Lesson 7: Tool Selection Framework — Claude Code vs Gemini CLI
The Problem You're About to Solve
You're facing a developer's dilemma.
Your team just inherited a massive codebase: 150,000 lines of legacy Python. The previous team documented nothing. The architecture is unclear. Before you can modernize it, you need to understand what you're looking at.
You have two tools available:
Option A: Claude Code
- Deep reasoning capability
- Focused development workflow
- But: Limited context window (200,000 tokens standard)
Option B: Gemini CLI
- Massive context window (2 million tokens)
- Can load entire large codebases at once
- Different interface, less IDE integration
The Dilemma: Do you use Claude Code's superior reasoning for architectural analysis? Or Gemini CLI's context breadth to see the full picture?
Different answer for different situations.
This lesson teaches you to recognize task characteristics, evaluate tool capabilities, and select the right tool for your situation — sometimes Claude Code, sometimes Gemini CLI, sometimes both in sequence.
This is Layer 2-3 Hybrid: AI Collaboration (evaluating options) + Intelligence Design (creating reusable framework).
Tool Comparison: Verified 2025 Specifications
Understanding your tool options requires accurate, current specifications.
Claude Code (Anthropic)
| Capability | Standard | Extended (Beta) |
|---|---|---|
| Context Window | 200,000 tokens | 1,000,000 tokens (tier 4+) |
| Output Capacity | 64,000 tokens | 64,000 tokens |
| Reasoning Depth | Superior for complex architectural decisions | Superior |
| File Selection | Progressive loading (you choose files) | Progressive loading |
| Typical Session Length | 1-4 hours (depending on file size) | 4-8+ hours |
| Cost Model | Input + output tokens billed | Input + output tokens billed |
What 200K tokens means:
- ~500 pages of documentation
- ~50 typical Python files (2000 lines each)
- ~100+ typical JavaScript files
- Complete single-module projects
What 1M tokens means (extended):
- Multiple large modules
- Complete system documentation
- Extended refactoring across modules
- Multi-service architecture exploration
Best for:
- Focused development on specific features
- Deep architectural reasoning about complex systems
- Long sessions with persistent context (using memory files from Lesson 6)
- When you need explicit control over which files are loaded
Gemini CLI (Google)
| Capability | Standard | Extended |
|---|---|---|
| Context Window | 128,000 tokens | 2,000,000 tokens |
| File Handling | Load files specified at startup | Load entire large codebases |
| Reasoning Approach | Pattern analysis across files | Whole-system pattern analysis |
| File Selection | You list files to include | Load everything, AI analyzes |
| Typical Use | Utility scripts, small projects | Large codebase exploration |
| Code File Capacity | 10,000+ lines of code | 500,000+ lines of code |
What 2M tokens means:
- Entire 100K+ line codebases
- Complete documentation + code
- Multiple services + test suite
- Full architectural pattern analysis
Best for:
- Understanding unfamiliar large systems
- Broad pattern analysis across entire codebase
- Refactoring decisions that span multiple modules
- Legacy system exploration and documentation
Key Differences at a Glance
| Dimension | Claude Code | Gemini CLI |
|---|---|---|
| Reasoning | Deep, step-by-step | Broad pattern analysis |
| Context Control | High (you choose files) | Low (load all, AI analyzes) |
| IDE Integration | Excellent (Claude Code IDE) | CLI-based |
| Session Duration | Flexible (1-8+ hours with memory) | Single-session analysis |
| Error Recovery | Good (can refine approach) | Good (access to full codebase) |
| Typical Workflow | "Implement this feature" | "Understand this codebase" |
Decision Framework: Which Tool Should I Use?
Your choice depends on task characteristics: scope, complexity, reasoning need, context budget.
Dimension 1: Task Scope — How Much Code?
Small scope (1-5 files, <10K lines):
- Choose: Claude Code
- Why: 200K context is more than enough. You get IDE integration and deep reasoning.
- Example: Implement single endpoint in FastAPI module
Medium scope (5-50 files, 10K-100K lines):
- Choose: Claude Code with progressive loading (Lesson 3)
- Why: Progressive loading keeps context focused. Deep reasoning for architectural decisions.
- Example: Refactor authentication module across 20 files
Large scope (100+ files, 100K+ lines):
- Choose: Gemini CLI for exploration, then Claude Code for implementation
- Why: First understand architecture with Gemini, then implement details with Claude
- Example: Understand inherited 150K-line legacy system, then modernize
Dimension 2: Task Complexity — How Much Reasoning?
