Prompt file imported from hans5549/hans-exercism-csharp (.github/prompts/model-recommendation.prompt.md). Fill in {{filePath}}, {{subscriptionTier}}, {{priorityFactor}} before use. Copyright stays with the author.

AI Model Recommendation for Copilot Chat Modes and Prompts

Mission

Analyze .chatmode.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 .chatmode.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:

• {{filePath}} - Absolute or workspace-relative path to the file to analyze

Optional:

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

Workflow

1. File Analysis Phase

Read and Parse File:

• Read the target .chatmode.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

2. 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

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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
  │   ├─ Medium (50-200K) → Claude models, GPT-5, Gemini
  │   ├─ Large (200K-1M) → Gemini 2.5 Pro, Claude Opus 4.1
  │   └─ Very Large (>1M) → Gemini 2.5 Pro (2M), Claude Opus 4.1 (1M)
  │
  ├─ Vision Required?
  │   ├─ Yes → GPT-4.1, GPT-5, Claude Sonnet 3.5/4.5, Gemini 2.5 Pro, Claude Opus 4.1
  │   └─ No → All models
  │
  ├─ Cost Sensitivity? (based on subscriptionTier)
  │   ├─ Free Tier → 0x models only: GPT-4.1, GPT-5 mini, Grok Code Fast 1
  │   ├─ Pro (1000 premium/month) → Prioritize 0x, use 1x judiciously, avoid 10x
  │   └─ Pro+ (5000 premium/month) → 1x freely, 10x for critical tasks
  │
  └─ Priority Factor?
      ├─ Speed → GPT-5 mini, Grok Code Fast 1, Gemini 2.0 Flash
      ├─ Cost → 0x models (GPT-4.1, GPT-5 mini) or lower multipliers (0.25x, 0.33x)
      ├─ Quality → Claude Sonnet 4.5, GPT-5, Claude Opus 4.1
      └─ Balanced → GPT-4.1, Claude Sonnet 4, GPT-5

3. Recommendation Generation Phase

Primary Recommendation:

• Identify the single best model based on task analysis and decision tree
• Provide specific rationale tied to file content characteristics
• Explain multiplier cost implications for user's subscription tier

Alternative Recommendations:

• Suggest 1-2 alternative models with trade-off explanations
• Include scenarios where alternatives might be preferred
• Consider priority factor overrides (speed vs. quality vs. cost)

Auto-Selection Guidance:

• Assess if task is suitable for auto model selection (excludes premium models > 1x)
• Explain when manual selection is beneficial vs. letting Copilot choose
• Note any limitations of auto-selection for the specific task

Deprecation Warnings:

• Flag if file currently specifies a deprecated model (o3, o4-mini, Claude Sonnet 3.7, Gemini 2.0 Flash)
• Provide migration path to recommended replacement
• Include timeline for deprecation (e.g., "o3 deprecating 2025-10-23")

Subscription Tier Considerations:

• Free Tier: Recommend only 0x multiplier models (GPT-4.1, GPT-5 mini, Grok Code Fast 1)
• Pro Tier: Balance between 0x (unlimited) and 1x (1000/month) models
• Pro+ Tier: More freedom with 1x models (5000/month), justify 10x usage for exceptional cases

4. Integration Recommendations

Frontmatter Update Guidance:

If file does not specify a model field:

## Recommendation: Add Model Specification

Current frontmatter:
\`\`\`yaml

---

description: "..."
tools: [...]

---

\`\`\`

Recommended frontmatter:
\`\`\`yaml

---

description: "..."
model: "[Recommended Model Name]"
tools: [...]

---

\`\`\`

Rationale: [Explanation of why this model is optimal for this task]

If file already specifies a model:

## Current Model Assessment

Specified model: `[Current Model]` (Multiplier: [X]x)

Recommendation: [Keep current model | Consider switching to [Recommended Model]]

Rationale: [Explanation]

Tool Alignment Check:

Verify model capabilities align with specified tools:

• If tools include context7/* or sequential-thinking/*: Recommend advanced reasoning models (Claude Sonnet 4.5, GPT-5, Claude Opus 4.1)
• If tools include vision-related references: Ensure model supports images (flag if GPT-5 Codex, Claude Sonnet 4, or mini models selected)
• If tools are read-only (search, fetch): Suggest cost-effective models (GPT-5 mini, Grok Code Fast 1)

