Phi 3 (3.8B, 7B, 14B) vs Relativity
Side-by-side comparison to help you choose.
| Feature | Phi 3 (3.8B, 7B, 14B) | Relativity |
|---|---|---|
| Type | Model | Product |
| UnfragileRank | 26/100 | 35/100 |
| Adoption | 0 | 0 |
| Quality | 0 | 1 |
| Ecosystem | 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Paid |
| Capabilities | 12 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Generates coherent, instruction-aligned text responses using a decoder-only transformer architecture trained via supervised fine-tuning (SFT) and Direct Preference Optimization (DPO). Processes user messages in standard chat format (role/content structure) and produces contextually relevant outputs within a 4,096-token context window, optimized for latency-bound scenarios where model size and inference speed are critical constraints.
Unique: Phi-3 Mini achieves 'state-of-the-art performance among models with less than 13 billion parameters' through synthetic data augmentation combined with DPO post-training, enabling strong reasoning (math, logic, code) in a 3.8B parameter footprint where competitors typically require 7B+ parameters for equivalent capability
vs alternatives: Smaller and faster than Llama 2 7B or Mistral 7B while maintaining comparable instruction-following quality, making it ideal for latency-sensitive deployments where model size directly impacts inference speed and memory overhead
Extends the standard 4K context window to 128K tokens, enabling processing of long documents, extended conversation histories, and complex multi-document reasoning tasks. Accessed via specific model variant (phi3:medium-128k) requiring Ollama 0.1.39+, allowing developers to trade off some inference speed for dramatically increased context capacity without changing model weights or architecture.
Unique: Phi-3 Medium variant supports 128K context through architectural modifications (likely rotary position embeddings or similar) without requiring model retraining, enabling a single model to serve both latency-sensitive (4K) and context-heavy (128K) workloads via variant selection
vs alternatives: Offers 32x larger context window than default Phi-3 while maintaining 14B parameter efficiency, compared to Llama 2 70B or GPT-4 which require substantially more compute for equivalent context capacity
Phi-3 models undergo Direct Preference Optimization (DPO) post-training to improve instruction adherence and incorporate safety measures, reducing harmful outputs and improving alignment with user intent. DPO uses preference pairs (preferred vs. dispreferred responses) to fine-tune the model without requiring explicit reward models, enabling instruction-following behavior that better matches user expectations while maintaining model efficiency.
Unique: Phi-3 uses Direct Preference Optimization (DPO) instead of traditional RLHF, enabling safety alignment without separate reward models, reducing training complexity while maintaining instruction-following quality in a 3.8B-14B parameter footprint
vs alternatives: More efficient safety alignment than RLHF-based approaches (used by larger models), though less transparent than models with published safety documentation or red-teaming results
Phi-3 training incorporates synthetic data generation to create high-quality reasoning examples (math, logic, code), enabling the small 3.8B model to achieve reasoning performance comparable to 7B-13B models trained on natural data alone. Synthetic data augmentation compensates for parameter count disadvantage by providing dense, reasoning-focused training examples rather than relying on scale.
Unique: Phi-3 Mini achieves 7B-equivalent reasoning performance through synthetic data augmentation rather than parameter scaling, enabling reasoning capability in a 3.8B model that would typically require 7B+ parameters, making reasoning accessible in latency-sensitive deployments
vs alternatives: More efficient reasoning per parameter than models trained purely on natural data, though less capable than 70B+ models on complex multi-step reasoning or novel problem types
Executes Phi-3 models entirely on local hardware (macOS, Windows, Linux, Docker) without sending data to external servers, using Ollama's runtime which handles model downloading, quantization format management, and GPU/CPU inference orchestration. Exposes both CLI interface (ollama run phi3) and HTTP REST API (localhost:11434) for programmatic access, enabling zero-latency, privacy-preserving inference with full control over model execution.