Low complexity (straightforward execution):
- Choose: Either tool works
- Prefer: Claude Code (better IDE integration)
- Example: Add new database column, update tests
Medium complexity (architectural decisions needed):
- Choose: Claude Code
- Why: Superior reasoning for tradeoff analysis
- Example: Design session management strategy (JWT vs sessions vs OAuth)
High complexity (understand patterns across entire system):
- Choose: Gemini CLI
- Why: Need to see full codebase patterns at once
- Example: Identify code duplication patterns across 100+ files
Dimension 3: Context Budget — Can I Afford to Load Everything?
Abundant context (small codebase, simple architecture):
- Choose: Claude Code
- Load: Entire relevant codebase at once
- Example: 20-file project, load all files in one session
Limited context (large codebase, must be selective):
- Choose: Claude Code with memory files
- Load: Essential context only (foundation + current work), use on-demand loading
- Example: 500-file project, load foundation (10 files) + current task (5 files), fetch others as needed
Massive codebase (want to see everything):
- Choose: Gemini CLI
- Load: Entire codebase
- Example: Legacy 100K-line system, load all files, analyze patterns
Dimension 4: File Selection Control — Do I Need to Be Selective?
High control needed (I know which files matter):
- Choose: Claude Code
- Why: You manually select files, AI focuses on what you choose
- Example: "I only care about payment processing, ignore UI code"
Low control acceptable (AI should see everything):
- Choose: Gemini CLI
- Why: Load entire codebase, AI finds relevant patterns automatically
- Example: "Here's the codebase. Find all database-related code."
Decision Tree: Pick Your Tool
Follow this decision tree for any AI-assisted development task:
Task: "I need help with my codebase"
START HERE: How many lines of code?
├─ 1-10K lines?
│ └─> Consider Claude Code (plenty of context)
│ ├─ Need deep architectural reasoning?
│ │ └─> CHOOSE: Claude Code
│ └─ Just need exploratory analysis?
│ └─> CHOOSE: Either (Claude Code for IDE, Gemini CLI if massive)
│
├─ 10K-100K lines?
│ └─> How much of it do you need in context?
│ ├─ Only specific modules (you can point to them)?
│ │ └─> CHOOSE: Claude Code with progressive loading
│ └─ Need to see patterns across whole system?
│ └─> CHOOSE: Explore with Gemini CLI first, implement with Claude Code
│
└─ 100K+ lines?
└─> What's your goal?
├─ Understand architecture?
│ └─> CHOOSE: Gemini CLI (2M context)
│ Then: Switch to Claude Code for specific implementation
│
├─ Implement focused feature?
│ └─> CHOOSE: Claude Code with memory files
│ Load: Foundation (10 files) + current task (5 files)
│ Use: Lesson 6 memory files for architectural context
│
└─ Refactor across multiple modules?
└─> CHOOSE: Two-phase
Phase 1: Gemini CLI to understand current patterns
Phase 2: Claude Code to implement refactoring
Real-World Scenario Analysis
Let's apply the framework to five real situations:
Scenario 1: Implement New Endpoint (Small Scope, High Reasoning)
Situation: FastAPI project with 20 files. You need to implement a new /reports endpoint. It requires database queries, caching decisions, and authorization logic.
Analysis:
- Scope: Small (20 files, only 3-5 matter)
- Complexity: Medium (architectural decisions about caching, auth)
- Context budget: Abundant (200K tokens handles everything)
- File control: High (you know exactly which files matter)
Tool choice: Claude Code
- Load files: models/Report.py, services/auth.py, services/cache.py, routes/, main.py
- Deep reasoning needed for: "Where should caching logic live?" "How to integrate with existing auth?"
- IDE integration helps: Implement, test, iterate quickly
Scenario 2: Refactor Entire API Validation (Medium-Large Scope, Moderate Reasoning)
Situation: FastAPI project with 50 files. All endpoints validate input differently. You need to implement consistent validation framework. Must understand all endpoints before deciding approach.
Analysis:
- Scope: Large (50 files, need to see validation patterns across all 30 endpoints)
- Complexity: High (architectural decision about validation strategy)
- Context budget: Constrained (can't load all endpoints at once in depth)
- File control: Need selective (some endpoints are irrelevant)
Tool choice: Two-phase approach
Phase 1 — Gemini CLI: "Load entire codebase, show me all unique validation patterns across all 30 endpoints. What are the 5 most common patterns?"
- Gemini loads all 50 files
- Identifies pattern categories
- Delivers: "You have 5 validation patterns, here's the breakdown"
Phase 2 — Claude Code: "Based on these 5 patterns, design unified validation framework. Then implement it across all endpoints."
- Claude handles detailed reasoning about framework design
- Loads: validation/, routes/, models/ (essential files)
- Implements framework with IDE support
Scenario 3: Understand Legacy System (Very Large, Exploration Priority)
Situation: Inherited 150K-line Python monolith. Previous team left no docs. You need to understand architecture before proposing modernization.
Analysis:
- Scope: Massive (150 files, 150K lines)
- Complexity: High (need to understand patterns, dependencies, architecture)
- Context budget: Impossible with Claude Code alone (200K tokens insufficient)
- File control: Not applicable (need to see everything)
Tool choice: Gemini CLI
- 2M context handles entire codebase
- Prompts:
- "What's the overall architecture?" (Gemini analyzes all 150 files)
- "What are the biggest code duplication problems?" (Cross-file analysis)
- "What's the module dependency graph?" (System-level understanding)
- Output: Complete architectural overview
- Next step: Use Claude Code for specific refactoring once you understand what to change
Scenario 4: Emergency Bug Fix (Unknown Module, Time Pressure)
Situation: Critical bug in production. Module you've never seen. 100-file codebase. Bug must be fixed in 2 hours.
Analysis:
- Scope: Large (100 files, but only 3-5 relevant to bug)
- Complexity: Medium (understand bug, implement fix)
- Context budget: Good if selective (Claude Code is enough if you load right files)
- File control: Critical (load only files related to bug)
Tool choice: Claude Code with smart file selection
- Work with Claude to identify which files contain the bug
- Load only those files + related dependencies
- Example loading: "Payment webhook handler, Stripe models, database transaction layer"
- Claude's reasoning helps: "Here's the bug: you're not checking signature before processing webhook"
- IDE integration: Make the fix, test, deploy quickly
Scenario 5: Multi-Day Feature Development (Deep Work, Session Continuity)
Situation: Building new feature over 5 days. Requires deep architectural understanding. Lots of sessions, must maintain context across days.
Analysis:
- Scope: Medium-large (30 files, but evolving as you build)
- Complexity: High (architectural decisions, multiple iterations)
- Context budget: Persistent across sessions (use memory files)
- File control: High (you know which files matter for this feature)
Tool choice: Claude Code + Memory Files (Lesson 6)
- Day 1: Design architecture, document in memory files
- Day 2: Implement core logic (Claude reads memory from Day 1)
- Day 3: Add integration (Claude uses decisions from Days 1-2)
- Day 4-5: Testing and refinement (full context persistence)
- IDE integration keeps workflow smooth
- Memory files from Lesson 6 eliminate re-explanation
Building the Reusable Skill: tool-selection-framework
You've learned to evaluate tools. Now let's create a reusable skill for future projects.
What Goes Into the Skill
The tool-selection-framework skill captures:
- Verified specifications table (Claude Code vs Gemini CLI, updatable as specs change)
- Decision criteria (task scope, complexity, context budget, file control)
- Decision tree (flowchart for tool selection)
- Scenario examples (5+ real situations with analysis)
- Hybrid approach guidance (when to use both tools sequentially)
Using the Skill in Future Projects
For ANY new development task:
- Ask yourself: "How many files? What complexity? What's my reasoning need?"
- Reference tool-selection-framework decision tree
- Follow framework to select appropriate tool
- Execute with confidence (you've evaluated the decision)
This is reusable intelligence — the decision-making process you learned here applies to any future tool selection challenge.
Try With AI
Setup: Think of a real project you have or a codebase you could work with.
Prompt Set:
Prompt 1: Analyze Your Codebase
I have a codebase with these characteristics:
- [Number of files]: [50/150/500]
- [Total lines of code]: [10K/100K/500K]
- [Description of what it does]
My task is: [implement feature / understand architecture / refactor code / fix bug]
Based on the tool selection framework (Claude Code vs Gemini CLI),
which tool should I use? Explain your reasoning considering:
- Task scope (how much code?)
- Complexity (how much reasoning needed?)
- Context budget (can I load everything?)
- File control (do I need to be selective?)
Prompt 2: Two-Phase Approach
I need to both understand my large codebase AND implement specific changes.
[Describe your codebase and goals]
How would a two-phase approach work?
- Phase 1: What would I use Gemini CLI for?
- Phase 2: What would I use Claude Code for?
- How would insights from Phase 1 guide Phase 2?
Prompt 3: Memory Files for Long-Term Development
I'm planning a 5-day feature implementation in a 100-file codebase.
Based on Lesson 6 (Memory Files), how should I structure memory files
to support Claude Code sessions across all 5 days?
What should go in CLAUDE.md, architecture.md, and decisions.md
for this feature development?
Expected Outcomes:
- Prompt 1: Clear tool recommendation with reasoning
- Prompt 2: Detailed two-phase strategy
- Prompt 3: Memory file structure supporting multi-day continuity
Safety Note: When using Gemini CLI for large codebase exploration, be mindful of what you upload. Never include files with secrets, credentials, or PII. Use .gitignore equivalents to exclude sensitive data before loading codebases into AI tools.