Overview: Model Families, Not Versions
AI models evolve FAST. By the time you read this, there might be GPT-6, Claude 5, Gemini 3, DeepSeek v4, etc.This guide focuses on model families and providers rather than specific version numbers. When we say “GPT-5,” understand that might be GPT-5.1, GPT-5 Pro, or whatever OpenAI has released. Same for Claude Sonnet (4.5, 4.6, 5…), Gemini Pro (2.5, 2.6…), DeepSeek (v3, v3.2…), etc.The capabilities and characteristics stay relatively stable within families, though specific benchmarks change constantly. Family characteristics (OpenAI = reliable, Anthropic = nuanced, Google = context kings) are subjective generalizations from limited testing. Try them all yourself! Model personalities vary by task, and your experience may differ.
- Reasoning depth: Can it handle complex theoretical frameworks?
- Context capacity: How many papers can it process at once?
- Writing quality: Does it produce academic-grade prose?
- Cost efficiency: What’s the price-performance ratio?
Model Families by Provider
Free Access Options
🆓 Google AI Studio: Your Free Backup Plan
Why This Matters: Google AI Studio provides free daily access to Gemini models, ensuring you can continue your research even if you exceed API credits.| Model | Daily Limit | Context Window | Best For |
|---|---|---|---|
| Gemini Flash | 1,500 requests/day | 1M tokens | High-volume literature processing |
| Gemini Pro | 100 requests/day | 1M tokens | Complex theoretical analysis |
| Gemini Embeddings | Generous limits | N/A | Document similarity, semantic search |
Getting Started with Google AI Studio
- Create Account: Visit aistudio.google.com
- Generate API Key: Go to aistudio.google.com/app/apikey
- Add to Cherry Studio: Settings → API Keys → Add Provider → Google Gemini
- Test Connection: Verify your free daily limits are active
When to Use Free Options
- Literature exploration: Use Gemini Flash for processing large paper collections
- Backup strategy: When API credits are running low
- Experimentation: Try different approaches without cost concerns
- Learning: Understand model differences before using premium credits
Important Notes
- Limits reset daily at midnight Pacific Time
- Free access available worldwide (some regions may vary)
- Same high-quality models as paid versions
- Perfect for systematic review tasks requiring large context windows
Understanding AI Configuration Settings
- 🌡️ Temperature
- 🧠 Reasoning Effort
- 🎯 Task-Based Settings
Temperature Settings: Embrace the Heat! 🔥
Default recommendation: HIGH temperature (1.0-1.5)Most research tasks benefit from creative, exploratory thinking. Don’t default to low temps!HIGH Temperature (1.0-1.5): ⭐ We Usually Start Here- Use for: Most research tasks! Theory synthesis, exploration, analysis, writing
- Why: AI produces more interesting insights, varied perspectives, creative connections
- We find these work well: GPT-5 (1.0-1.2), DeepSeek (1.0-1.3), Qwen (1.0-1.4)
- Example: “Show me unexpected connections between these frameworks”
- Use for: Bringing out model personality, exploratory synthesis
- Why: Some models get REALLY creative at medium temps!
- Sweet spots:
- Kimi K2 at 0.6-0.7: Developer-recommended, unlocks creative side
- Gemini 2.5 Pro at 0.7-0.8: Quirky insights, interesting angles
- GLM at 0.7-0.9: Creative multilingual connections
- Example: “Give me fresh perspectives I haven’t considered”
- Use for: Deterministic tasks ONLY (citations, final formatting, systematic coding)
- Why: Kills creativity, repetitive outputs, boring responses
- When: You need the SAME answer every time
- Example: “Extract author names from this citation - nothing else”
Our experience: The old advice of “start at 0.1 for precision” often kills the AI’s ability to surprise you with insights. Research is creative work - we usually let the models explore! Your needs might differ, but experiment with higher temperatures before defaulting to low.
For a deep dive into advanced reasoning, see our guide on Mastering Sequential Thinking with MCP.
Strategic Model Usage for Research
- 🔍 Discovery & Exploration
- 📖 Deep Analysis
- ✍️ Writing & Synthesis
- 🔧 Specialized Applications
🔍 Discovery & Exploration
Sample Widely - Build Understanding- All models: Try everything to understand what works for your research style
- Focus: Finding the right tool for each type of task
- Approach: Small tasks, broad exploration, document preferences
- Initial literature scanning
- Research question refinement
- Methodology exploration
- Theoretical framework discovery
When Cost Considerations Matter
High Token Consumption Scenarios
These are where strategic model selection saves significant money:-
Automated Literature Processing
- Processing 100+ papers automatically
- Multiple extraction passes
- Strategy: Develop workflow with DeepSeek, deploy with premium if needed
-
Iterative Development
- Refining complex prompts through many cycles
- Testing workflow logic extensively
- Strategy: Iterate with efficient models, finalize with best
-
Large-Scale Analysis
- Systematic coding of hundreds of documents
- Cross-referencing massive literature sets
- Strategy: Prototype small-scale, then choose model based on quality needs
Practical Guidelines
Context Window Strategy
- Under 50 pages: Any model works fine
- 50-200 pages: Use 200K+ models (Claude, GPT-5)
- 200+ pages: Use 1M+ models (Gemini Pro, Claude Sonnet 4 extended)
Quality Assurance (Always Important)
- Verify citations: All models can hallucinate references
- Cross-check critical analysis: Use multiple models for important insights
- Use reasoning modes: For complex theoretical questions
- Document model choices: Track what works best for different tasks
Getting Started
- Phase 1: Discovery
- Phase 2: Task Matching
- Experimentation
- Build Your Strategy
Phase 1: Capability Discovery
Sample Everything (1-2 days of exploration)- Access via OpenRouter: All models available through single API key
- Choose one complex research task (e.g., theory synthesis from 3 papers)
- Run the same prompt across ALL models:
- GPT-5, Gemini Pro, Claude Opus 4.1, Claude Sonnet 4
- DeepSeek V3.1, Kimi K2, Qwen3, GLM-4.5
- Note differences: Style, depth, accuracy, approach
- Test temperature variations: Try each model at 0.1, 0.6-0.8, 1.0
- Experiment with reasoning modes: Built-in vs. MCP Sequential Thinking
Next Steps:
- Set up your workspace: Cherry Studio Setup Guide
- Build your research pipeline: Zotero Setup Guide and Research Rabbit Setup Guide
- Learn common pitfalls: Failure Museum
- Get started: Quick Start Checklist