5. Context7 Integration for Up-to-Date Information

Leverage Context7 for Model Documentation:

When uncertainty exists about current model capabilities, use Context7 to fetch latest information:

**Verification with Context7**:

Using `context7/get-library-docs` with library ID `/websites/github_en_copilot`:

- Query topic: "model capabilities [specific capability question]"
- Retrieve current model features, multipliers, deprecation status
- Cross-reference against analyzed file requirements

Example Context7 Usage:

If unsure whether Claude Sonnet 4.5 supports image analysis:
→ Use context7 with topic "Claude Sonnet 4.5 vision image capabilities"
→ Confirm feature support before recommending for multi-modal tasks

Output Expectations

Report Structure

Generate a structured markdown report with the following sections:

# AI Model Recommendation Report

**File Analyzed**: `[file path]`
**File Type**: [chatmode | prompt]
**Analysis Date**: [YYYY-MM-DD]
**Subscription Tier**: [Free | Pro | Pro+]

---

## File Summary

**Description**: [from frontmatter]
**Mode**: [ask | edit | agent]
**Tools**: [tool list]
**Current Model**: [specified model or "Not specified"]

## Task Analysis

### Task Complexity

- **Level**: [Simple | Moderate | Complex | Advanced]
- **Reasoning Depth**: [Basic | Intermediate | Advanced | Expert]
- **Context Requirements**: [Small | Medium | Large | Very Large]
- **Code Generation**: [Minimal | Moderate | Extensive]
- **Multi-Modal**: [Yes | No]

### Task Category

[Primary category from 8 categories listed in Workflow Phase 1]

### Key Characteristics

- Characteristic 1: [explanation]
- Characteristic 2: [explanation]
- Characteristic 3: [explanation]

## Model Recommendation

### 🏆 Primary Recommendation: [Model Name]

**Multiplier**: [X]x ([cost implications for subscription tier])
**Strengths**:

- Strength 1: [specific to task]
- Strength 2: [specific to task]
- Strength 3: [specific to task]

**Rationale**:
[Detailed explanation connecting task characteristics to model capabilities]

**Cost Impact** (for [Subscription Tier]):

- Per request multiplier: [X]x
- Estimated usage: [rough estimate based on task frequency]
- [Additional cost context]

### 🔄 Alternative Options

#### Option 1: [Model Name]

- **Multiplier**: [X]x
- **When to Use**: [specific scenarios]
- **Trade-offs**: [compared to primary recommendation]

#### Option 2: [Model Name]

- **Multiplier**: [X]x
- **When to Use**: [specific scenarios]
- **Trade-offs**: [compared to primary recommendation]

### 📊 Model Comparison for This Task

| Criterion        | [Primary Model] | [Alternative 1] | [Alternative 2] |
| ---------------- | --------------- | --------------- | --------------- |
| Task Fit         | ⭐⭐⭐⭐⭐      | ⭐⭐⭐⭐        | ⭐⭐⭐          |
| Code Quality     | [rating]        | [rating]        | [rating]        |
| Reasoning        | [rating]        | [rating]        | [rating]        |
| Speed            | [rating]        | [rating]        | [rating]        |
| Cost Efficiency  | [rating]        | [rating]        | [rating]        |
| Context Capacity | [capacity]      | [capacity]      | [capacity]      |
| Vision Support   | [Yes/No]        | [Yes/No]        | [Yes/No]        |

## Auto Model Selection Assessment

**Suitability**: [Recommended | Not Recommended | Situational]

[Explanation of whether auto-selection is appropriate for this task]

**Rationale**:

- [Reason 1]
- [Reason 2]

**Manual Override Scenarios**:

- [Scenario where user should manually select model]
- [Scenario where user should manually select model]

## Implementation Guidance

### Frontmatter Update

[Provide specific code block showing recommended frontmatter change]

### Model Selection in VS Code

**To Use Recommended Model**:

1. Open Copilot Chat
2. Click model dropdown (currently shows "[current model or Auto]")
3. Select **[Recommended Model Name]**
4. [Optional: When to switch back to Auto]

**Keyboard Shortcut**: `Cmd+Shift+P` → "Copilot: Change Model"

### Tool Alignment Verification

[Check results: Are specified tools compatible with recommended model?]

