24 - Multiple Models in Copilot: GPT-5, Claude Opus 4.7, Gemini 3.1

Multiple Models in Copilot: GPT-5, Claude Opus 4.7, Gemini 3.1

GitHub Copilot in 2026 is no longer a single integrated AI model. It became a model marketplace where developers and teams can choose the best one for each task. From code rewriting to agent refactoring, From rapid debugging to in-depth architectural analysis, each model excels in a specific role.

In this twenty-fourth article of the series, we will explore in depth the Copilot's multi-model system in 2026: the release history, the Premium Requests system with multipliers per model, how to choose the right model, and Auto Model Selection which automatically selects the most efficient model for the task. We'll find out when it's worth it really spend 10x premium tokens for Claude Opus 4.7 and when free GPT-5 mini is more than enough.

1. Complete Model Timeline in Copilot (January 2025 - April 2026)

Model availability on GitHub Copilot has grown rapidly over the last 15 months. We follow the exact history of releases, deprecations and promotions to GA (General Availability).

OpenAI models

Anthropic Models (Claude)

Google Models (Gemini)

2. Premium Requests and Multipliers system

Not all models in Copilot consume the same number of tokens. The system of Premium Requests introduces a multiplier per model that determines how many standard request equivalents they are deducted from your monthly allotment.

How the Multiplier Works

Each request to your chosen model is counted as 1.0 × moltiplicatore requests. For example, a request to Claude Opus 4.7 with 7.5x multiplier counts as 7.5 requests on your allotment. A request to GPT-5 mini with 0x multiplier counts as 0 requests (completely free, does not consume allotments).

Scenario: Piano Pro (300 richieste/mese)

1. Usi GPT-5 mini (0x) - 5 richieste per refactor = 0 richieste consumate
2. Usi Claude Opus 4.7 (7.5x) - 1 code review approfondita = 7.5 richieste consumate
3. Usi GPT-5.3-Codex (0x) - 10 autocompletioni = 0 richieste consumate
4. Usi Claude Opus 4.7 (7.5x) - 1 architectural analysis = 7.5 richieste consumate

Totale consumato: 0 + 7.5 + 0 + 7.5 = 15 richieste su 300 disponibili

Rimanenti: 300 - 15 = 285 richieste per il resto del mese

Complete Table of Multipliers by Model (April 2026)

3. Allotment Mensile Premium Requests per Piano

GitHub Copilot offre quattro tier di pricing, ognuno con un allotment mensile diverso di richieste premium. Il timing e il ciclo di reset variano a livello di organizzazione.

Real Calculation: How Many Days Do Premium Requests Last?

It completely depends on your usage pattern. Let's look at three real scenarios:

4. Auto Model Selection: Automatic Routing (November 2025)

In November 2025, GitHub introduced Auto Model Selection - a feature that analyzes your prompt and automatically chooses the most efficient and suitable model for that specific task. Available in JetBrains, Xcode, Eclipse, and VS Code (preview).

How It Works

When you enable Auto Model Selection, Copilot examines your prompt and assigns a model following this logic:

When to Enable Auto vs. Manual Selection

Enabling Auto Model Selection

"github.copilot.codeCompletions.enabled": true,
"github.copilot.chat.autoModelSelection": true,
"github.copilot.chat.preferredModel": "auto"  // Forza routing automatico

Copilot Chat Settings:
  ✓ Enable Auto Model Selection
  ✓ Prefer Cost-Efficient Models (per PM Business)
  Model Priority: Cost < Quality < Speed (è il default)

5. Real Cases: Which Model to Use?

Case 1: In-Depth Code Review (500 lines Java Spring Boot)

Scenario: Complete review of a REST controller with transactional logic. You have to check security, performance, error handling, logging, concurrency.

Advise: Take advantage of the Claude Opus 4.7 promo at 7.5x until April 30, 2026. Afterwards, evaluate whether the quality justify the jump to 10x, or go back to Sonnet 4.1 (3x) accepting completeness trade-off.

Case 2: Scaffolding Standalone Angular Component

Scenario: New route with responsive form + API integration + i18n keys. Approximately 200 lines of expected code.

Advise: Use GPT-5.2-Codex (0x). It's free, specialized for code generation, and the boilerplate does not benefit from the profound reasoning of Claude Opus.

Case 3: Agentic Refactor - Migrate 50 Files from Promises to Async/Await

Scenario: Old JavaScript codebase. Batch migration on 50 files, with dependency updates and test fixtures. Requires multi-file orchestration, not just regex replace.

Advise: Use GPT-5.5 (0.5x). It was specifically designed for agentic multi-file refactoring. Minimum cost, maximum orchestration.

6. Plan Mode in JetBrains, Xcode, Eclipse (November 2025)

In November 2025, GitHub introduced Plan Mode - a mode that forces Copilot to generate an explicit plan before refactoring. Available in JetBrains Professional, Xcode (macOS 13+), and Eclipse (preview).

