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
🥇 Premium Providers
🚀 Cost-Effective
🌟 Specialized
OpenAI GPT Family
- Current: GPT-5 series (Pro, standard)
- Context: 400K-1M tokens (expanding)
- Strength: Reliable, consistent, excellent writing
- Best for: General research tasks, final writing, systematic coding
Anthropic Claude Family
- Current: Opus 4.x, Sonnet 4.x series
- Context: 200K-1M tokens
- Strength: Deep reasoning, nuanced understanding, academic style
- Best for: Theory development, qualitative analysis, complex arguments
Google Gemini Family
- Current: 2.5 Pro, 2.5 Flash, 2.5 Flash-Lite
- Context: 1M-2M tokens (largest available!)
- Strength: Massive context, free tier, fast, cost-effective Flash-Lite
- Best for: Large literature sets, exploratory analysis, volume processing
DeepSeek Family (DeepSeek AI)
- Current: V3.x series (Chat, Reasoner)
- Context: 128K-256K tokens
- Strength: Budget-friendly, good reasoning, frequent updates
- Best for: Exploration, high-volume tasks, iterative development
Kimi Family (Moonshot AI)
- Current: K2 series
- Context: 128K-200K tokens
- Strength: MCP integration, workflow automation
- Best for: Automated pipelines, systematic processing
- Quirk: Medium temp (0.6-0.8) brings out creative side!
GLM Family (Zhipu AI)
- Current: 4.x series
- Context: 128K tokens
- Strength: Multilingual (Chinese-English), international research
- Best for: Cross-language work, Asian market studies
- Quirk: Loves medium-high temp for creative exploration!
Qwen Family (Alibaba)
- Current: Qwen3 series (Thinking models)
- Context: 256K tokens
- Strength: Open source, reasoning capabilities
- Best for: Complex logical analysis, customizable workflows
Meta Llama Family (Meta)
- Current: Llama 3.x series
- Context: Varies by deployment
- Strength: Fully open source, self-hostable
- Best for: Privacy-sensitive research, customization needs
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”
MEDIUM Temperature (0.6-0.8): 🎨 For Creative Quirks
- 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”
LOW Temperature (0.1-0.3): ⚠️ Only When Needed
- 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.
Reasoning Effort Settings
Built-in Reasoning Models (GPT-5, Gemini 2.5 Pro, Claude Opus 4.1, Claude Sonnet 4):
- Low/Standard: Quick responses for straightforward tasks
- Medium: Balanced thinking for complex analysis
- High/Extended: Deep reasoning for difficult theoretical problems
MCP Sequential Thinking (Kimi K2, GLM-4.5, non-reasoning models):
- Access through Cherry Studio’s MCP Sequential Thinking tool
- Provides step-by-step reasoning for any model
- Particularly effective for logical analysis and complex problem solving
Recommended Settings by Task
| Task Type | Temperature | Reasoning | Why |
|---|
| Theory Synthesis | 1.0-1.3 | High/Extended | Let AI make creative leaps! |
| Framework Building | 1.0-1.5 | High/Extended | Maximum creativity + deep thinking |
| Exploratory Analysis | 0.7-0.9 | Medium | Creative quirks (try with Kimi, Gemini!) |
| Literature Synthesis | 0.8-1.2 | Medium/High | Balance creativity with grounding |
| Draft Writing | 0.8-1.0 | Medium | Varied prose, interesting angles |
| Final Writing | 0.5-0.7 | Medium | Some consistency but not boring |
| Citation Extraction | 0.1-0.2 | Standard | Only time you need deterministic! |
| Systematic Coding | 0.2-0.4 | Standard | Consistency in categories |
Notice the pattern? We find most research benefits from HIGH temps! We only drop low for mechanical tasks like citation extraction or systematic coding where you need identical outputs. Your optimal settings might differ—experiment to find what works for your research style.
For a deep dive into advanced reasoning, see our guide on Mastering Sequential Thinking with MCP.
Strategic Model Usage for Research
🔍 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
Common Discovery Tasks:
- Initial literature scanning
- Research question refinement
- Methodology exploration
- Theoretical framework discovery
📖 Deep Analysis Phase
Choose Based on Task Requirements| Analysis Type | Recommended Models | Why |
|---|
| Large Literature Sets | Gemini Pro | 1M+ token context |
| Theoretical Depth | Claude Opus 4.1 | Understands paradigmatic nuances |
| Consistent Coding | GPT-5 | Reliable, predictable analysis |
| Complex Reasoning | DeepSeek Reasoner, Qwen3 | Step-by-step thinking |
| Tool Integration | Kimi K2 | Seamless workflow automation |
✍️ Writing & Synthesis
Quality Matters Most
- Primary choice: Best model for the specific writing task
- GPT-5: Consistent voice, reliable editing
- Claude Sonnet 4: Academic style, nuanced arguments
- Claude Opus 4.1: Complex theoretical writing
- Iteration strategy: Use cheaper models only for early drafts if doing many iterations
🔧 Specialized Applications
- Multilingual Research: GLM-4.5 (excellent Chinese-English)
- Open-Source Needs: Qwen3, GLM-4.5 (fully customizable)
- Automated Workflows: Kimi K2 (MCP integration)
- Budget-Conscious Scale: DeepSeek models (when token volume is high)
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
Phase 2: Task Matching
Find Your Research Tools (ongoing process)
- Match models to specific tasks based on what you discovered
- Build your personal toolkit: 2-3 go-to models for different needs
- Develop model-specific prompting styles: Each model has optimal approaches
- Test with your actual research materials: Move beyond generic examples
Temperature Experimentation Protocol
Discover Each Model’s Personality
- Choose one complex task (theory synthesis, framework comparison, etc.)
- Start HIGH and work down:
- GPT-5: Try 1.3 → 1.0 → 0.7 (note where it gets interesting!)
- Gemini 2.5 Pro: Try 1.2 → 0.8 → 0.6 (quirky at 0.7-0.8!)
- Kimi K2: Try 0.8 → 0.6 → 0.4 (sweet spot often 0.6-0.7)
- Claude Sonnet: Try 1.2 → 1.0 → 0.8 (maintains quality across range)
- Document the differences: Where does each model shine?
- Find YOUR preferences: Some researchers love wild creativity (1.5), others prefer balanced (0.8)
From my experience: Kimi K2 and Gemini 2.5 Pro at medium temps (0.6-0.8) often produce interesting, quirky insights. But this is subjective! Try different temps yourself and see what works for YOUR research style. Search online for what others are saying too - temperature preferences vary widely!
Reasoning Mode Comparison
When to Think Deeper
- Test same complex task with different reasoning levels:
- Built-in reasoning (GPT-5, Gemini, Claude): Low → Medium → High
- MCP Sequential Thinking (any model): Standard vs. detailed prompts
- Compare: Does deeper reasoning actually help or just add time?
- Learn: When is standard thinking sufficient vs. when you need extended modes?
Building Your Personal Research Strategy
What you should develop:
- Task-model matching: Which models excel at your specific research needs
- Temperature preferences: Settings that match your thinking style
- Reasoning workflows: When to use which type of AI thinking
- Cost-quality trade-offs: Where premium models matter most
- Fallback strategies: Backup options when preferred models are unavailable
Next Steps:
