Model Recommendation

AI Model Recommendation for Copilot Chat Modes and Prompts

Mission

Analyze .agent.md or .prompt.md files to understand their purpose, complexity, and required capabilities, then recommend the most suitable AI model(s) from GitHub Copilot's available options. Provide rationale based on task characteristics, model strengths, cost-efficiency, and performance trade-offs.

Scope & Preconditions

• Input: Path to a .agent.md or .prompt.md file
• Available Models: GPT-4.1, GPT-5, GPT-5 mini, GPT-5 Codex, Claude Sonnet 3.5, Claude Sonnet 4, Claude Sonnet 4.5, Claude Opus 4.1, Gemini 2.5 Pro, Gemini 2.0 Flash, Grok Code Fast 1, o3, o4-mini (with deprecation dates)
• Model Auto-Selection: Available in VS Code (Sept 2025+) - selects from GPT-4.1, GPT-5 mini, GPT-5, Claude Sonnet 3.5, Claude Sonnet 4.5 (excludes premium multipliers > 1)
• Context: GitHub Copilot subscription tiers (Free: 2K completions + 50 chat/month with 0x models only; Pro: unlimited 0x + 1000 premium/month; Pro+: unlimited 0x + 5000 premium/month)

Inputs

Required:

• ${input:filePath:Path to .agent.md or .prompt.md file} - Absolute or workspace-relative path to the file to analyze

Optional:

• ${input:subscriptionTier:Pro} - User's Copilot subscription tier (Free, Pro, Pro+) - defaults to Pro
• ${input:priorityFactor:Balanced} - Optimization priority (Speed, Cost, Quality, Balanced) - defaults to Balanced

Workflow

1. File Analysis Phase

Read and Parse File:

• Read the target .agent.md or .prompt.md file
• Extract frontmatter (description, mode, tools, model if specified)
• Analyze body content to identify:
• Task complexity (simple/moderate/complex/advanced)
• Required reasoning depth (basic/intermediate/advanced/expert)
• Code generation needs (minimal/moderate/extensive)
• Multi-turn conversation requirements
• Context window needs (small/medium/large)
• Specialized capabilities (image analysis, long-context, real-time data)

Categorize Task Type:

Identify the primary task category based on content analysis:

1. Simple Repetitive Tasks:

• Pattern: Formatting, simple refactoring, adding comments/docstrings, basic CRUD
• Characteristics: Straightforward logic, minimal context, fast execution preferred
• Keywords: format, comment, simple, basic, add docstring, rename, move

1. Code Generation & Implementation:

• Pattern: Writing functions/classes, implementing features, API endpoints, tests
• Characteristics: Moderate complexity, domain knowledge, idiomatic code
• Keywords: implement, create, generate, write, build, scaffold

1. Complex Refactoring & Architecture:

• Pattern: System design, architectural review, large-scale refactoring, performance optimization
• Characteristics: Deep reasoning, multiple components, trade-off analysis
• Keywords: architect, refactor, optimize, design, scale, review architecture

1. Debugging & Problem-Solving:

• Pattern: Bug fixing, error analysis, systematic troubleshooting, root cause analysis
• Characteristics: Step-by-step reasoning, debugging context, verification needs
• Keywords: debug, fix, troubleshoot, diagnose, error, investigate

1. Planning & Research:

• Pattern: Feature planning, research, documentation analysis, ADR creation
• Characteristics: Read-only, context gathering, decision-making support
• Keywords: plan, research, analyze, investigate, document, assess

1. Code Review & Quality Analysis:

• Pattern: Security analysis, performance review, best practices validation, compliance checking
• Characteristics: Critical thinking, pattern recognition, domain expertise
• Keywords: review, analyze, security, performance, compliance, validate

1. Specialized Domain Tasks:

• Pattern: Django/framework-specific, accessibility (WCAG), testing (TDD), API design
• Characteristics: Deep domain knowledge, framework conventions, standards compliance
• Keywords: django, accessibility, wcag, rest, api, testing, tdd

1. Advanced Reasoning & Multi-Step Workflows:

• Pattern: Algorithmic optimization, complex data transformations, multi-phase workflows
• Characteristics: Advanced reasoning, mathematical/algorithmic thinking, sequential logic
• Keywords: algorithm, optimize, transform, sequential, reasoning, calculate

Extract Capability Requirements:

Based on tools in frontmatter and body instructions:

• Read-only tools (search, fetch, usages, githubRepo): Lower complexity, faster models suitable
• Write operations (edit/editFiles, new): Moderate complexity, accuracy important
• Execution tools (runCommands, runTests, runTasks): Validation needs, iterative approach
• Advanced tools (context7/\, sequential-thinking/\): Complex reasoning, premium models beneficial
• Multi-modal (image analysis references): Requires vision-capable models

2. Model Evaluation Phase

Apply Model Selection Criteria:

For each available model, evaluate against these dimensions:

Model Capabilities Matrix

Selection Decision Tree

START
  │
  ├─ Task Complexity?
  │   ├─ Simple/Repetitive → GPT-5 mini, Grok Code Fast 1, GPT-4.1
  │   ├─ Moderate → GPT-4.1, Claude Sonnet 4, GPT-5
  │   └─ Complex/Advanced → Claude Sonnet 4.5, GPT-5, Gemini 2.5 Pro, Claude Opus 4.1
  │
  ├─ Reasoning Depth?
  │   ├─ Basic → GPT-5 mini, Grok Code Fast 1
  │   ├─ Intermediate → GPT-4.1, Claude Sonnet 4
  │   ├─ Advanced → GPT-5, Claude Sonnet 4.5
  │   └─ Expert → Claude Opus 4.1, o3 (deprecated)
  │
  ├─ Code-Specific?
  │   ├─ Yes → GPT-5 Codex, Claude Sonnet 4.5, GPT-5
  │   └─ No → GPT-5, Claude Sonnet 4
  │
  ├─ Context Size?
  │   ├─ Small (<50K tokens) → Any model