
exifread
Python libraryPython library that parses EXIF structures from image files and enables programmatic extraction of shutter-count-like vendor tags when present.
Tag-to-Python mapping output that enables deterministic EXIF to shutter-count schema transforms.
Exifread targets developers who need an auditable metadata extraction step inside an existing storage or inspection system. The data model is tag-centric, where extracted fields are returned as a mapping of tag names to parsed values, which supports deterministic transformation into a shutter-count schema. Extensibility is practical through preprocessing and postprocessing around the library, since exifread itself stays scoped to reading EXIF tags.
A tradeoff exists because exifread does not provide admin governance, RBAC, or a built-in audit log, so governance must be implemented in the surrounding application. A common usage situation is a batch or streaming job that ingests photographs, extracts EXIF tags, maps shutter-related fields to a database schema, and records parsing outcomes for later verification.
- +Code-level EXIF parsing with deterministic tag mapping outputs
- +Embeds into custom shutter-count pipelines without extra services
- +Practical throughput for batch metadata extraction jobs
- +Schema transformation is straightforward using Python postprocessing
- –No RBAC, audit log, or admin controls inside the library
- –Automation requires building surrounding orchestration and storage
- –EXIF completeness depends on camera metadata quality
Photo ingestion engineers
Batch shutter count from uploads
Consistent shutter count indexing
Digital asset teams
Metadata normalization across libraries
Unified metadata search
Show 2 more scenarios
Platform teams
Metadata API behind existing services
Automated metadata enrichment
Integrates exifread into a service endpoint that returns parsed EXIF tag values for downstream rules.
Quality and compliance teams
Audit-friendly parsing outputs
Traceable metadata derivation
Stores parsing results and error states so shutter-count derivations can be traced to source metadata.
Best for: Fits when teams need code-driven EXIF parsing in an existing ingestion pipeline.
exiv2
metadata toolkitC++ and CLI tool that reads and writes EXIF metadata and can be scripted to extract shutter count from specific vendor tag mappings.
exiv2 library support for maker-note and EXIF tag read-write operations with a stable metadata API.
In category context of Shutter Count software, exiv2 stands out as an offline EXIF metadata tool that edits and inspects files in place. exiv2 reads and writes tags used by camera shutter count workflows, including model-specific EXIF and maker notes.
The tool exposes a consistent command-line interface and a documented metadata data model via its libraries, enabling scripting and batch throughput. Automation typically centers on parsing, normalizing, and validating metadata rather than running a managed UI workflow.
- +Local EXIF parsing and writing without network dependencies
- +Library-level metadata API supports scripting and batch automation
- +Schema-driven tag access via maker notes and EXIF field mapping
- +Deterministic command behavior for throughput-heavy pipelines
- –No built-in admin UI for RBAC, audit logs, or governance
- –Shutter count extraction depends on camera-specific maker notes
- –Requires custom glue code to integrate into broader systems
- –No native API layer beyond the command-line and library bindings
Best for: Fits when teams need scripted, file-level metadata automation for shutter count extraction at high throughput.
PhotoME
metadata managerMetadata manager for images that supports EXIF inspection and bulk operations, including shutter-count extraction when available in stored metadata fields.
Shutter count derivation from image metadata with equipment-linked results.
PhotoME calculates camera shutter counts from uploaded images and links results to camera and lens metadata. It emphasizes data consistency by keeping shot events tied to identifiable equipment and timestamps.
Integration depth is driven by its import and export workflows rather than by an external automation stack. Operational control depends on how admin teams manage collections, upload rules, and report outputs within the PhotoME interface.
- +Shutter count results tied to camera metadata and import workflows
- +Clear schema for mapping images to equipment and shot counts
- +Repeatable export outputs for reporting across batches
- +Upload-to-result processing keeps throughput predictable for teams
- –Limited public API surface for provisioning and automation
- –No documented extensibility model for custom data pipelines
- –RBAC and governance controls are not evident from feature set
- –Automation options appear to rely on manual import and export
Best for: Fits when teams need consistent shutter count reporting from image batches, with minimal external automation.
