{"id":"3177b4a7-f56c-4c8e-a78c-6d831e78b66c","shortId":"33rGy5","kind":"skill","title":"Model Recommendation","tagline":"Awesome Copilot skill by Github","description":"# AI Model Recommendation for Copilot Chat Modes and Prompts\n\n## Mission\n\nAnalyze `.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.\n\n## Scope & Preconditions\n\n- **Input**: Path to a `.agent.md` or `.prompt.md` file\n- **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)\n- **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)\n- **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)\n\n## Inputs\n\nRequired:\n\n- `${input:filePath:Path to .agent.md or .prompt.md file}` - Absolute or workspace-relative path to the file to analyze\n\nOptional:\n\n- `${input:subscriptionTier:Pro}` - User's Copilot subscription tier (Free, Pro, Pro+) - defaults to Pro\n- `${input:priorityFactor:Balanced}` - Optimization priority (Speed, Cost, Quality, Balanced) - defaults to Balanced\n\n## Workflow\n\n### 1. File Analysis Phase\n\n**Read and Parse File**:\n\n- Read the target `.agent.md` or `.prompt.md` file\n- Extract frontmatter (description, mode, tools, model if specified)\n- Analyze body content to identify:\n  - Task complexity (simple/moderate/complex/advanced)\n  - Required reasoning depth (basic/intermediate/advanced/expert)\n  - Code generation needs (minimal/moderate/extensive)\n  - Multi-turn conversation requirements\n  - Context window needs (small/medium/large)\n  - Specialized capabilities (image analysis, long-context, real-time data)\n\n**Categorize Task Type**:\n\nIdentify the primary task category based on content analysis:\n\n1. **Simple Repetitive Tasks**:\n\n   - Pattern: Formatting, simple refactoring, adding comments/docstrings, basic CRUD\n   - Characteristics: Straightforward logic, minimal context, fast execution preferred\n   - Keywords: format, comment, simple, basic, add docstring, rename, move\n\n2. **Code Generation & Implementation**:\n\n   - Pattern: Writing functions/classes, implementing features, API endpoints, tests\n   - Characteristics: Moderate complexity, domain knowledge, idiomatic code\n   - Keywords: implement, create, generate, write, build, scaffold\n\n3. **Complex Refactoring & Architecture**:\n\n   - Pattern: System design, architectural review, large-scale refactoring, performance optimization\n   - Characteristics: Deep reasoning, multiple components, trade-off analysis\n   - Keywords: architect, refactor, optimize, design, scale, review architecture\n\n4. **Debugging & Problem-Solving**:\n\n   - Pattern: Bug fixing, error analysis, systematic troubleshooting, root cause analysis\n   - Characteristics: Step-by-step reasoning, debugging context, verification needs\n   - Keywords: debug, fix, troubleshoot, diagnose, error, investigate\n\n5. **Planning & Research**:\n\n   - Pattern: Feature planning, research, documentation analysis, ADR creation\n   - Characteristics: Read-only, context gathering, decision-making support\n   - Keywords: plan, research, analyze, investigate, document, assess\n\n6. **Code Review & Quality Analysis**:\n\n   - Pattern: Security analysis, performance review, best practices validation, compliance checking\n   - Characteristics: Critical thinking, pattern recognition, domain expertise\n   - Keywords: review, analyze, security, performance, compliance, validate\n\n7. **Specialized Domain Tasks**:\n\n   - Pattern: Django/framework-specific, accessibility (WCAG), testing (TDD), API design\n   - Characteristics: Deep domain knowledge, framework conventions, standards compliance\n   - Keywords: django, accessibility, wcag, rest, api, testing, tdd\n\n8. **Advanced Reasoning & Multi-Step Workflows**:\n   - Pattern: Algorithmic optimization, complex data transformations, multi-phase workflows\n   - Characteristics: Advanced reasoning, mathematical/algorithmic thinking, sequential logic\n   - Keywords: algorithm, optimize, transform, sequential, reasoning, calculate\n\n**Extract Capability Requirements**:\n\nBased on `tools` in frontmatter and body instructions:\n\n- **Read-only tools** (search, fetch, usages, githubRepo): Lower complexity, faster models suitable\n- **Write operations** (edit/editFiles, new): Moderate complexity, accuracy important\n- **Execution tools** (runCommands, runTests, runTasks): Validation needs, iterative approach\n- **Advanced tools** (context7/\\*, sequential-thinking/\\*): Complex reasoning, premium models beneficial\n- **Multi-modal** (image analysis references): Requires vision-capable models\n\n### 2. Model Evaluation Phase\n\n**Apply Model Selection Criteria**:\n\nFor each available model, evaluate against these dimensions:\n\n#### Model Capabilities Matrix\n\n| Model                   | Multiplier | Speed    | Code Quality | Reasoning | Context | Vision | Best For                                          |\n| ----------------------- | ---------- | -------- | ------------ | --------- | ------- | ------ | ------------------------------------------------- |\n| GPT-4.1                 | 0x         | Fast     | Good         | Good      | 128K    | ✅     | Balanced general tasks, included in all