Loqua vs ReLLM: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Loqua and ReLLM — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Loqua
FlowMind Technology Inc.
Desktop voice typing that turns speech into clean, structured text in any app, plus screenshot questions and voice editing.
Key features
- Global Shortcut Dictation: One shortcut invokes Loqua in any app and drops text straight at the cursor, with no window switching or waiting.
- Real-Time Cleanup: Filler words are removed, repetition is cut and phrasing is refined as you speak, so what lands on screen is ready to send.
- Automatic Structure: Loqua hears the structure in your speech and builds lists, headings and hierarchy on its own instead of making you dictate formatting.
- Mid-Sentence Translation: Speak one language and get natively phrased output in nearly 100 target languages, switching language mid-sentence.
- Capture to Ask: Select a table, chart or any screen region, speak a question about it, and get an answer, analysis, translation or summary in place.
- Ask & Edit: Highlight an existing draft, product description or note and revise it by voice rather than retyping.
- Per-App Context Intelligence: Tone and formatting adapt to the app you are writing in, available on the Pro plan.
- Privacy Defaults: Zero cloud data retention, on-device history storage, no training on user data, user-controlled dictation history, and GDPR compliance.
Best for
- Clearing a Message Backlog: Dictate Slack, email and comment replies at speaking speed instead of typing them one by one.
- Drafting Documents Hands-Free: Speak a structured draft into Notion, Google Docs or Word and get headings and lists built automatically.
- Cross-Language Correspondence: Reply to a partner or customer in their language by speaking your own.
- Understanding an Unfamiliar Screen: Capture a dense chart, table or error dialog and ask what it means without leaving the app.
- Revising Copy by Voice: Highlight a product description or draft paragraph and speak the edit you want applied.
- Coding Notes and Commit Messages: Dictate into a terminal, VS Code or IntelliJ where typing context-switches away from the code.
ReLLM
r2d4
Python library that enforces exact structured outputs from LLM completions by masking logits to match regex patterns.
Key features
- Token-level Pre-filtering: Tests each possible next-token completion against a partial regular expression and prevents tokens that would break the pattern from being generated by masking their logits.
- Partial-Regex Evaluation: Evaluates potential continuations incrementally using partial regular expressions so the generation process can be constrained at every step to enforce complex formats.
- Logit Masking Compatibility: Operates by masking logits rather than post-filtering, enabling deterministic enforcement of structure within standard decoding loops (e.g., greedy, top-k) when the model exposes logits.
- Structured Output Enforcement: Ensures outputs conform to strict formats such as JSON arrays or other schema-like regex patterns, reducing the need for downstream parsing and correction.
- Easy Installation and Examples: Distributed as a Python package (pip install rellm) with repository examples demonstrating common patterns and prompt setups for extracting structured data.
- Hosted API Reference: Notes availability of a hosted Regex Completion API (Thiggle Regex Completion API) for users seeking a managed service rather than self-hosted integration.
- Regex-driven pre-generation filtering that tests completions against a partial pattern per token
- Masks logits for disallowed tokens so the language model cannot generate non-matching outputs
- Designed to produce exact structured outputs (e.g., JSON, constrained formats) from LMs
- Pip-installable Python package (pip install rellm) with repository examples
- References a hosted API option (Thiggle Regex Completion API) for managed usage
Best for
- Reliable JSON Extraction: Force LLM responses to produce valid JSON arrays or objects (e.g., lists of items) so downstream systems can parse them without additional validation.
- Formatted Data Generation: Ensure generated outputs match strict formats like CSV rows, phone numbers, or fixed field templates required by legacy systems or ingestion pipelines.
- Schema-Constrained APIs: Integrate ReLLM into production inference to guarantee responses conform to expected regex-based schemas for webhooks, microservices, or data pipelines.
- Testing and Validation of LLM Behavior: Use regex constraints to probe and validate model behavior (e.g., detect memorized patterns or biased outputs) by restricting generations to specific patterns.
- Safe Prompting for Automation: Prevent hallucinated or malformed outputs in automation workflows (chatbots, document generation) by constraining possible tokens to only those that maintain the format.
- Hybrid Hosted or Self-Hosted Deployment: Use the open-source library in local inference stacks or adopt referenced hosted Regex Completion APIs for managed deployments where self-hosting isn't desired.
- Enforcing JSON or other structured output formats from LLMs for reliable parsing
- Constraining model generation to domain-specific patterns (IDs, phone numbers, codes)
- Validating or sanitizing LM outputs in pipelines that require strict formatting
- Research and testing of model behavior under constrained decoding
- Providing deterministic or format-guaranteed completions for integrations and APIs