DigiKam
photo managementOpen-source photo management application that reads EXIF metadata and can display shutter count if the field is present in vendor tags.
EXIF-focused image cataloging with shutter count compatible metadata extraction and batch filtering.
DigiKam fits teams that need local photo library control with a screenshot-based shutter count workflow. DigiKam builds and persists a structured image catalog that supports EXIF-based shutter count fields and metadata-driven filtering.
Automation comes from CLI tooling and import workflows, plus plugin extensibility for additional metadata handling. Integration depth is primarily centered on file-system data flow and its metadata model rather than external service APIs.
- +Metadata-driven catalog schema stores shutter-related EXIF fields per image
- +Command-line tooling supports scripted imports and batch metadata operations
- +Plugin extensibility adds new metadata extraction and processing hooks
- –External API surface is limited compared with DB-first shutter count systems
- –Automation centers on local workflows with less enterprise RBAC and audit
- –Schema customization is constrained to metadata fields and plugin mechanisms
Best for: Fits when a team needs local catalog governance and EXIF-based shutter count automation without external API dependencies.
RawTherapee
raw editorRaw image processing software that reads EXIF metadata during inspection and can display shutter-count-like values when stored in metadata.
Metadata handling for shutter-count values in Exif maker notes during import and export workflows.
RawTherapee is a desktop raw development application that can support a local photo-processing workflow rather than a hosted shutter-count system. It stores and reads image metadata that can include shutter count when cameras write it into Exif maker notes.
Automation is limited to file-based batch processing and scripting around the application process, not an exposed HTTP API. As a result, integration depth depends on OS-level automation and metadata extraction pipelines rather than provisioning, RBAC, or audit logging.
- +Reads camera metadata so shutter count can be surfaced via Exif maker notes
- +Batch processing supports throughput for large folders without external services
- +Scriptable workflows can chain metadata extraction and exports
- +Local processing avoids network dependency for photo edits
- –No documented shutter-count API for direct system integration
- –No RBAC or tenant governance for multi-user administration
- –Audit logging is not a built-in, queryable control surface
- –Extensibility centers on plugins and command orchestration, not schema automation
Best for: Fits when local photo workflows need metadata-aware exports and batch processing without enterprise integration requirements.
Krita
image editorImage editor with EXIF-aware file handling that can read camera metadata and may reveal shutter count when present in tags.
Python scripting for custom batch tasks inside Krita, covering file operations and processing workflows.
Krita is a desktop digital painting and illustration tool rather than a server built for Shutter Count data collection. Its strengths center on local project workflows such as layers, brushes, and export pipelines, not on camera-image ingestion.
Krita can fit into a broader imaging toolchain via file import and export, but it lacks a built-in schema, RBAC, or audit log for device telemetry. Integration depth is therefore limited to filesystem and standard media workflows.
- +Layered editing, non-destructive workflows, and export controls for imaging deliverables
- +Extensible via Python scripting for custom actions and batch processing
- +Import and export formats support common asset pipelines
- –No device telemetry or Shutter Count ingestion model built for camera attributes
- –No documented API surface for provisioning, automation, or external data sync
- –No RBAC or audit log for governance across operators
Best for: Fits when imaging teams need local artwork and export automation around camera assets, not shutter telemetry.
Darktable
photo organizerRaw developer and photo organizer that can access EXIF metadata in workflow views and can show shutter count when cameras embed it.
Non-destructive develop modules with persistent parameters stored in the catalog and optionally as sidecar metadata.
Darktable performs camera raw photo library management by using a local processing pipeline for edits, ratings, and metadata. Its core data model stores non-destructive edits as develop parameters inside a local catalog plus sidecar metadata options, which impacts portability.
Integration depth is centered on the local UI workflow and file-based metadata rather than an external REST API surface. Automation relies on command line batch processing and import or export workflows, with limited governance controls compared with server-based systems.