plans     |\n| GPT-5 mini              | 0x         | Fastest  | Good         | Basic     | 128K    | ❌     | Simple tasks, quick responses, cost-effective     |\n| GPT-5                   | 1x         | Moderate | Excellent    | Advanced  | 128K    | ✅     | Complex code, advanced reasoning, multi-turn chat |\n| GPT-5 Codex             | 1x         | Fast     | Excellent    | Good      | 128K    | ❌     | Code optimization, refactoring, algorithmic tasks |\n| Claude Sonnet 3.5       | 1x         | Moderate | Excellent    | Excellent | 200K    | ✅     | Code generation, long context, balanced reasoning |\n| Claude Sonnet 4         | 1x         | Moderate | Excellent    | Advanced  | 200K    | ❌     | Complex code, robust reasoning, enterprise tasks  |\n| Claude Sonnet 4.5       | 1x         | Moderate | Excellent    | Expert    | 200K    | ✅     | Advanced code, architecture, design patterns      |\n| Claude Opus 4.1         | 10x        | Slow     | Outstanding  | Expert    | 1M      | ✅     | Large codebases, architectural review, research   |\n| Gemini 2.5 Pro          | 1x         | Moderate | Excellent    | Advanced  | 2M      | ✅     | Very long context, multi-modal, real-time data    |\n| Gemini 2.0 Flash (dep.) | 0.25x      | Fastest  | Good         | Good      | 1M      | ❌     | Fast responses, cost-effective (deprecated)       |\n| Grok Code Fast 1        | 0.25x      | Fastest  | Good         | Basic     | 128K    | ❌     | Speed-critical simple tasks, preview (free)       |\n| o3 (deprecated)         | 1x         | Slow     | Good         | Expert    | 128K    | ❌     | Advanced reasoning, algorithmic optimization      |\n| o4-mini (deprecated)    | 0.33x      | Fast     | Good         | Good      | 128K    | ❌     | Reasoning at lower cost (deprecated)              |\n\n#### Selection Decision Tree\n\n```\nSTART\n  │\n  ├─ Task Complexity?\n  │   ├─ Simple/Repetitive → GPT-5 mini, Grok Code Fast 1, GPT-4.1\n  │   ├─ Moderate → GPT-4.1, Claude Sonnet 4, GPT-5\n  │   └─ Complex/Advanced → Claude Sonnet 4.5, GPT-5, Gemini 2.5 Pro, Claude Opus 4.1\n  │\n  ├─ Reasoning Depth?\n  │   ├─ Basic → GPT-5 mini, Grok Code Fast 1\n  │   ├─ Intermediate → GPT-4.1, Claude Sonnet 4\n  │   ├─ Advanced → GPT-5, Claude Sonnet 4.5\n  │   └─ Expert → Claude Opus 4.1, o3 (deprecated)\n  │\n  ├─ Code-Specific?\n  │   ├─ Yes → GPT-5 Codex, Claude Sonnet 4.5, GPT-5\n  │   └─ No → GPT-5, Claude Sonnet 4\n  │\n  ├─ Context Size?\n  │   ├─ Small (<50K tokens) → Any model\n  │   ├─ Medium (50-200K) → Claude models, GPT-5, Gemini\n  │   ├─ Large (200K-1M) → Gemini 2.5 Pro, Claude Opus 4.1\n  │   └─ Very Large (>1M) → Gemini 2.5 Pro (2M), Claude Opus 4.1 (1M)\n  │\n  ├─ Vision Required?\n  │   ├─ Yes → GPT-4.1, GPT-5, Claude Sonnet 3.5/4.5, Gemini 2.5 Pro, Claude Opus 4.1\n  │   └─ No → All models\n  │\n  ├─ Cost Sensitivity? (based on subscriptionTier)\n  │   ├─ Free Tier → 0x models only: GPT-4.1, GPT-5 mini, Grok Code Fast 1\n  │   ├─ Pro (1000 premium/month) → Prioritize 0x, use 1x judiciously, avoid 10x\n  │   └─ Pro+ (5000 premium/month) → 1x freely, 10x for critical tasks\n  │\n  └─ Priority Factor?\n      ├─ Speed → GPT-5 mini, Grok Code Fast 1, Gemini 2.0 Flash\n      ├─ Cost → 0x models (GPT-4.1, GPT-5 mini) or lower multipliers (0.25x, 0.33x)\n      ├─ Quality → Claude Sonnet 4.5, GPT-5, Claude Opus 4.1\n      └─ Balanced → GPT-4.1, Claude Sonnet 4, GPT-5\n```\n\n### 3. Recommendation Generation Phase\n\n**Primary Recommendation**:\n\n- Identify the single best model based on task analysis and decision tree\n- Provide specific rationale tied to file content characteristics\n- Explain multiplier cost implications for user's subscription tier\n\n**Alternative Recommendations**:\n\n- Suggest 1-2 alternative models with trade-off explanations\n- Include scenarios where alternatives might be preferred\n- Consider priority factor overrides (speed vs. quality vs. cost)\n\n**Auto-Selection Guidance**:\n\n- Assess if task is suitable for auto model selection (excludes premium models > 1x)\n- Explain when manual selection is beneficial vs. letting Copilot choose\n- Note any limitations of auto-selection for the specific task\n\n**Deprecation Warnings**:\n\n- Flag if file currently specifies a deprecated model (o3, o4-mini, Claude Sonnet 3.7, Gemini 2.0 Flash)\n- Provide migration path to recommended replacement\n- Include timeline for deprecation (e.g., \"o3 deprecating 2025-10-23\")\n\n**Subscription Tier Considerations**:\n\n- **Free Tier**: Recommend only 0x multiplier models (GPT-4.1, GPT-5 mini, Grok Code Fast 1)\n- **Pro Tier**: Balance between 0x (unlimited) and 1x (1000/month) models\n- **Pro+ Tier**: More freedom with 1x models (5000/month), justify 10x usage for exceptional cases\n\n### 4. Integration Recommendations\n\n**Frontmatter Update Guidance**:\n\nIf file does not specify a `model` field:\n\n```markdown\n## Recommendation: Add Model Specification\n\nCurrent frontmatter:\n\\`\\`\\`yaml\n\n---\n\ndescription: \"...\"\ntools: [...]\n\n---\n\n\\`\\`\\`\n\nRecommended frontmatter:\n\\`\\`\\`yaml\n\n---\n\ndescription: \"...\"\nmodel: \"[Recommended Model Name]\"\ntools: [...]