Key Parameters for Model Selection
Every Large Language Model (LLM) decision hinges on these six interconnected metrics:- Cost: The primary driver is token usage (input tokens read + output tokens generated). Top-tier models are significantly more expensive per million tokens than fast, low-latency models.
- Latency (Speed): The end-to-end response time (Time to First Token + Time to Last Token). Low latency is non-negotiable for real-time user-facing applications (e.g., chat), while high-intelligence models often require longer “thinking” time for complex reasoning, increasing latency.
- Context Size (Memory): Defines how much data (measured in tokens) the model can analyze in a single prompt. This includes the entire conversation history, input documents, and tool schemas. Larger context windows (e.g., 1M+ tokens) are vital for document processing and long-running agentic tasks.
- Reasoning & Intelligence: The model’s ability to handle complex logic, multi-step planning, mathematical inference, and synthesizing ideas (Chain-of-Thought). This is the key differentiator for top-tier models.
- Web Search & Tool Integration: The model’s inherent ability to access live data (via built-in or external tools) or execute code within a sandboxed environment. This is crucial for agents that need up-to-the-minute information or programmatic execution.
- Extra Capabilities (Modality): Features beyond text, such as Multimodal understanding (image, audio, video input) and generation capabilities (Image/Code/Audio output).
🧩 Matching Model Type to Use Case
The model choice directly dictates the maximum complexity and speed your Agent can achieve.| Use Case Category | Characteristics | Recommended Models | Ideal Lyzr Agent Scenario |
|---|---|---|---|
| 1. Medium Intelligence, Fast Response, Low Cost | Prioritizes speed (low latency) and cost-efficiency. Acceptable for basic summarization, classification, and simple Q&A. | Gemini Flash Models, Mistral 7B, Claude Sonnet (for balancing speed/context) | Customer Support Bots, Workflow Automation Assistants, Fast Chat Assistants (e.g., GPT-5 Mini) |
| 2. High Intelligence, Slower Response, Costlier | Prioritizes accuracy and complex reasoning. Necessary for multi-step tasks, deep analysis, and high-stakes decision support. | GPT-5 Series, Claude Opus Series, Gemini Pro Series | Research Writing, Data Analytics Engines, Strategic Planning, Multi-Agent Orchestration |
| 3. Ultra-Fast, Real-Time Inference | Extreme focus on minimal latency, often served on specialized hardware. Cost is secondary to speed. | Groq-supported models (e.g., Llama 3 8B, Llama 3 70B), Haiku | Real-Time Voice Bots, Live Code Assistants, High-Frequency Trading Agents |
🧠 When to Use Readymade vs. Bring Your Own Model (BYOM)
Lyzr supports both commercial APIs and your own hosted models, each serving distinct business needs.| Decision Point | Use Readymade (Commercial) Models | Use Bring Your Own Model (BYOM) |
|---|---|---|
| Setup & Maintenance | Quick setup, stable API, maintenance handled by the provider. | Requires your own compute infrastructure (GPU/CPU hosting, scaling, monitoring). |
| Data Control | Data handled according to provider’s terms (often anonymized but leaves your environment). | Full data privacy and residency control (data never leaves your servers). |
| Customization | Limited to prompt engineering and fine-tuning via provider APIs. | Full fine-tuning flexibility on proprietary data. |
| Cost Structure | Pay-per-token (variable, scales with usage). | Predictable fixed cost (for infrastructure) with no per-token billing. |
💡 Lyzr allows secure BYOM integration, enabling you to connect private, fine-tuned, or open-source model endpoints directly into Agent Studio for data compliance and custom performance.
⚠️ The Open Source Model Trade-off
Open source models (like Llama 3, Mistral, Mixtral, Gemma) offer unparalleled control but come with infrastructure complexity.| Use Open Source Models When | Drawbacks to Consider |
|---|---|
| Data Privacy is paramount and internal compliance requires an on-premise or private cloud deployment. | Lower Baseline Intelligence compared to commercial flagships (e.g., GPT-5, Opus). |
| You must fine-tune the model on unique, proprietary data to achieve a specific domain capability. | Requires significant compute resources (GPU hosting, scaling, monitoring). |
| You want a fixed, predictable cost model based on hardware, not variable token usage. | Higher maintenance burden (updating versions, patching security). |
Detailed Provider Strengths & Use Case Mapping
| Provider | Core Strengths | Ideal Agent Use Cases |
|---|---|---|
| OpenAI (GPT) | Top-Tier Reasoning, complex logic, best-in-class coding & tool usage, massive ecosystem. | Complex High-Intelligence Agents, Coding Assistants, High-Value Document Analysis. |
| Anthropic (Claude) | Long Context Window, structured thinking, superior performance in safety and coherence, enterprise-grade. | Enterprise Chatbots, Legal/Policy Review, Multi-hour Agentic Workflows. |
| Google (Gemini) | Native Multimodality (text, image, audio, video), strong general-purpose reasoning. | Visual Reasoning (OCR), Multimodal Assistants (e.g., analyzing graphs in a document). |
| Mistral / Mixtral | Lightweight, Extremely Fast, high throughput, excellent balance of quality for its size. | Low-Latency APIs, Budget-friendly tasks, Simple Classification/Extraction at scale. |
| Groq (Hardware Acceleration) | Ultra-low Latency Inference (sub-100ms response), specializing in speed. | Real-Time Interactive Agents, Voice Chatbots, Time-Sensitive Financial Monitoring. |
| Meta (Llama 3) | Fully Open Source, excellent performance for BYOM, strong foundation for fine-tuning. | Private/On-Premise Deployments, Custom Fine-Tuned Domain Experts. |
🎯 Use Case vs. Model Recommendation Matrix
| Use Case | Recommended Models | Key Model Rationale |
|---|---|---|
| Image Recognition (OCR) | Gemini 3 Pro | Strong native multimodal reasoning and visual understanding. |
| Image Generation | Gemini Nano Banana Series | Specialized models built for high-fidelity, controllable image creation. |
| High Reasoning / Strategy | Claude Opus Series, GPT 5 Series, Gemini 3 Pro | Highest benchmarks in complex logic, planning, and long-horizon tasks. |
| Multi-Agent Orchestration | Claude Opus Series, GPT 5 Series, Gemini 3 Pro | Requires robust reasoning to break down goals, manage tool use, and synthesize multiple worker outputs. |
| Fastest to Answer / Real-Time | Groq-supported Models, Haiku, Gemini 2.5 Flash | Optimized for throughput and minimal latency using specialized infrastructure or model architecture. |
| General Chat Assistants | GPT 5 Mini, Gemini 2.5 Flash, Claude Sonnet | Optimal balance of cost, speed, and sufficient reasoning for conversational tasks. |
| High Context Window Size | Gemini 3 Pro, Claude 4.5 Sonnet, Claude Opus | Models offering 200K, 1M, or larger token contexts for deep document analysis. |
🧭 Pro Tip: Iterative Model Selection
The best practice is always an iterative approach:- Start with the Balanced Tier: Begin with reliable, reasonably priced models like GPT-5 Mini or Claude Sonnet.
- Test & Measure: Deploy your Agent and carefully track response quality, latency, and cost for real user queries.
- Iterate:
- If Reasoning/Accuracy is lacking, upgrade to a High Intelligence model (Opus/GPT-5/Gemini Pro).
- If Latency/Cost is too high, downgrade to a Fast/Low Cost model (Flash/Haiku/Groq).