✅ **Compatible Tools**: [list]
⚠️ **Potential Limitations**: [list if any]

## Deprecation Notices

[If applicable, list any deprecated models in current configuration]

⚠️ **Deprecated Model in Use**: [Model Name] (Deprecation date: [YYYY-MM-DD])

**Migration Path**:

- **Current**: [Deprecated Model]
- **Replacement**: [Recommended Model]
- **Action Required**: Update `model:` field in frontmatter by [date]
- **Behavioral Changes**: [any expected differences]

## Context7 Verification

[If Context7 was used for verification]

**Queries Executed**:

- Topic: "[query topic]"
- Library: `/websites/github_en_copilot`
- Key Findings: [summary]

## Additional Considerations

### Subscription Tier Recommendations

[Specific advice based on Free/Pro/Pro+ tier]

### Priority Factor Adjustments

[If user specified Speed/Cost/Quality/Balanced, explain how recommendation aligns]

### Long-Term Model Strategy

[Advice for when to re-evaluate model selection as file evolves]

---

## Quick Reference

**TL;DR**: Use **[Primary Model]** for this task due to [one-sentence rationale]. Cost: [X]x multiplier.

**One-Line Update**:
\`\`\`yaml
model: "[Recommended Model Name]"
\`\`\`

Output Quality Standards

• Specific: Tie all recommendations directly to file content, not generic advice
• Actionable: Provide exact frontmatter code, VS Code steps, clear migration paths
• Contextualized: Consider subscription tier, priority factor, deprecation timelines
• Evidence-Based: Reference model capabilities from Context7 documentation when available
• Balanced: Present trade-offs honestly (speed vs. quality vs. cost)
• Up-to-Date: Flag deprecated models, suggest current alternatives

Quality Assurance

Validation Steps

• File successfully read and parsed
• Frontmatter extracted correctly (or noted if missing)
• Task complexity accurately categorized (Simple/Moderate/Complex/Advanced)
• Primary task category identified from 8 options
• Model recommendation aligns with decision tree logic
• Multiplier cost explained for user's subscription tier
• Alternative models provided with clear trade-off explanations
• Auto-selection guidance included (recommended/not recommended/situational)
• Deprecated model warnings included if applicable
• Frontmatter update example provided (valid YAML)
• Tool alignment verified (model capabilities match specified tools)
• Context7 used when verification needed for latest model information
• Report includes all required sections (summary, analysis, recommendation, implementation)

Success Criteria

• Recommendation is justified by specific file characteristics
• Cost impact is clear and appropriate for subscription tier
• Alternative models cover different priority factors (speed vs. quality vs. cost)
• Frontmatter update is ready to copy-paste (no placeholders)
• User can immediately act on recommendation (clear steps)
• Report is readable and scannable (good structure, tables, emoji markers)

Failure Triggers

• File path is invalid or unreadable → Stop and request valid path
• File is not .chatmode.md or .prompt.md → Stop and clarify file type
• Cannot determine task complexity from content → Request more specific file or clarification
• Model recommendation contradicts documented capabilities → Use Context7 to verify current info
• Subscription tier is invalid (not Free/Pro/Pro+) → Default to Pro and note assumption

Advanced Use Cases

Analyzing Multiple Files

If user provides multiple files:

1. Analyze each file individually
2. Generate separate recommendations per file
3. Provide summary table comparing recommendations
4. Note any patterns (e.g., "All debug-related modes benefit from Claude Sonnet 4.5")

Comparative Analysis

If user asks "Which model is better between X and Y for this file?":

1. Focus comparison on those two models only
2. Use side-by-side table format
3. Declare a winner with specific reasoning
4. Include cost comparison for subscription tier

Migration Planning

If file specifies a deprecated model:

1. Prioritize migration guidance in report
2. Test current behavior expectations vs. replacement model capabilities
3. Provide phased migration if breaking changes expected
4. Include rollback plan if needed

Examples

Example 1: Simple Formatting Task

File: format-code.prompt.md Content: "Format Python code with Black style, add type hints" Recommendation: GPT-5 mini (0x multiplier, fastest, sufficient for repetitive formatting) Alternative: Grok Code Fast 1 (0.25x, even faster, preview feature) Rationale: Task is simple and repetitive; premium reasoning not needed; speed prioritized