\n\n---\n\n\\`\\`\\`\n\nRationale: [Explanation of why this model is optimal for this task]\n```\n\nIf file already specifies a model:\n\n```markdown\n## Current Model Assessment\n\nSpecified model: `[Current Model]` (Multiplier: [X]x)\n\nRecommendation: [Keep current model | Consider switching to [Recommended Model]]\n\nRationale: [Explanation]\n```\n\n**Tool Alignment Check**:\n\nVerify model capabilities align with specified tools:\n\n- If tools include `context7/*` or `sequential-thinking/*`: Recommend advanced reasoning models (Claude Sonnet 4.5, GPT-5, Claude Opus 4.1)\n- If tools include vision-related references: Ensure model supports images (flag if GPT-5 Codex, Claude Sonnet 4, or mini models selected)\n- If tools are read-only (search, fetch): Suggest cost-effective models (GPT-5 mini, Grok Code Fast 1)\n\n### 5. Context7 Integration for Up-to-Date Information\n\n**Leverage Context7 for Model Documentation**:\n\nWhen uncertainty exists about current model capabilities, use Context7 to fetch latest information:\n\n```markdown\n**Verification with Context7**:\n\nUsing `context7/get-library-docs` with library ID `/websites/github_en_copilot`:\n\n- Query topic: \"model capabilities [specific capability question]\"\n- Retrieve current model features, multipliers, deprecation status\n- Cross-reference against analyzed file requirements\n```\n\n**Example Context7 Usage**:\n\n```\nIf unsure whether Claude Sonnet 4.5 supports image analysis:\n→ Use context7 with topic \"Claude Sonnet 4.5 vision image capabilities\"\n→ Confirm feature support before recommending for multi-modal tasks\n```\n\n## Output Expectations\n\n### Report Structure\n\nGenerate a structured markdown report with the following sections:\n\n```markdown\n# AI Model Recommendation Report\n\n**File Analyzed**: `[file path]`\n**File Type**: [chatmode | prompt]\n**Analysis Date**: [YYYY-MM-DD]\n**Subscription Tier**: [Free | Pro | Pro+]\n\n---\n\n## File Summary\n\n**Description**: [from frontmatter]\n**Mode**: [ask | edit | agent]\n**Tools**: [tool list]\n**Current Model**: [specified model or \"Not specified\"]\n\n## Task Analysis\n\n### Task Complexity\n\n- **Level**: [Simple | Moderate | Complex | Advanced]\n- **Reasoning Depth**: [Basic | Intermediate | Advanced | Expert]\n- **Context Requirements**: [Small | Medium | Large | Very Large]\n- **Code Generation**: [Minimal | Moderate | Extensive]\n- **Multi-Modal**: [Yes | No]\n\n### Task Category\n\n[Primary category from 8 categories listed in Workflow Phase 1]\n\n### Key Characteristics\n\n- Characteristic 1: [explanation]\n- Characteristic 2: [explanation]\n- Characteristic 3: [explanation]\n\n## Model Recommendation\n\n### 🏆 Primary Recommendation: [Model Name]\n\n**Multiplier**: [X]x ([cost implications for subscription tier])\n**Strengths**:\n\n- Strength 1: [specific to task]\n- Strength 2: [specific to task]\n- Strength 3: [specific to task]\n\n**Rationale**:\n[Detailed explanation connecting task characteristics to model capabilities]\n\n**Cost Impact** (for [Subscription Tier]):\n\n- Per request multiplier: [X]x\n- Estimated usage: [rough estimate based on task frequency]\n- [Additional cost context]\n\n### 🔄 Alternative Options\n\n#### Option 1: [Model Name]\n\n- **Multiplier**: [X]x\n- **When to Use**: [specific scenarios]\n- **Trade-offs**: [compared to primary recommendation]\n\n#### Option 2: [Model Name]\n\n- **Multiplier**: [X]x\n- **When to Use**: [specific scenarios]\n- **Trade-offs**: [compared to primary recommendation]\n\n### 📊 Model Comparison for This Task\n\n| Criterion        | [Primary Model] | [Alternative 1] | [Alternative 2] |\n| ---------------- | --------------- | --------------- | --------------- |\n| Task Fit         | ⭐⭐⭐⭐⭐      | ⭐⭐⭐⭐        | ⭐⭐⭐          |\n| Code Quality     | [rating]        | [rating]        | [rating]        |\n| Reasoning        | [rating]        | [rating]        | [rating]        |\n| Speed            | [rating]        | [rating]        | [rating]        |\n| Cost Efficiency  | [rating]        | [rating]        | [rating]        |\n| Context Capacity | [capacity]      | [capacity]      | [capacity]      |\n| Vision Support   | [Yes/No]        | [Yes/No]        | [Yes/No]        |\n\n## Auto Model Selection Assessment\n\n**Suitability**: [Recommended | Not Recommended | Situational]\n\n[Explanation of whether auto-selection is appropriate for this task]\n\n**Rationale**:\n\n- [Reason 1]\n- [Reason 2]\n\n**Manual Override Scenarios**:\n\n- [Scenario where user should manually select model]\n- [Scenario where user should manually select model]\n\n## Implementation Guidance\n\n### Frontmatter Update\n\n[Provide specific code block showing recommended frontmatter change]\n\n### Model Selection in VS Code\n\n**To Use Recommended Model**:\n\n1. Open Copilot Chat\n2. Click model dropdown (currently shows \"[current model or Auto]\")\n3. Select **[Recommended Model Name]**\n4. [Optional: When to switch back to Auto]\n\n**Keyboard Shortcut**: `Cmd+Shift+P` → \"Copilot: Change Model\"\n\n### Tool Alignment Verification\n\n[Check results: Are specified tools compatible with recommended model?]\n\n✅ **Compatible Tools**: [list]\n⚠️ **Potential Limitations**: [list if any]\n\n## Deprecation Notices\n\n[If applicable, list any deprecated models in current configuration]\n\n⚠️ **Deprecated Model in Use**: [Model Name] (Deprecation date: [YYYY-MM-DD])\n\n**Migration Path**:\n\n- **Current**: [Deprecated Model]\n- **Replacement**: [Recommended Model]\n- **Action Required**: Update `model:` field in frontmatter by [date]\n- **Behavioral Changes**: [any expected differences]\n\n## Context7 Verification\n\n[If Context7 was used for verification]\n\n**Queries Executed**:\n\n- Topic: \"[query topic]\"\n- Library: `/websites/github_en_copilot`\n- Key Findings: [summary]\n\n## Additional Considerations\n\n### Subscription Tier Recommendations\n\n[Specific advice based on Free/Pro/Pro+ tier]\n\n### Priority Factor Adjustments\n\n[If user specified Speed/Cost/Quality/Balanced, explain how recommendation aligns]\n\n### Long-Term Model Strategy\n\n[Advice for when to re-evaluate model selection as file evolves]\n\n---\n\n## Quick Reference\n\n**TL;DR**: Use **[Primary Model]** for this task due to [one-sentence rationale]. Cost: [X]x multiplier.\n\n**One-Line Update**:\n\\`\\`\\`yaml\nmodel: \"[Recommended Model Name]\"\n\\`\\`\\`\n```\n\n### Output Quality Standards\n\n- **Specific**: Tie all recommendations directly to file content, not generic advice\n- **Actionable**: Provide exact frontmatter code, VS Code steps, clear migration paths\n- **Contextualized**: Consider subscription tier, priority factor, deprecation timelines\n- **Evidence-Based**: Reference model capabilities from Context7 documentation when available\n- **Balanced**: Present trade-offs honestly (speed vs. quality vs. cost)\n- **Up-to-Date**: Flag deprecated models, suggest current alternatives\n\n## Quality Assurance\n\n### Validation Steps\n\n- [ ] File successfully read and parsed\n- [ ] Frontmatter extracted correctly (or noted if missing)\n- [ ] Task complexity accurately categorized (Simple/Moderate/Complex/Advanced)\n- [ ] Primary task category identified from 8 options\n- [ ] Model recommendation aligns with decision tree logic\n- [ ] Multiplier cost explained for user's subscription tier\n- [ ] Alternative models provided with clear trade-off explanations\n- [ ] Auto-selection guidance included (recommended/not recommended/situational)\n- [ ] Deprecated model warnings included if applicable\n- [ ] Frontmatter update example provided (valid YAML)\n- [ ] Tool alignment verified (model capabilities match specified tools)\n- [ ] Context7 used when verification needed for latest model information\n- [ ] Report includes all required sections (summary, analysis, recommendation, implementation)\n\n### Success Criteria\n\n- Recommendation is justified by specific file characteristics\n- Cost impact is clear and appropriate for subscription tier\n- Alternative models cover different priority factors (speed vs. quality vs. cost)\n- Frontmatter update is ready to copy-paste (no placeholders)\n- User can immediately act on recommendation (clear steps)\n- Report is readable and scannable (good structure, tables, emoji markers)\n\n### Failure Triggers\n\n- File path is invalid or unreadable → Stop and request valid path\n- File is not `.agent.md` or `.prompt.md` → Stop and clarify file type\n- Cannot determine task complexity from content → Request more specific file or clarification\n- Model recommendation contradicts documented capabilities → Use Context7 to verify current info\n- Subscription tier is invalid (not Free/Pro/Pro+) → Default to Pro and note assumption\n\n## Advanced Use Cases\n\n### Analyzing Multiple Files\n\nIf user provides multiple files:\n\n1. Analyze each file individually\n2. Generate separate recommendations per file\n3. Provide summary table comparing recommendations\n4. Note any patterns (e.g., \"All debug-related modes benefit from Claude Sonnet 4.5\")\n\n### Comparative Analysis\n\nIf user asks \"Which model is better between X and Y for this file?\":\n\n1. Focus comparison on those two models only\n2. Use side-by-side table format\n3. Declare a winner with specific reasoning\n4. Include cost comparison for subscription tier\n\n### Migration Planning\n\nIf file specifies a deprecated model:\n\n1. Prioritize migration guidance in report\n2. Test current behavior expectations vs. replacement model capabilities\n3. Provide phased migration if breaking changes expected\n4. Include rollback plan if needed\n\n## Examples\n\n### Example 1: Simple Formatting Task\n\n**File**: `format-code.prompt.md`\n**Content**: \"Format Python code with Black style, add type hints\"\n**Recommendation**: GPT-5 mini (0x multiplier, fastest, sufficient for repetitive formatting)\n**Alternative**: Grok Code Fast 1 (0.25x, even faster, preview feature)\n**Rationale**: Task is simple and repetitive; premium reasoning not needed; speed prioritized\n\n### Example 2: Complex Architecture Review\n\n**File**: `architect.agent.md`\n**Content**: \"Review system design for scalability, security, maintainability; analyze trade-offs; provide ADR-level recommendations\"\n**Recommendation**: Claude Sonnet 