COMPARISON
Choose the guarantees your analysis workflow needs.
There are three fundamentally different ways to turn text into decisions: generated reasoning, managed classification, and versioned semantic analysis. Sentiqra is built for the third.
Reasoning can be flexible. Evidence should be reproducible.
Reproducible semantic analysis · 12 production analyzers · Structured outputs
Three architectures, three guarantee models
Before comparing features, compare what each system actually produces - and what it can promise about tomorrow's output.
Prompted LLM
Text → reasoning → generated answer
Maximum flexibility. Output depends on model revision, prompt, context, sampling, and provider behavior.
Moderation API
Text → provider taxonomy → category scores
Managed classification against a fixed provider taxonomy. Convenient, but the provider owns taxonomy, model, and thresholds.
Sentiqra
Text → pinned analyzer → versioned semantic signals → reproducible evidence
Analysis tied to a versioned configuration. Same normalized input, same pinned configuration, same output profile, same canonical result.
Operational comparison
The rows an engineering, security, or compliance team actually evaluates.
| Capability | Sentiqra | Moderation API | Prompted LLM |
|---|---|---|---|
| Output model | Versioned semantic signals | Provider categories | Generated / constrained output |
| Taxonomy | Documented + versioned | Provider-defined | Prompt-defined |
| Configuration pinning | Yes - configuration ID per response | Varies | Model/API dependent |
| Semantic version identity | Explicit | Varies | Usually model name only |
| Output schema stability | Fixed contract | Usually fixed | Prompt/schema dependent |
| Historical replay | Designed for it | Provider-dependent | Difficult across model revisions |
| Re-evaluation of old data | Designed for replay | Varies | Often difficult |
| Explainability | Taxonomy evidence | Category scores | Generated explanation |
| Audit artifact | Config ID + fingerprint + signature | Provider response | Prompt + model response |
| Customer policy separation | Explicit - evidence vs decision | Varies | Often mixed into prompt |
| Drift surface | Analyzer/configuration change | Provider model + taxonomy change | Prompt + model + provider change |
| Free-form reasoning | No | No | Yes |
| Open-ended tasks | No | No | Yes |
The last two rows are intentional: open-ended reasoning is a real LLM strength. Sentiqra is not competing for it.
Qualitative comparison of approach classes, last reviewed September 21, 2026. Re-reviewed at least every 90 days; per-vendor claims on the benchmark pages carry their own verified dates and sources.
Compare at the contract level
Output format is the easiest thing to match - any system can return JSON. The harder questions are whether the vendor commits to configuration pinning, historical replay, stable output semantics, verification tooling, and lifecycle guarantees. Five separations worth testing independently:
Schema validity
Does the output match a documented structure? Most systems - generative included - can promise this.
Repeatability
Does identical input under a pinned version produce the same result - verifiably, not approximately?
Classification quality
Precision and recall on your own evaluation set - measured separately from output format or reproducibility.
Policy decisions
Who owns the decision - the vendor's threshold, or your policy layer consuming signals as evidence?
Evidence authenticity
Is the result artifact signed and verifiable independent of the vendor - or a response you can only store?
A vendor can score well on one separation and poorly on another. Valid JSON is not repeatability; repeatability is not accuracy; a signed receipt is evidence of what the system produced, not that its classification was correct.
Reproducibility is not the same as accuracy.
A reproducible result can still be wrong. Sentiqra's guarantee is that the same supported pinned configuration evaluates the same normalized input consistently - so errors can be investigated, benchmarked, reproduced, and corrected systematically.
That is the claim - not that deterministic output is automatically correct, but that it can be inspected, tested, and governed against a fixed standard. That is harder to maintain when the analysis itself can differ between calls.
A semantic contract, not just a schema
An LLM can return perfectly valid JSON too. Structured output is not the differentiator.
Schema stability
Tells you where the field is. Any model with JSON mode can promise this.
Semantic contract
Tells you what the field means. Signal identifiers carry versioned meaning tied to a documented taxonomy and a pinned configuration.
What one signal carries
Different systems fail differently
The drift surface - everything that can change output without you noticing - is defined by architecture, not vendor promises.
Prompted LLMs drift on
- model revision
- system + user prompt
- few-shot examples
- context window contents
- sampling parameters
- schema interpretation
- provider behavior
Moderation APIs drift on
- provider taxonomy changes
- model upgrades
- category definitions
- threshold changes
Sentiqra changes when
- you move to another supported
- analyzer configuration/version
Output behavior is tied to normalized input, analyzer, and pinned supported configuration. That is the whole surface.
Analysis is not the decision.
Decision flows downstream: human review, enforcement, automation.