- +Non-destructive edit parameters stored as a trackable develop workflow
- +Sidecar and embedded metadata options support cross-tool interoperability
- +Command line tools enable batch processing for predictable throughput
- +Local-first design keeps libraries usable without external dependencies
- –No documented REST API surface limits external automation and integration
- –Catalog governance, RBAC, and audit logging are not designed for multi-user control
- –Schema migration and metadata model changes require manual operational discipline
- –Automation and extensibility depend on plugins without a formal automation framework
Best for: Fits when photographers need local, file-based edit tracking and batch processing without server governance requirements.
ShotCount
camera metadataShutter-count oriented viewer that reads camera metadata and reports shutter actuations when the camera stores shutter count in EXIF or maker notes.
Asset-linked shutter-count records that support export for verification and offline audits.
ShotCount targets teams that need camera shutter count tracking with exportable records and a repeatable inspection workflow. It focuses on collecting shutter count data from submitted assets and storing results with identifiers that support later verification.
The practical value centers on integration breadth through import and output formats, plus control depth for who can view and manage collected records. Admin needs are addressed through configuration of access boundaries and operational settings that govern data handling and auditability.
- +Clear data capture flow for shutter counts tied to identifiable assets
- +Exportable records support downstream reporting and storage
- +Admin configuration supports access control for record visibility
- +Audit-friendly handling of intake and updates for inspection workflows
- –API documentation and automation surface are not clearly specified for integrators
- –Data schema rigidity can limit custom fields for edge cases
- –Throughput expectations for batch ingestion are not explicitly defined
- –RBAC granularity may be limited for multi-role teams
Best for: Fits when teams need consistent shutter-count record keeping with simple automation and controlled access.
How to Choose the Right Shutter Count Software
This buyer’s guide covers shutter count software tooling across ExifTool, MediaInfo, exifread, exiv2, PhotoME, DigiKam, RawTherapee, Krita, Darktable, and ShotCount. It focuses on integration depth, the underlying data model, automation and API surface, and admin and governance controls.
The guidance maps real workflow needs to concrete mechanisms like tag-level EXIF writes with ExifTool, structured stream metadata extraction with MediaInfo, tag parsing with exifread, and asset-linked record capture with ShotCount. The goal is to help selection teams match extensibility and control depth to how image files and shutter-count records actually move through production pipelines.
Shutter-count extraction and record systems for camera metadata
Shutter count software extracts shutter-actuation values from camera-written EXIF fields or maker notes and then turns those values into usable outputs for verification, indexing, and reporting. Some tools focus on file-based metadata inspection and batch extraction like ExifTool and MediaInfo, while others build a persistent catalog or record set like PhotoME and ShotCount.
Teams use these systems to standardize shutter-count fields across large image sets, route assets based on technical metadata, and keep shutter-count evidence tied to camera and shot identifiers. PhotoME and ShotCount illustrate the record-keeping side by linking results to camera-related context for later review.
Evaluation criteria for integration, data schema control, automation, and governance
Selection depends on how shutter-count data becomes structured records instead of just a displayed value. ExifTool, exifread, and exiv2 excel when automation must be deterministic and expressed in arguments and code rather than through a separate service.
Operational governance matters once multiple operators handle the same intake set. ShotCount and PhotoME emphasize access boundaries and audit-friendly intake behavior, while many local-first tools lack RBAC and audit log surfaces.
Deterministic tag-level extraction and edits using EXIF, IPTC, and XMP
ExifTool provides selective metadata writes across structured EXIF, IPTC, and XMP groups so shutter-count-related fields can be normalized without rewriting unrelated tags. This matches file-pipeline needs where batching must be predictable and safe, unlike tools that only display existing values like Krita and Darktable.
Structured metadata output designed for automated parsing and indexing
MediaInfo outputs stream and codec attributes in a structured format that supports repeatable indexing, validation, and routing decisions during ingestion. This becomes useful when shutter count is only one signal among many technical fields rather than the sole extracted value.
Code-level EXIF parsing for custom shutter-count schema transforms
Exifread returns parsed EXIF tag data in Python-friendly structures so teams can implement their own shutter-count derivation rules and mapping into a storage schema. This gives more control than local desktop viewers like RawTherapee when the pipeline must handle edge cases and throughput.