How to Activate Plan Mode

1. Apri Copilot Chat (⌘/Ctrl+Shift+K)
2. In fondo a sinistra: "Model Selection"
3. Attiva toggle "Plan Mode" (prima di scrivere il prompt)
4. Digita il tuo prompt:
   "Refactor i 50 file JavaScript da Promise a async/await"
5. Copilot genera un piano step-by-step, lo visualizza
6. Clicca "Execute Plan" per procedere

1. Apri Code Assistant panel (Cmd+Shift+A)
2. Menu in alto: "Copilot Settings"
3. Toggle "Intent-Driven Development"
4. Accanto al textarea, appare il badge "PLAN MODE"
5. Scrivi il tuo intent (non il prompt tecnico)
6. Es: "Make this function testable by extracting DB access"

Benefits of Plan Mode

• Transparency: See the plan before Copilot changes your files
• Quick Fix: If the plan is wrong, rewrite a line without waiting for execution
• Learning: I understand how Copilot intends to approach the task
• Safety: You don't risk catastrophic refactors on the wrong branch (Piano Mode forces pre-execution display)

Plan:
1. Creare file CacheService.java con metodi @Cacheable
2. Aggiornare UserService: annotare findById con @Cacheable(value="users")
3. Aggiornare RoleService: @Cacheable(value="roles")
4. Aggiornare PermissionService: @CacheEvict al salvataggio
5. Aggiornare application.yml: redis config, TTL 10min

Files affected: 5
Estimated refactoring effort: 45 min manual = 3 min Copilot
Confidence: HIGH

7. ROI Calculation: When Premium Requests Are Worth It

The most powerful model is not always the best. A simple ROI calculation can guide your choice:

ROI = (Tempo Risparmiato in USD + Difetti Evitati in USD) / Costo Premium Requests

Assunzioni:
- Tuo salary lordo: €60K/anno = €29/ora = €0.48/minuto
- Tempo fisso per code review manuale: 45 minuti
- Copilot time: 15 minuti
- Tempo risparmiato per richiesta: 30 minuti = €14.40

Scenario A: Claude Opus 4.7 @ 7.5x
- Costo per 1 richiesta: 7.5 premium requests
- Costo in €: 7.5 × (€20/300) = €0.50
- Tempo risparmiato: €14.40
- ROI: €14.40 / €0.50 = 28.8x ✅ Convenientissimo

Scenario B: Claude Opus 4.7 @ 10x (post-aprile)
- Costo per 1 richiesta: 10 premium requests
- Costo in €: 10 × (€20/300) = €0.67
- Tempo risparmiato: €14.40 (idem)
- ROI: €14.40 / €0.67 = 21.5x ✅ Ancora conveniente, ma 26% meno profittevole

Conclusion: As long as the premium model saves you time > 1 minute on a request, the ROI is always positive (salary €0.48/min > model cost €0.05-0.10/min). The only case where it is not convenient is the trivial boilerplate (scaffolding) where GPT-5 mini does the same job in 0.5 minutes = free.

8. Bring Your Own Key (BYOK) - Private Fine-Tuned Templates (Preview)

In November 2025, GitHub launched preview of BYOK (Bring Your Own Key) - the capacity to connect a fine-tuned proprietary model in Copilot. Ideal for enterprises with custom models.

Use Case: Bank Using Fine-Tuned Model on Compliance

A bank has fine-tuned a Claude Opus custom on 50K compliance documents, banking regulations, risk assessment. He wants his devs to use this model in Copilot without exposing the training data to third parties.

BYOK (Early Access) enablement

{
  "custom_models": {
    "compliance_advisor": {
      "endpoint": "https://copilot-custom.bank.internal/api/v1/chat",
      "auth_type": "mtls",
      "client_cert_path": "/var/secrets/bank-client.crt",
      "client_key_path": "/var/secrets/bank-client.key",
      "ca_cert_path": "/var/secrets/bank-ca.crt",
      "model_name": "claude-opus-4.7-compliance-ftv3",
      "display_name": "Compliance Assistant (Private)",
      "description": "Fine-tuned su regolazioni bancarie EU",
      "availability": "internal_only"  // Non disponibile a dev esterni
    }
  },
  "model_selection": {
    "default_models": [
      "gpt-5-mini",
      "claude-opus-4.7",
      "compliance_advisor"  // Custom model always available
    ],
    "internal_network_models_only": true  // Usa custom solo da VPN aziendale
  }
}

Note: BYOK is in preview (November 2025). General availability expected Q2 2026.

9. Best Practice: Monitoring and Cost Optimization

Monthly Monitoring of the Allotment

GitHub provides a graphical dashboard to monitor premium requests consumed. Login to: https://github.com/organizations/YOUR-ORG/settings/copilot/billing

Monthly Optimization Checklist

Conclusion: The Right Model for the Right Task

GitHub Copilot in 2026 is a model marketplace, not a single-model solution. True skill it is learning to choose or make the system choose (Auto Model Selection) the model that maximizes productivity and minimizes cost.

In the next article (25) we will delve deeper MCP (Model Context Protocol) - the open standard that allows Copilot to connect to external data sources (database, API, corporate file system) and operate with real context instead of synthetic embeddings. MCP is the game-changer for enterprises.