Sentiqra does not decide
ban · approve · reject · escalate · block payment
Sentiqra reports
what signals were detected - so your policy owns the decision and the audit trail behind it.
Policy ownership stays with you. That is the separation enterprise governance requires.
Where the boundary is
Use an LLM when you need
- ·open-ended reasoning
- ·document summarization
- ·novel label generation
- ·conversational explanation
- ·arbitrary extraction schemas
- ·multi-step reasoning over changing instructions
Use Sentiqra when you need
- ·stable semantic categories
- ·repeated high-volume analysis
- ·policy separation
- ·historical replay
- ·version-controlled behavior
- ·audit evidence
- ·machine-consumable signals
When replayability is a hard requirement, generation is often the wrong abstraction.
What about building your own classifier?
Fine-tuning BERT, DeBERTa, or ModernBERT is the comparison sophisticated teams raise first. It is a build-vs-buy question, not a model question.
A custom classifier means owning
- ·training data collection + annotation effort
- ·taxonomy design from scratch
- ·GPU infrastructure and model hosting
- ·version lifecycle, monitoring, and retraining
- ·evaluation methodology + multilingual coverage
- ·auditability of every model revision
When it makes sense
When your classification problem is truly unique, you already run an ML platform team, and you can fund the annotation and lifecycle work per language.
When Sentiqra is cheaper
When you need versioned taxonomies across 12 domains and 43 languages platform-wide now - with the evidence layer, replay, and configuration pinning already built.
Same technology, different reason to care
Evaluate the architecture through your own lens.
Engineering
- latency
- schema stability
- integration complexity
- versioning
- throughput
Trust & Safety
- taxonomy coverage
- false positives
- policy separation
- review workflows
Security
- evidence integrity
- audit trail
- configuration control
- replay
Compliance / Risk
- historical reproducibility
- change management
- documentation
- retention
- signatures
What about OpenAI Moderation?
OpenAI provides a dedicated Moderation API separate from its general-purpose GPT models. It returns predefined safety categories, category scores, and a flagged result. The current default model is omni-moderation-latest, and OpenAI currently provides the moderation endpoint free to API users.
That's a different comparison from asking a generative model to analyze text through a prompt.
Choose OpenAI Moderation
When its predefined safety taxonomy fits your moderation workflow.
Choose Sentiqra
When you need granular domain taxonomies, pinned analysis behavior, and reproducible semantic analysis architecture.
What about prompted LLM classification?
Generative models are valuable when the task requires flexible reasoning, changing schemas, explanation, or open-ended interpretation.
Sentiqra solves a different problem.
When replayability is a hard requirement, generation is often the wrong abstraction.
Use an LLM for reasoning. Use Sentiqra when the analysis itself needs to come from a versioned system.
What about Azure AI Content Safety?
Azure AI Content Safety provides four default harm categories and additional customization capabilities.
The useful comparison is therefore not "Azure has four categories; Sentiqra has domain-specific taxonomies." It is:
- How granular is the taxonomy?
- How is it versioned?
- Can historical analyses be replayed?
- How long are versions supported?
- What evidence does each result expose?
Those are the questions enterprise teams can actually evaluate.
AWS Comprehend's moderation endpoint fits the same category: broad, managed safety classification for AWS-native stacks. The same questions apply - taxonomy granularity, versioning, replay, and evidence exposure are where the architectures genuinely differ.
Named comparisons
vs OpenAI Moderation
Broad safety classification → domain-specific semantic taxonomy
vs GPT-4o classification
Prompted moderation vs versioned analyzer
vs Google Perspective
Retires Dec 31, 2026 - migration playbook
vs Self-hosted LLM
Build-vs-buy: GPUs, fine-tuning, drift monitoring
Azure AI Content Safety, AWS Comprehend, and Hive follow the same evaluation frame - dedicated pages are on the roadmap.
Moving from Perspective API?
Perspective API retires December 31, 2026.
Perspective remains available through the end of 2026, after which the service will shut down. Perspective says it will not provide direct migration support.
If Perspective is part of your moderation stack, evaluate the replacement against your own production examples before cutover.
Where human review fits
Human review isn't an alternative to Sentiqra in every workflow. It is often the decision layer around it.
Sentiqra supplies structured signals. Your team decides what happens next.
Compare on your own data.
Don't choose from a feature table alone. Run the same labeled dataset through Sentiqra and your current analysis stack. Compare:
ranking performance · false-positive rate · false-negative rate · taxonomy coverage · output stability · replayability · latency · cost per 1K · version migration effort · human-review rate · language performance · observability
Evaluate by domain and language, not only by aggregate score - a model that performs well overall can still have weak categories or weak languages.
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