4.5 (1x multiplier, expert reasoning, excellent for architecture)\n**Alternative**: Claude Opus 4.1 (10x, use for very large codebases >500K tokens)\n**Rationale**: Requires deep reasoning, architectural expertise, design pattern knowledge; Sonnet 4.5 excels at this\n\n### Example 3: Django Expert Mode\n\n**File**: `django.agent.md`\n**Content**: \"Django 5.x expert with ORM optimization, async views, REST API design; uses context7 for up-to-date Django docs\"\n**Recommendation**: GPT-5 (1x multiplier, advanced reasoning, excellent code quality)\n**Alternative**: Claude Sonnet 4.5 (1x, alternative perspective, strong with frameworks)\n**Rationale**: Domain expertise + context7 integration benefits from advanced reasoning; 1x cost justified for expert mode\n\n### Example 4: Free Tier User with Planning Mode\n\n**File**: `plan.agent.md`\n**Content**: \"Research and planning mode with read-only tools (search, fetch, githubRepo)\"\n**Subscription**: Free (2K completions + 50 chat requests/month, 0x models only)\n**Recommendation**: GPT-4.1 (0x, balanced, included in Free tier)\n**Alternative**: GPT-5 mini (0x, faster but less context)\n**Rationale**: Free tier restricted to 0x models; GPT-4.1 provides best balance of quality and context for planning tasks\n\n## Knowledge Base\n\n### Model Multiplier Cost Reference\n\n| Multiplier | Meaning                                          | Free Tier | Pro Usage | Pro+ Usage |\n| ---------- | ------------------------------------------------ | --------- | --------- | ---------- |\n| 0x         | Included in all plans, no premium count          | ✅        | Unlimited | Unlimited  |\n| 0.25x      | 4 requests = 1 premium request                   | ❌        | 4000 uses | 20000 uses |\n| 0.33x      | 3 requests = 1 premium request                   | ❌        | 3000 uses | 15000 uses |\n| 1x         | 1 request = 1 premium request                    | ❌        | 1000 uses | 5000 uses  |\n| 1.25x      | 1 request = 1.25 premium requests                | ❌        | 800 uses  | 4000 uses  |\n| 10x        | 1 request = 10 premium requests (very expensive) | ❌        | 100 uses  | 500 uses   |\n\n### Model Changelog & Deprecations (October 2025)\n\n**Deprecated Models** (Effective 2025-10-23):\n\n- ❌ o3 (1x) → Replace with GPT-5 or Claude Sonnet 4.5 for reasoning\n- ❌ o4-mini (0.33x) → Replace with GPT-5 mini (0x) for cost, GPT-5 (1x) for quality\n- ❌ Claude Sonnet 3.7 (1x) → Replace with Claude Sonnet 4 or 4.5\n- ❌ Claude Sonnet 3.7 Thinking (1.25x) → Replace with Claude Sonnet 4.5\n- ❌ Gemini 2.0 Flash (0.25x) → Replace with Grok Code Fast 1 (0.25x) or GPT-5 mini (0x)\n\n**Preview Models** (Subject to Change):\n\n- 🧪 Claude Sonnet 4.5 (1x) - Preview status, may have API changes\n- 🧪 Grok Code Fast 1 (0.25x) - Preview, free during preview period\n\n**Stable Production Models**:\n\n- ✅ GPT-4.1, GPT-5, GPT-5 mini, GPT-5 Codex (OpenAI)\n- ✅ Claude Sonnet 3.5, Claude Sonnet 4, Claude Opus 4.1 (Anthropic)\n- ✅ Gemini 2.5 Pro (Google)\n\n### Auto Model Selection Behavior (Sept 2025+)\n\n**Included in Auto Selection**:\n\n- GPT-4.1 (0x)\n- GPT-5 mini (0x)\n- GPT-5 (1x)\n- Claude Sonnet 3.5 (1x)\n- Claude Sonnet 4.5 (1x)\n\n**Excluded from Auto Selection**:\n\n- Models with multiplier > 1 (Claude Opus 4.1, deprecated o3)\n- Models blocked by admin policies\n- Models unavailable in subscription plan (1x models in Free tier)\n\n**When Auto Selects**:\n\n- Copilot analyzes prompt complexity, context size, task type\n- Chooses from eligible pool based on availability and rate limits\n- Applies 10% multiplier discount on auto-selected models\n- Shows selected model on hover over response in Chat view\n\n## Context7 Query Templates\n\nUse these query patterns when verification needed:\n\n**Model Capabilities**:\n\n```\nTopic: \"[Model Name] code generation quality capabilities\"\nLibrary: /websites/github_en_copilot\n```\n\n**Model Multipliers**:\n\n```\nTopic: \"[Model Name] request multiplier cost billing\"\nLibrary: /websites/github_en_copilot\n```\n\n**Deprecation Status**:\n\n```\nTopic: \"deprecated models October 2025 timeline\"\nLibrary: /websites/github_en_copilot\n```\n\n**Vision Support**:\n\n```\nTopic: \"[Model Name] image vision multimodal support\"\nLibrary: /websites/github_en_copilot\n```\n\n**Auto Selection**:\n\n```\nTopic: \"auto model selection behavior eligible models\"\nLibrary: /websites/github_en_copilot\n```\n\n---\n\n**Last Updated**: 2025-10-28\n**Model Data Current As Of**: October 2025\n**Deprecation Deadline**: 2025-10-23 for o3, o4-mini, Claude Sonnet 3.7 variants, Gemini 2.0 Flash","tags":["model","recommendation","awesome","copilot","github"],"capabilities":["skill","source-github","category-awesome-copilot"],"categories":["awesome-copilot"],"synonyms":[],"warnings":[],"endpointUrl":"https://skills.sh/github/awesome-copilot/model-recommendation","protocol":"skill","transport":"skills-sh","auth":{"type":"none","details":{"install_from":"skills.sh"}},"qualityScore":"0.300","qualityRationale":"deterministic score 0.30 from registry signals: · indexed on skills.sh · published under