Unique: Ollama abstracts away quantization, GPU memory management, and model format complexity, allowing developers to run Phi-3 with a single command (ollama run phi3) while automatically handling hardware detection, format selection, and inference optimization without explicit configuration
vs alternatives: Simpler local deployment than vLLM or llama.cpp for non-expert users, with built-in model management and REST API, though less flexible than lower-level frameworks for advanced optimization or custom quantization schemes
Deploys Phi-3 models to Ollama's managed cloud infrastructure (separate from local execution), enabling remote inference without maintaining local hardware while retaining API compatibility with local Ollama instances. Subscription tiers (Pro: $20/mo, Max: $100/mo) determine concurrent model capacity (1, 3, or 10 concurrent models), with identical REST API and SDK interfaces to local execution, allowing seamless switching between local and cloud deployment.
Unique: Ollama cloud maintains identical REST API and SDK interfaces to local execution, enabling developers to deploy the same code locally or remotely by changing only the endpoint URL, eliminating vendor-specific API refactoring when scaling from prototype to production
vs alternatives: Simpler than AWS SageMaker or Azure ML for Phi-3 deployment due to API consistency with local Ollama, though less flexible than cloud-native platforms for custom optimization, monitoring, or multi-model orchestration
Phi-3 models are instruction-tuned and benchmarked on code generation, mathematical reasoning, and logical problem-solving tasks, leveraging synthetic training data and DPO post-training to improve reasoning capability. The 3.8B Mini variant achieves competitive performance on code and math benchmarks despite its small size, making it suitable for code completion, algorithm explanation, and structured problem-solving without requiring 7B+ parameter models.
Unique: Phi-3 Mini (3.8B) achieves code and math reasoning performance comparable to 7B-13B models through synthetic data augmentation (high-quality reasoning examples) and DPO fine-tuning, enabling code-generation capabilities in a model small enough for edge deployment or local-only execution
vs alternatives: Smaller and faster than CodeLlama 7B or Mistral 7B for code tasks while maintaining competitive accuracy on benchmarks, making it suitable for latency-sensitive code-completion features where inference speed is critical
Supports multi-turn conversations using standard chat message format (role: user/assistant, content: text), enabling stateless conversation management where each API call includes full conversation history. Ollama REST API and SDKs handle message serialization and streaming responses, allowing developers to build chatbot interfaces without managing conversation state or session persistence.
Unique: Ollama's chat API uses standard OpenAI-compatible message format, enabling drop-in compatibility with existing chatbot frameworks and client libraries designed for OpenAI API, while maintaining identical interface for local and cloud deployment
vs alternatives: Simpler than building custom conversation state management with vector databases, though less sophisticated than systems with automatic context compression or hierarchical conversation memory
+4 more capabilities
Automatically categorizes and codes documents based on learned patterns from human-reviewed samples, using machine learning to predict relevance, privilege, and responsiveness. Reduces manual review burden by identifying documents that match specified criteria without human intervention.
Ingests and processes massive volumes of documents in native formats while preserving metadata integrity and creating searchable indices. Handles format conversion, deduplication, and metadata extraction without data loss.
Provides tools for organizing and retrieving documents during depositions and trial, including document linking, timeline creation, and quick-search capabilities. Enables attorneys to rapidly locate supporting documents during proceedings.
Manages documents subject to regulatory requirements and compliance obligations, including retention policies, audit trails, and regulatory reporting. Tracks document lifecycle and ensures compliance with legal holds and preservation requirements.
Manages multi-reviewer document review workflows with task assignment, progress tracking, and quality control mechanisms. Supports parallel review by multiple team members with conflict resolution and consistency checking.
Enables rapid searching across massive document collections using full-text indexing, Boolean operators, and field-specific queries. Supports complex search syntax for precise document retrieval and filtering.
Relativity scores higher at 35/100 vs Phi 3 (3.8B, 7B, 14B) at 26/100. Phi 3 (3.8B, 7B, 14B) leads on ecosystem, while Relativity is stronger on quality. However, Phi 3 (3.8B, 7B, 14B) offers a free tier which may be better for getting started.
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Identifies and flags privileged communications (attorney-client, work product) and confidential information through pattern recognition and metadata analysis. Maintains comprehensive audit trails of all access to sensitive materials.
Implements role-based access controls with fine-grained permissions at document, workspace, and field levels. Allows administrators to restrict access based on user roles, case assignments, and security clearances.
+5 more capabilities