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How to Choose the Right LLM for Your Assistant

Learn how to choose the right LLM for your tasks

Updated over 3 weeks ago

Selecting the right Large Language Model (LLM) for your elvex assistant is crucial for delivering the best experience to you and your coworkers. This guide will help you evaluate and choose models based on your specific use cases and requirements.

Before You Start

You should have:

  • Access to elvex with assistant creation permissions

  • A clear understanding of what tasks your assistant will perform

  • Knowledge of your organization's budget and performance requirements

Identify Your Use Case

Start by categorizing what your assistant will primarily do:

General Conversation & Support

  • Best for: Customer service, general Q&A, internal help desk

  • Recommended models: Gemini 2.5 Pro, Claude 4 Sonnet, GPT-4.1, Claude 3.7 Sonnet

  • Key considerations: Natural conversation flow, instruction following

Code Generation & Technical Tasks

  • Best for: Developer tools, code review, technical documentation

  • Recommended models: Claude 4 Sonnet, Claude 3.7 Sonnet, GPT-4.1, DeepSeek V3

  • Key considerations: Code quality, debugging ability, multiple programming languages

Data Analysis & Reasoning

  • Best for: Business intelligence, report generation, complex problem solving

  • Recommended models: o3, Gemini 2.5 Pro, Claude 4 Sonnet, GPT-4.1

  • Key considerations: Logical consistency, step-by-step analysis

Quick Reference & Simple Tasks

  • Best for: FAQ responses, simple lookups, basic automation

  • Recommended models: Claude 3 Haiku, GPT-4.1 mini, Gemini 2.5 Flash

  • Key considerations: Speed and cost efficiency

Creative Content

  • Best for: Marketing copy, content creation, brainstorming

  • Recommended models: Claude 4 Sonnet, Gemini 2.5 Pro, GPT-4.1

  • Key considerations: Creativity, style consistency, brand voice

Current Model Landscape (Updated June 2025)

Top-Tier Models (Premium Performance):

  • Gemini 2.5 Pro Preview: Leading overall performance, excellent reasoning

  • o3: Top reasoning capabilities, complex problem solving

  • Claude 4 Sonnet: Excellent for coding, strong general performance

  • GPT-4.1: Large context window (1M tokens), solid all-around performance

High-Performance Models (Great Balance):

  • Claude 3.7 Sonnet: Excellent coding, good value proposition

  • Claude 4 Opus: Strong reasoning, premium Anthropic model

  • Gemini 2.5 Flash: Fast performance, good for high-volume use

  • o4-mini (high): Strong reasoning at lower cost

Cost-Effective Models (Budget-Friendly):

  • Claude 3 Haiku: Fast and affordable for simple tasks

  • GPT-4.1 mini: Good performance with smaller context needs

  • DeepSeek V3: Open-source option with strong capabilities

  • Gemini 2.5 Flash: Good balance of speed and cost

Troubleshooting Common Issues

Responses Are Too Slow

  • Switch to a faster model (Claude 3 Haiku, Gemini 2.5 Flash)

  • Optimize your assistant instructions to be more concise

  • Consider breaking complex tasks into simpler steps

Responses Are Inaccurate

  • Upgrade to a higher-quality model (Gemini 2.5 Pro, Claude 4 Sonnet, o3)

  • Improve your assistant instructions with more specific examples

  • Add relevant datasources to provide better context

Costs Are Too High

  • Switch to a more cost-effective model (Claude 3.7 Sonnet, GPT-4.1 mini)

  • Optimize prompts to reduce token usage

  • Set usage limits in elvex settings

  • Review if all features are necessary

Model Availability Issues

  • Have backup model options configured

  • Monitor model provider status pages

  • Consider using multiple providers for redundancy

Additional Resources

  • LMSYS Chatbot Arena: lmarena.ai - Real-world model comparisons with 3M+ votes

  • Artificial Analysis: artificialanalysis.ai - Comprehensive cost, speed, and quality benchmarks

  • elvex Model Documentation: Check the latest available models in your elvex settings

Key Trends:

  • Longer context windows: Many models now support 128k+ tokens

  • Improved reasoning: New models excel at multi-step problem solving

  • Better coding capabilities: Significant improvements in code generation and debugging

  • Cost optimization: More efficient models offering better value

Remember: The "best" model depends entirely on your specific needs. What works for one team may not be optimal for another. Start with testing and iterate based on real-world performance. The model landscape evolves rapidly, so plan to reassess your choices quarterly.

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