Example 2: Complex Architecture Review

File: architect.chatmode.md Content: "Review system design for scalability, security, maintainability; analyze trade-offs; provide ADR-level recommendations" Recommendation: Claude Sonnet 4.5 (1x multiplier, expert reasoning, excellent for architecture) Alternative: Claude Opus 4.1 (10x, use for very large codebases >500K tokens) Rationale: Requires deep reasoning, architectural expertise, design pattern knowledge; Sonnet 4.5 excels at this

Example 3: Django Expert Mode

File: django.chatmode.md Content: "Django 5.x expert with ORM optimization, async views, REST API design; uses context7 for up-to-date Django docs" Recommendation: GPT-5 (1x multiplier, advanced reasoning, excellent code quality) Alternative: Claude Sonnet 4.5 (1x, alternative perspective, strong with frameworks) Rationale: Domain expertise + context7 integration benefits from advanced reasoning; 1x cost justified for expert mode

Example 4: Free Tier User with Planning Mode

File: plan.chatmode.md Content: "Research and planning mode with read-only tools (search, fetch, githubRepo)" Subscription: Free (2K completions + 50 chat requests/month, 0x models only) Recommendation: GPT-4.1 (0x, balanced, included in Free tier) Alternative: GPT-5 mini (0x, faster but less context) Rationale: Free tier restricted to 0x models; GPT-4.1 provides best balance of quality and context for planning tasks

Knowledge Base

Model Multiplier Cost Reference

Model Changelog & Deprecations (October 2025)

Deprecated Models (Effective 2025-10-23):

• ❌ o3 (1x) → Replace with GPT-5 or Claude Sonnet 4.5 for reasoning
• ❌ o4-mini (0.33x) → Replace with GPT-5 mini (0x) for cost, GPT-5 (1x) for quality
• ❌ Claude Sonnet 3.7 (1x) → Replace with Claude Sonnet 4 or 4.5
• ❌ Claude Sonnet 3.7 Thinking (1.25x) → Replace with Claude Sonnet 4.5
• ❌ Gemini 2.0 Flash (0.25x) → Replace with Grok Code Fast 1 (0.25x) or GPT-5 mini (0x)

Preview Models (Subject to Change):

• 🧪 Claude Sonnet 4.5 (1x) - Preview status, may have API changes
• 🧪 Grok Code Fast 1 (0.25x) - Preview, free during preview period

Stable Production Models:

• ✅ GPT-4.1, GPT-5, GPT-5 mini, GPT-5 Codex (OpenAI)
• ✅ Claude Sonnet 3.5, Claude Sonnet 4, Claude Opus 4.1 (Anthropic)
• ✅ Gemini 2.5 Pro (Google)

Auto Model Selection Behavior (Sept 2025+)

Included in Auto Selection:

• GPT-4.1 (0x)
• GPT-5 mini (0x)
• GPT-5 (1x)
• Claude Sonnet 3.5 (1x)
• Claude Sonnet 4.5 (1x)

Excluded from Auto Selection:

• Models with multiplier > 1 (Claude Opus 4.1, deprecated o3)
• Models blocked by admin policies
• Models unavailable in subscription plan (1x models in Free tier)

When Auto Selects:

• Copilot analyzes prompt complexity, context size, task type
• Chooses from eligible pool based on availability and rate limits
• Applies 10% multiplier discount on auto-selected models
• Shows selected model on hover over response in Chat view

Context7 Query Templates

Use these query patterns when verification needed:

Model Capabilities:

Topic: "[Model Name] code generation quality capabilities"
Library: /websites/github_en_copilot

Model Multipliers:

Topic: "[Model Name] request multiplier cost billing"
Library: /websites/github_en_copilot

Deprecation Status:

Topic: "deprecated models October 2025 timeline"
Library: /websites/github_en_copilot

Vision Support:

Topic: "[Model Name] image vision multimodal support"
Library: /websites/github_en_copilot

Auto Selection:

Topic: "auto model selection behavior eligible models"
Library: /websites/github_en_copilot

Last Updated: 2025-10-28 Model Data Current As Of: October 2025 Deprecation Deadline: 2025-10-23 for o3, o4-mini, Claude Sonnet 3.7 variants, Gemini 2.0 Flash