github/awesome-copilot","verified":false,"liveness":"unknown","lastLivenessCheck":null,"agentReviews":{"count":0,"score_avg":null,"cost_usd_avg":null,"success_rate":null,"latency_p50_ms":null,"narrative_summary":null,"summary_updated_at":null},"enrichmentModel":"deterministic:skill:v1","enrichmentVersion":1,"enrichedAt":"2026-04-22T11:40:19.775Z","embedding":null,"createdAt":"2026-04-18T20:26:04.240Z","updatedAt":"2026-04-22T11:40:19.775Z","lastSeenAt":"2026-04-22T11:40:19.775Z","tsv":"'-10':1157,2678,2967,2979 '-2':1061 '-200':891 '-23':1158,2679,2980 '-28':2968 '-4.1':74,125,613,815,818,848,923,950,994,1016,1170,2555,2579,2780,2815 '-5':76,78,81,127,130,627,642,657,808,823,829,840,854,869,875,878,896,925,952,981,996,1010,1021,1172,1300,1318,1341,2363,2487,2564,2685,2700,2706,2747,2782,2784,2787,2818,2822 '/4.5':929 '/websites/github_en_copilot':1383,1867,2920,2931,2941,2952,2963 '0.25':745,761,1001,2377,2614,2735,2743,2769 '0.33':789,1003,2625,2695 '0x':152,157,162,614,629,946,962,991,1166,1182,2365,2550,2556,2566,2576,2604,2702,2749,2816,2820 '1':104,140,214,285,760,813,845,957,986,1060,1177,1346,1536,1540,1564,1611,1657,1712,1753,2228,2276,2314,2345,2376,2618,2629,2637,2639,2648,2658,2742,2768,2839 '1.25':2646,2650,2725 '10':2660,2882 '100':2665 '1000':158,959,2642 '1000/month':1186 '10x':713,967,973,1197,2434,2657 '128k':618,633,647,663,766,780,794 '15000':2634 '1m':717,750,901,910,918 '1x':643,659,672,686,700,726,776,964,971,1101,1185,1193,2423,2488,2499,2514,2636,2681,2707,2713,2758,2823,2827,2831,2855 '2':314,583,1543,1569,1630,1659,1714,1757,2233,2284,2320,2396 '2.0':99,742,988,1141,2733,2991 '2.5':96,724,831,903,912,931,2801 '20000':2623 '200k':676,690,704,900 '200k-1m':899 '2025':121,1156,2673,2677,2809,2938,2966,2975,2978 '2k':147,2545 '2m':730,914 '3':340,1022,1546,1574,1767,2239,2292,2329,2457,2627 '3.5':85,133,671,928,2792,2826 '3.7':1139,2712,2723,2988 '3000':2632 '4':88,372,685,821,851,881,1019,1202,1322,1772,2245,2299,2337,2521,2616,2718,2795 '4.1':94,712,835,861,907,917,935,1013,1303,2433,2798,2842 '4.5':91,136,699,827,857,873,1008,1298,1413,1423,2259,2422,2452,2498,2689,2720,2731,2757,2830 '4000':2621,2655 '5':404,1347,2465 '50':149,890,2547 '500':2667 '5000':163,969,2644 '5000/month':1195 '500k':2440 '50k':885 '6':432 '7':461 '8':489,1530,2030 '800':2653 'absolut':175 'access':467,483 'accur':2022 'accuraci':550 'act':2143 'action':1839,1953 'ad':293 'add':310,1218,2358 'addit':1605,1871 'adjust':1884 'admin':2848 'adr':413,2416 'adr-level':2415 'advanc':490,507,561,646,650,689,705,729,781,852,1293,1501,1506,2217,2490,2512 'advic':1877,1898,1952 'agent':1482 'agent.md':19,67,171,225,2174 'ai':8,36,1451 'algorithm':497,514,667,783 'align':1275,1280,1789,1892,2034,2076 'alreadi':1248 'altern':1057,1062,1072,1608,1656,1658,2003,2047,2119,2372,2430,2495,2500,2562 'analysi':216,265,284,363,381,386,412,436,439,576,1036,1416,1463,1494,2098,2261 'analyz':18,185,237,428,456,1402,1456,2220,2229,2410,2864 'anthrop':2799 'api':323,471,486,2474,2763 'appli':587,2881 'applic':1811,2068 'approach':560 'appropri':1706,2115 'architect':365 'architect.agent.md':2401 'architectur':343,347,371,707,720,2398,2429,2446 'ask':1480,2264 'assess':431,1089,1255,1693 'assumpt':2216 'assur':2005 'async':2471 'auto':114,1086,1095,1117,1690,1703,1766,1779,2057,2804,2812,2834,2861,2887,2953,2956 'auto-select':113,1085,1116,1702,2056,2886 'avail':43,71,116,593,1982,2877 'avoid':966 'awesom':3 'back':1777 'balanc':203,209,212,619,681,1014,1180,1983,2557,2582 'base':47,281,523,941,1033,1601,1878,1974,2591,2875 'basic':295,309,632,765,838,1504 'basic/intermediate/advanced/expert':248 'behavior':1848,2323,2807,2959 'benefici':571,1107 'benefit':2255,2510 'best':442,610,1031,2581 'better':2268 'bill':2929 'black':2356 'block':1739,2846 'bodi':238,529 'break':2334 'bug':378 'build':338 'calcul':519 'cannot':2182 'capabl':30,263,521,581,600,1279,1367,1387,1389,1426,1586,1977,2079,2198,2328,2911,2918 'capac':1681,1682,1683,1684 'case':1201,2219 'categor':273,2023 'categori':280,1526,1528,1531,2027 'category-awesome-copilot' 'caus':385 'chang':1743,1786,1849,2335,2754,2764 'changelog':2670 'characterist':50,297,326,355,387,415,447,473,506,1047,1538,1539,1542,1545,1583,2109 'chat':13,655,1756,2548,2898 'chat/month':150 'chatmod':1461 'check':446,1276,1791 'choos':1111,2871 'clarif':2193 'clarifi':2179 'claud':83,86,89,92,131,134,669,683,697,710,819,825,833,849,855,859,871,879,893,905,915,926,933,1006,1011,1017,1137,1296,1301,1320,1411,1421,2257,2420,2431,2496,2687,2710,2716,2721,2729,2755,2790,2793,2796,2824,2828,2840,2986 'clear':1961,2051,2113,2146 'click':1758 'cmd':1782 'code':102,119,249,315,332,433,605,649,664,677,692,706,758,811,843,865,955,984,1175,1344,1515,1662,1738,1748,1957,1959,2354,2374,2493,2740,2766,2915 'code-specif':864 'codebas':719,2439 'codex':82,658,870,1319,2788 'comment':307 'comments/docstrings':294 'compar':1625,1644,2243,2260 'comparison':1649,2278,2302 'compat':1796,1800 'complet':148,2546 'complex':27,243,328,341,499,540,549,567,648,691,805,1496,1500,2021,2185,2397,2866 'complex/advanced':824 'complianc':445,459,480 'compon':359 'configur':1818 'confirm':1427 'connect':1581 