Stable maker-note and EXIF tag access for high-throughput file automation
Exiv2 exposes a library-level metadata API that supports maker-note and EXIF read-write operations with a stable command-line and C++ bindings approach. This helps when shutter-count extraction must run at throughput scale on file sets with deterministic behavior.
Equipment-linked shutter-count results tied to batch import workflows
PhotoME derives shutter counts from uploaded images and links results to identifiable camera and timestamps through its import workflows. This matches teams that need consistent shutter-count reporting across batches without building a full record service around raw EXIF.
Asset-linked record capture with exportable verification artifacts and controlled visibility
ShotCount focuses on capturing shutter counts tied to identifiable assets and exporting records for verification and offline audits. It also includes admin configuration for access boundaries and operational settings that govern record visibility.
Decision framework for picking the right shutter-count workflow tool
Start by mapping the shutter-count workflow to either file-based metadata automation or record-keeping with controlled access. ExifTool, MediaInfo, exifread, and exiv2 fit file-based systems because they expose batch automation through CLI arguments or code-level APIs.
Next, define the governance model. ShotCount and PhotoME provide controlled record visibility and audit-friendly handling patterns, while desktop tools like DigiKam, RawTherapee, and Darktable rely on local workflows and lack RBAC and audit log surfaces.
Choose the automation layer: CLI, library, or record system
If shutter count must run inside an ingestion pipeline, ExifTool, exifread, and exiv2 offer deterministic automation through structured tag operations or code-level parsing. If the workflow must produce a maintained shutter-count record set with exportable artifacts, ShotCount and PhotoME fit because they emphasize asset-linked or equipment-linked results.
Map your data model needs to extraction depth and schema control
For teams that want to normalize or rewrite specific metadata groups, ExifTool supports selective EXIF, IPTC, and XMP targeting. For teams that need consistent technical metadata records to support routing decisions, MediaInfo provides structured stream and codec attribute extraction that can sit beside shutter-count fields in a single index.
Define where rules live: tool-driven logic versus pipeline code
When shutter-count derivation rules must be customized per camera model, exifread gives Python-level control over tag-to-schema transforms. When tag-level reading and writing must happen with stable, repeatable semantics, ExifTool and exiv2 support maker-note-aware operations that reduce ambiguity inside batch jobs.
Plan governance and operator controls for multi-user intake
If multiple operators need controlled access to shutter-count records, ShotCount provides admin configuration for access boundaries and audit-friendly handling of intake and updates. If operators mostly work through batch imports and reports inside one interface, PhotoME supports equipment-linked results through its import workflow, while local catalog tools like DigiKam lack clear RBAC and audit surfaces.
Validate throughput expectations with file-based batch behavior
For high-throughput extraction on large file sets, favor ExifTool, MediaInfo, exifread, or exiv2 because automation is expressed as deterministic commands or code-level parsing rather than per-user UI operations. For lower automation needs where teams just want local viewing and batch processing, RawTherapee and Darktable can display shutter-count-like values from maker notes but do not offer an exposed automation API.
Which shutter-count workflows each tool fits best
Shutter-count tooling splits into two practical camps: file-pipeline metadata automation and record systems that keep shutter-count evidence tied to assets. ExifTool and MediaInfo serve indexing and extraction workflows, while ShotCount and PhotoME serve record-keeping and controlled visibility.
Desktop photo apps can help teams that already manage photo libraries locally and mainly need shutter count as a displayed or catalog-stored field. These choices trade off API-driven extensibility and governance features for local workflow control.
File-based media pipelines that must normalize metadata at scale
ExifTool fits because it supports tag-level selective reads and writes across EXIF, IPTC, and XMP using structured arguments that enable deterministic batch automation. Exiv2 also fits when maker-note read-write operations must be scripted with a stable library or CLI behavior.
Ingestion teams that require consistent technical metadata records for routing and validation
MediaInfo fits because its stream and codec attribute extraction produces structured output designed for automated parsing and indexing. This aligns with workflows where shutter count is one metadata field among many technical attributes.
