WizardLM 2 (7B, 8x22B) vs HubSpot
Side-by-side comparison to help you choose.
| Feature | WizardLM 2 (7B, 8x22B) | HubSpot |
|---|---|---|
| Type | Model | Product |
| UnfragileRank | 23/100 | 33/100 |
| Adoption | 0 | 0 |
| Quality | 0 | 1 |
| Ecosystem | 0 |
| 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 14 decomposed |
| Times Matched | 0 | 0 |
Processes multi-turn chat interactions using a standard role/content message format (user/assistant/system roles) with transformer-based attention mechanisms optimized for instruction-following. Maintains conversation context across turns through full context window utilization (32K tokens for 7B, 64K for 8x22B variants), enabling coherent multi-step dialogues without explicit memory management. Implements instruction-tuning via supervised fine-tuning on complex reasoning tasks, allowing the model to follow nuanced user directives and adapt responses based on conversational context.
Unique: Instruction-tuning optimized for complex reasoning tasks via Microsoft's supervised fine-tuning approach, with 64K context window in 8x22B variant enabling longer conversation histories than typical 7B models; distributed as GGUF quantized format for local inference without cloud dependency
vs alternatives: Offers instruction-following comparable to larger proprietary models (claimed 10x larger model equivalence for 7B) while remaining fully open-source and deployable locally, unlike GPT-4 or Claude which require cloud APIs
Executes chain-of-thought reasoning patterns through transformer attention mechanisms trained on complex reasoning tasks, enabling step-by-step problem solving without explicit prompt engineering. The model decomposes multi-step problems by generating intermediate reasoning tokens that guide subsequent token generation, effectively implementing implicit planning through learned reasoning patterns. Supports both explicit reasoning traces (where the model outputs its reasoning steps) and implicit reasoning (where intermediate computations influence final answers), leveraging the instruction-tuned architecture to recognize when problems require decomposition.
Unique: Instruction-tuned specifically for complex reasoning tasks via supervised fine-tuning on reasoning-heavy datasets, enabling implicit chain-of-thought without explicit prompt engineering; 8x22B MoE variant routes complex reasoning through specialized expert pathways for improved reasoning quality
vs alternatives: Provides reasoning capabilities comparable to GPT-3.5-turbo or Claude-2 while remaining fully open-source and locally deployable, avoiding cloud API costs and latency for reasoning-intensive workloads
Distributes model weights as open-source artifacts through Ollama's package manager, enabling community inspection, fine-tuning, and redistribution. The model is available under an unspecified open-source license (license terms not documented), with 1.1M downloads on Ollama indicating community adoption. Open-source distribution enables researchers and developers to audit model behavior, implement custom quantizations, and fine-tune for domain-specific tasks without proprietary restrictions.
Unique: Open-source distribution via Ollama enables community transparency and fine-tuning without proprietary restrictions; 1.1M downloads indicate significant community adoption and validation
vs alternatives: Fully open-source vs. proprietary models (GPT-4, Claude) which cannot be audited or fine-tuned; enables community-driven improvements and domain-specific customization
Supports structured function calling through schema-based tool definitions that the model can invoke to extend its capabilities beyond text generation. The model receives a schema describing available tools (functions, parameters, return types) and learns to recognize when a tool invocation is appropriate, generating structured function calls that applications can execute and feed results back into the conversation. This enables agentic workflows where the model acts as a reasoning engine that orchestrates external tools (APIs, databases, code execution) to solve problems iteratively.
Unique: Tool calling implemented as cloud-only feature on Ollama Pro/Max tiers, leveraging instruction-tuned model to recognize tool invocation patterns and generate structured function calls; separates local inference (no tool calling) from cloud inference (with tool calling) to manage compute costs
vs alternatives: Enables agentic workflows on open-source models without proprietary APIs, though tool calling is cloud-only; local inference remains available for non-agentic use cases, providing cost flexibility vs. always-cloud solutions like OpenAI or Anthropic
Distributes pre-quantized GGUF-format models through Ollama's package manager, enabling single-command local inference without manual quantization or compilation. Models are downloaded as compressed GGUF artifacts (4.1GB for 7B, 80GB for 8x22B) and loaded into memory for inference via Ollama's C++ runtime, which handles GPU acceleration (CUDA/Metal) and CPU fallback automatically. This approach eliminates cloud API dependencies and latency, enabling private inference with full model control and no data transmission to external servers.