'consid':1076,1267,1965 'consider':1161,1872 'content':239,283,1046,1949,2187,2351,2402,2463,2530 'context':141,258,268,301,394,419,608,680,733,882,1508,1607,1680,2570,2586,2867 'context7':563,1287,1348,1357,1369,1377,1406,1418,1853,1856,1979,2083,2200,2477,2508,2900 'context7/get-library-docs':1379 'contextu':1964 'contradict':2196 'convent':478 'convers':256 'copi':2136 'copilot':4,12,41,143,192,1110,1755,1785,2863 'copy-past':2135 'correct':2015 'cost':54,207,639,754,798,939,990,1050,1084,1337,1557,1587,1606,1675,1926,1993,2040,2110,2129,2301,2515,2594,2704,2928 'cost-effect':638,753,1336 'cost-effici':53 'count':2611 'cover':2121 'creat':335 'creation':414 'criteria':590,2102 'criterion':1653 'critic':448,769,975 'cross':1399 'cross-refer':1398 'crud':296 'current':1128,1221,1253,1258,1265,1365,1392,1486,1761,1763,1817,1833,2002,2203,2322,2971 'data':272,500,740,2970 'date':111,1354,1464,1826,1847,1997,2482 'dd':1468,1830 'deadlin':2977 'debug':373,393,398,2252 'debug-rel':2251 'decis':422,801,1038,2036 'decision-mak':421 'declar':2293 'deep':356,474,2444 'default':198,210,2211 'dep':744 'deprec':110,756,775,788,799,863,1123,1131,1152,1155,1396,1808,1814,1819,1825,1834,1970,1999,2063,2312,2671,2674,2843,2932,2935,2976 'depth':247,837,1503 'descript':231,1224,1229,1476 'design':346,368,472,708,2405,2448,2475 'detail':1579 'determin':2183 'diagnos':401 'differ':1852,2122 'dimens':598 'direct':1946 'discount':2884 'django':482,2458,2464,2483 'django.agent.md':2462 'django/framework-specific':466 'doc':2484 'docstr':311 'document':411,430,1360,1980,2197 'domain':329,452,463,475,2506 'dr':1913 'dropdown':1760 'due':1920 'e.g':1153,2249 'edit':1481 'edit/editfiles':546 'effect':640,755,1338,2676 'effici':55,1676 'elig':2873,2960 'emoji':2156 'endpoint':324 'ensur':1311 'enterpris':695 'error':380,402 'estim':1597,1600 'evalu':585,595,1904 'even':2379 'evid':1973 'evidence-bas':1972 'evolv':1909 'exact':1955 'exampl':1405,2071,2343,2344,2395,2456,2520 'excel':645,661,674,675,688,702,728,2427,2453,2492 'except':1200 'exclud':137,1098,2832 'execut':303,552,1862 'exist':1363 'expect':1438,1851,2324,2336 'expens':2664 'expert':703,716,779,858,1507,2425,2459,2467,2518 'expertis':453,2447,2507 'explain':1048,1102,1889,2041 'explan':1068,1236,1273,1541,1544,1547,1580,1699,2055 'extens':1519 'extract':229,520,2014 'factor':978,1078,1883,1969,2124 'failur':2158 'fast':103,302,615,660,751,759,791,812,844,956,985,1176,1345,2375,2741,2767 'faster':541,2380,2567 'fastest':630,747,763,2367 'featur':322,408,1394,1428,2382 'fetch':536,1334,1371,2541 'field':1215,1843 'file':22,70,174,183,215,221,228,1045,1127,1209,1247,1403,1455,1457,1459,1474,1908,1948,2008,2108,2160,2171,2180,2191,2222,2227,2231,2238,2275,2309,2349,2400,2461,2528 'filepath':168 'find':1869 'fit':1661 'fix':379,399 'flag':1125,1315,1998 'flash':100,743,989,1142,2734,2992 'focus':2277 'follow':1448 'format':290,306,2291,2347,2352,2371 'format-code.prompt.md':2350 'framework':477,2504 'free':146,195,773,944,1162,1471,2522,2544,2560,2572,2598,2772,2858 'free/pro/pro':1880,2210 'freedom':1191 'freeli':972 'frequenc':1604 'frontmatt':230,527,1205,1222,1227,1478,1734,1742,1845,1956,2013,2069,2130 'functions/classes':320 'gather':420 'gemini':95,98,723,741,830,897,902,911,930,987,1140,2732,2800,2990 'general':620 'generat':250,316,336,678,1024,1441,1516,2234,2916 'generic':1951 'github':7,40,142 'githubrepo':538,2542 'good':616,617,631,662,748,749,764,778,792,793,2153 'googl':2803 'gpt':73,75,77,80,124,126,129,612,626,641,656,807,814,817,822,828,839,847,853,868,874,877,895,922,924,949,951,980,993,995,1009,1015,1020,1169,1171,1299,1317,1340,2362,2486,2554,2563,2578,2684,2699,2705,2746,2779,2781,2783,2786,2814,2817,2821 'grok':101,757,810,842,954,983,1174,1343,2373,2739,2765 'guidanc':1088,1207,1733,2059,2317 'hint':2360 'honest':1988 'hover':2894 'id':1382 'identifi':241,276,1028,2028 'idiomat':331 'imag':264,575,1314,1415,1425,2947 'immedi':2142 'impact':1588,2111 'implement':317,321,334,1732,2100 'implic':1051,1558 'import':551 'includ':622,1069,1149,1286,1306,2060,2066,2093,2300,2338,2558,2605,2810 'individu':2232 'info':2204 'inform':1355,1373,2091 'input':63,165,167,187,201 'instruct':530 'integr':1203,1349,2509 'intermedi':846,1505 'invalid':2163,2208 'investig':403,429 'iter':559 'judici':965 'justifi':1196,2105,2516 'k':892 'keep':1264 'key':1537,1868 'keyboard':1780 'keyword':305,333,364,397,425,454,481,513 'knowledg':330,476,2450,2590 'larg':350,718,898,909,1512,1514,2438 'large-scal':349 'last':2964 'latest':1372,2089 'less':2569 'let':1109 'level':1497,2417 'leverag':1356 'librari':1381,1866,2919,2930,2940,2951,2962 'limit':1114,1804,2880 'line':1932 'list':1485,1532,1802,1805,1812 'logic':299,512,2038 'long':267,679,732,1894 'long-context':266 'long-term':1893 'lower':539,797,999 'maintain':2409 'make':423 'manual':1104,1715,1722,1729 'markdown':1216,1252,1374,1444,1450 'marker':2157 'match':2080 'mathematical/algorithmic':509 'matrix':601 'may':2761 'mean':2597 'medium':889,1511 'might':1073 'migrat':1144,1831,1962,2306,2316,2332 'mini':79,108,128,628,787,809,841,953,982,997,1136,1173,1324,1342,2364,2565,2694,2701,2748,2785,2819,2985 