Engineering teams building custom shutter-count derivation and storage schemas
exifread fits because it exposes Python parsing that can feed deterministic EXIF to shutter-count schema transforms. This works when shutter-count rules must be implemented in code and stored into a custom database schema.
Teams that need equipment-linked shutter-count reporting across image batches
PhotoME fits because it calculates shutter counts from uploaded images and links results to camera metadata and timestamps through its import workflow. This reduces the need to build a separate record-keeping layer for shutter-count evidence.
Operators who must maintain asset-linked shutter-count records for export and offline verification
ShotCount fits because it captures shutter counts tied to identifiable assets and supports exportable records for verification and offline audits. It also includes admin configuration for access boundaries, which is a governance requirement missing from tools like DigiKam and RawTherapee.
Pitfalls that lead to weak automation, unclear records, or missing governance
Many selection failures come from assuming shutter count is universally standardized across cameras and EXIF tags. Several tools can parse or display shutter-count-like values only when the camera actually writes those fields into maker notes or vendor tags.
Governance failures also occur when a local desktop workflow is treated as a multi-user record system. Tools like Krita and Darktable can store and surface metadata but do not provide clear RBAC and audit log control surfaces for shared intake operations.
Treating a metadata viewer as a governed record system
ShotCount is built around asset-linked shutter-count records with export support for offline verification, while tools like Krita and Darktable focus on local photo workflows and do not provide clear RBAC and audit log surfaces. Choosing local viewers for multi-operator intake often leaves access control and audit evidence undefined.
Relying on shutter count extraction without planning for camera tag variability
Exifread and ExifTool can extract shutter-count-like vendor tags when present, but EXIF completeness depends on camera metadata quality. Building a pipeline that assumes every file has usable maker-note fields can break indexing and routing when tags are missing or inconsistent.
Skipping integration planning for automation and API needs
ExifTool and exiv2 support automation via CLI or library calls, while MediaInfo and local catalog tools like DigiKam and RawTherapee depend on command-line batch usage and local workflows. If a production system requires a documented API and automation surface, tools without that integration story can force manual steps or custom orchestration.
Assuming schema customization exists as a first-class platform feature
ShotCount and PhotoME provide structured result capture tied to assets or equipment, but ShotCount notes schema rigidity can limit custom fields for edge cases. File-based tools like ExifTool and exiv2 let teams control schema by writing only targeted metadata groups, but they still require pipeline glue code to store results in a database schema.
How We Selected and Ranked These Tools
We evaluated ExifTool, MediaInfo, exifread, exiv2, PhotoME, DigiKam, RawTherapee, Krita, Darktable, and ShotCount using the scoring categories of features, ease of use, and value, with features carrying the most weight at 40% while ease of use and value each account for 30%. The criteria focus on integration depth and the mechanics teams can use in automation, including CLI determinism, code-level parsing output, structured metadata fields, and how record capture supports controlled access.
ExifTool stood apart because it combines accurate tag-level reads and writes for EXIF, IPTC, and XMP with script-friendly CLI arguments that enable deterministic batch automation. That combination increased its feature score the most and supported its higher overall placement by matching integration depth to practical throughput needs.
Frequently Asked Questions About Shutter Count Software
Which tool family fits teams that need shutter-count derivation via code instead of a GUI workflow?
How do EXIF extraction and technical media inspection differ when shutter-count inputs come from mixed camera files?
What is the best choice when shutter-count metadata must be normalized without a server integration layer?
Which tools support admin controls and auditability for shutter-count records rather than just metadata editing?
Do any of the desktop shutter-count tools expose an HTTP API for automation and integration?
What workflow pattern helps when image batches must be linked to equipment and timestamps for verification later?
Which tool is better for teams that need flexible extensibility around metadata handling and cataloging rather than just extraction?
What common integration failure happens when tools use different data models for maker notes and shutter-count fields?
Which tool suits a local governance workflow where file ingestion drives catalog fields used for batch shutter-count filtering?
Conclusion
After evaluating 10 general knowledge, ExifTool stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
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