Unique: Pre-quantized GGUF distribution via Ollama eliminates manual quantization complexity, with automatic GPU acceleration detection and CPU fallback; single-command deployment (`ollama run wizardlm2`) vs. manual model downloading, quantization, and runtime setup required by alternatives
vs alternatives: Dramatically simpler local deployment than vLLM, llama.cpp, or Hugging Face Transformers (which require manual quantization and CUDA setup); trades some inference speed for ease of use and automatic hardware optimization
Offers three model size variants (7B, 8x22B MoE, 70B) enabling developers to select optimal performance-cost-VRAM tradeoffs for their deployment constraints. The 7B variant provides lightweight inference suitable for resource-constrained environments (laptops, edge devices), while the 8x22B Mixture-of-Experts variant uses sparse activation to achieve 176B effective parameters with lower VRAM than dense 70B models, and the 70B variant provides maximum reasoning capability for compute-rich environments. Developers can benchmark locally and switch variants by changing the model name in API calls (`ollama run wizardlm2:7b` vs. `ollama run wizardlm2:8x22b`).
Unique: Mixture-of-Experts (8x22B) variant uses sparse activation to achieve 176B effective parameters with lower VRAM than dense models, enabling high-capacity reasoning on mid-range hardware; three-tier variant strategy (7B/8x22B/70B) provides explicit performance-cost-VRAM tradeoff options
vs alternatives: MoE architecture provides better VRAM efficiency than dense models of equivalent capacity (e.g., 8x22B vs. 70B dense), while maintaining compatibility with single API; more explicit variant selection than auto-scaling solutions like vLLM
Generates text incrementally via streaming API endpoints, returning tokens as they are generated rather than buffering the complete response. Ollama's streaming implementation prioritizes low time-to-first-token (TTFT) through optimized KV-cache management and batch processing, enabling responsive user interfaces that display text as it appears. Streaming is supported across all deployment modes (local REST API, Python SDK, JavaScript SDK, cloud API) via standard HTTP chunked transfer encoding or SDK-level streaming callbacks.
Unique: Streaming implemented across all deployment modes (local, cloud, SDKs) with consistent API surface; Ollama's C++ runtime optimizes KV-cache for streaming to minimize TTFT, though specific optimizations not documented
vs alternatives: Streaming available on local inference (unlike some cloud APIs with streaming-only premium tiers); consistent streaming API across Python/JavaScript SDKs reduces implementation complexity vs. managing different streaming patterns per SDK
Exposes inference capabilities through a standard REST API (POST /api/chat) and language-specific SDKs (Python, JavaScript) that abstract HTTP details and provide idiomatic interfaces. The REST API accepts JSON-formatted chat messages and returns responses in JSON, supporting both buffered and streaming modes via query parameters. SDKs provide type-safe interfaces (Python: `ollama.chat()`, JavaScript: `ollama.chat()`) that handle serialization, streaming callbacks, and error handling, enabling integration into existing Python/Node.js applications without manual HTTP management.
Unique: Unified API surface across local and cloud deployments (same REST endpoint and SDK calls work for both), with automatic endpoint routing based on configuration; SDKs provide streaming callbacks and error handling abstractions vs. raw HTTP clients
vs alternatives: Simpler integration than managing raw HTTP clients or multiple SDK versions; local REST API eliminates cloud API dependency for development/testing, while cloud API provides scalability without infrastructure management
+3 more capabilities
Centralized storage and organization of customer contacts across marketing, sales, and support teams with synchronized data accessible to all departments. Eliminates data silos by maintaining a single source of truth for customer information.
Generates and recommends optimized email subject lines using AI analysis of historical performance data and engagement patterns. Provides multiple subject line variations to improve open rates.
Embeds scheduling links in emails and pages allowing prospects to book meetings directly. Syncs with calendar systems and automatically creates meeting records linked to contacts.
Connects HubSpot with hundreds of external tools and services through native integrations and workflow automation. Reduces dependency on third-party automation platforms for common use cases.
Creates customizable dashboards and reports showing metrics across marketing, sales, and support. Provides visibility into KPIs, campaign performance, and team productivity.
Allows creation of custom fields and properties to track company-specific information about contacts and deals. Enables flexible data modeling for unique business needs.
HubSpot scores higher at 33/100 vs WizardLM 2 (7B, 8x22B) at 23/100.
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Creates automated marketing sequences and workflows triggered by customer actions, behaviors, or time-based events without requiring external tools. Includes email sequences, lead nurturing, and multi-step campaigns.
+6 more capabilities