'minim':300,1517 'minimal/moderate/extensive':252 'miss':2019 'mission':17 'mm':1467,1829 'modal':574,736,1435,1522 'mode':14,232,1479,2254,2460,2519,2527,2534 'model':1,9,37,51,72,112,153,234,542,570,582,584,588,594,599,602,888,894,938,947,992,1032,1063,1096,1100,1132,1168,1187,1194,1214,1219,1230,1232,1240,1251,1254,1257,1259,1266,1271,1278,1295,1312,1325,1339,1359,1366,1386,1393,1452,1487,1489,1548,1552,1585,1612,1631,1648,1655,1691,1724,1731,1744,1752,1759,1764,1770,1787,1799,1815,1820,1823,1835,1838,1842,1896,1905,1916,1935,1937,1976,2000,2032,2048,2064,2078,2090,2120,2194,2266,2282,2313,2327,2551,2577,2592,2669,2675,2751,2778,2805,2836,2845,2850,2856,2889,2892,2910,2913,2921,2924,2936,2945,2957,2961,2969 'moder':327,548,644,673,687,701,727,816,1499,1518 'move':313 'multi':254,493,503,573,653,735,1434,1521 'multi-mod':572,734,1433,1520 'multi-phas':502 'multi-step':492 'multi-turn':253,652 'multimod':2949 'multipl':358,2221,2226 'multipli':139,603,1000,1049,1167,1260,1395,1554,1594,1614,1633,1929,2039,2366,2424,2489,2593,2596,2838,2883,2922,2927 'name':1233,1553,1613,1632,1771,1824,1938,2914,2925,2946 'need':251,260,396,558,2087,2342,2392,2909 'new':547 'note':1112,2017,2215,2246 'notic':1809 'o3':105,774,862,1133,1154,2680,2844,2982 'o4':107,786,1135,2693,2984 'o4-mini':106,785,1134,2692,2983 'octob':2672,2937,2974 'off':60,1624,1643,1987,2413 'one':1923,1931 'one-lin':1930 'one-sent':1922 'open':1754 'openai':2789 'oper':545 'optim':204,354,367,498,515,665,784,1242,2470 'option':44,186,1609,1610,1629,1773,2031 'opus':93,711,834,860,906,916,934,1012,1302,2432,2797,2841 'orm':2469 'output':1437,1939 'outstand':715 'overrid':1079,1716 'p':1784 'pars':220,2012 'past':2137 'path':64,169,180,1145,1458,1832,1963,2161,2170 'pattern':289,318,344,377,407,437,450,465,496,709,2248,2449,2906 'per':1592,2237 'perform':57,353,440,458 'period':2775 'perspect':2501 'phase':217,504,586,1025,1535,2331 'placehold':2139 'plan':405,409,426,625,2307,2340,2526,2533,2588,2608,2854 'plan.agent.md':2529 'polici':2849 'pool':2874 'potenti':1803 'practic':443 'precondit':62 'prefer':304,1075 'premium':138,569,1099,2389,2610,2619,2630,2640,2651,2661 'premium/month':159,164,960,970 'present':1984 'preview':772,2381,2750,2759,2771,2774 'primari':278,1026,1527,1550,1627,1646,1654,1915,2025 'priorit':961,2315,2394 'prioriti':205,977,1077,1882,1968,2123 'priorityfactor':202 'pro':97,155,160,189,196,197,200,725,832,904,913,932,958,968,1178,1188,1472,1473,2213,2600,2602,2802 'problem':375 'problem-solv':374 'product':2777 'prompt':16,1462,2865 'prompt.md':21,69,173,227,2176 'provid':45,1040,1143,1736,1954,2049,2072,2225,2240,2330,2414,2580 'purpos':26 'python':2353 'qualiti':208,435,606,1005,1082,1663,1940,1991,2004,2127,2494,2584,2709,2917 'queri':1384,1861,1864,2901,2905 'question':1390 'quick':636,1910 'rate':1664,1665,1666,1668,1669,1670,1672,1673,1674,1677,1678,1679,2879 'rational':46,1042,1235,1272,1578,1710,1925,2383,2442,2505,2571 're':1903 're-evalu':1902 'read':218,222,417,532,1331,2010,2537 'read-on':416,531,1330,2536 'readabl':2150 'readi':2133 'real':270,738 'real-tim':269,737 'reason':246,357,392,491,508,518,568,607,651,682,694,782,795,836,1294,1502,1667,1711,1713,2298,2390,2426,2445,2491,2513,2691 'recognit':451 'recommend':2,10,32,1023,1027,1058,1147,1164,1204,1217,1226,1231,1263,1270,1292,1431,1453,1549,1551,1628,1647,1695,1697,1741,1751,1769,1798,1837,1875,1891,1936,1945,2033,2099,2103,2145,2195,2236,2244,2361,2418,2419,2485,2553 'recommended/not':2061 'recommended/situational':2062 'refactor':292,342,352,366,666 'refer':577,1310,1400,1911,1975,2595 'relat':179,1309,2253 'renam':312 'repetit':287,2370,2388 'replac':1148,1836,2326,2682,2697,2714,2727,2737 'report':1439,1445,1454,2092,2148,2319 'request':1593,2168,2188,2617,2620,2628,2631,2638,2641,2649,2652,2659,2662,2926 'requests/month':2549 'requir':29,166,245,257,522,578,920,1404,1509,1840,2095,2443 'research':406,410,427,722,2531 'respons':637,752,2896 'rest':485,2473 'restrict':2574 'result':1792 'retriev':1391 'review':348,370,434,441,455,721,2399,2403 'robust':693 'rollback':2339 'root':384 'rough':1599 'runcommand':554 'runtask':556 'runtest':555 'scaffold':339 'scalabl':2407 'scale':351,369 'scannabl':2152 'scenario':1070,1621,1640,1717,1718,1725 'scope':61 'search':535,1333,2540 'section':1449,2096 'secur':438,457,2408 'select':115,122,589,800,1087,1097,1105,1118,1326,1692,1704,1723,1730,1745,1768,1906,2058,2806,2813,2835,2862,2888,2891,2954,2958 'sensit':940 'sentenc':1924 'separ':2235 'sept':120,2808 'sequenti':511,517,565,1290 'sequential-think':564,1289 'shift':1783 'shortcut':1781 'show':1740,1762,2890 'side':2287,2289 'side-by-sid':2286 'simpl':286,291,308,634,770,1498,2346,2386 'simple/moderate/complex/advanced':244,2024 'simple/repetitive':806 'singl':1030 'situat':1698 'size':883,2868 'skill':5 'slow':714,777 'small':884,1510 'small/medium/large':261 'solv':376 'sonnet':84,87,90,132,135,670,684,698,820,826,850,856,872,880,927,1007,1018,1138,1297,1321,1412,1422,2258,2421,2451,2497,2688,2711,2717,2722,2730,2756,2791,2794,2825,2829,2987 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