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:

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

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

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

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

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

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

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

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

Model

Multiplier

Speed

Code Quality

Reasoning

Context

Vision

Best For

GPT-4.1

0x

Fast

Good

128K

✅

Balanced general tasks, included in all plans

GPT-5 mini

0x

Fastest

Good

Basic

128K

❌

Simple tasks, quick responses, cost-effective

GPT-5

1x

Moderate

Excellent

Advanced

128K

✅

Complex code, advanced reasoning, multi-turn chat

GPT-5 Codex

1x

Fast

Excellent

Good

128K

❌

Code optimization, refactoring, algorithmic tasks

Claude Sonnet 3.5

1x

Moderate

Excellent

200K

✅

Code generation, long context, balanced reasoning

Claude Sonnet 4

1x

Moderate

Excellent

Advanced

200K

❌

Complex code, robust reasoning, enterprise tasks

Claude Sonnet 4.5

1x

Moderate

Excellent

Expert

200K

✅

Advanced code, architecture, design patterns

Claude Opus 4.1

10x

Slow

Outstanding

Expert

1M

✅

Large codebases, architectural review, research

Gemini 2.5 Pro

1x

Moderate

Excellent

Advanced

2M

✅

Very long context, multi-modal, real-time data

Gemini 2.0 Flash (dep.)

0.25x

Fastest

Good

1M

❌

Fast responses, cost-effective (deprecated)

Grok Code Fast 1

0.25x

Fastest

Good

Basic

128K

❌

Speed-critical simple tasks, preview (free)

o3 (deprecated)

1x

Slow

Good

Expert

128K

❌

Advanced reasoning, algorithmic optimization

o4-mini (deprecated)

0.33x

Fast

Good

128K

❌

Reasoning at lower cost (deprecated)

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]

|

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

.agent.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:

Analyze each file individually

Generate separate recommendations per file

Provide summary table comparing recommendations

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?":

Focus comparison on those two models only

Use side-by-side table format

Declare a winner with specific reasoning

Include cost comparison for subscription tier

Migration Planning

If file specifies a deprecated model:

Prioritize migration guidance in report

Test current behavior expectations vs. replacement model capabilities

Provide phased migration if breaking changes expected

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.agent.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.agent.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.agent.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

Multiplier

Meaning

Free Tier

Pro Usage

Pro+ Usage

0x

Included in all plans, no premium count

✅

Unlimited

0.25x

4 requests = 1 premium request

❌

4000 uses

20000 uses

0.33x

3 requests = 1 premium request

❌

3000 uses

15000 uses

1x

1 request = 1 premium request

❌

1000 uses

5000 uses

1.25x

1 request = 1.25 premium requests

❌

800 uses

4000 uses

10x

1 request = 10 premium requests (very expensive)

❌

100 uses

500 uses

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"