Skip to content

Model Selection & Pricing

← Back to Guide

This page exists because model advice in token-optimization discussions gets mixed together too easily. There are three different pricing views in play:

  1. GitHub Copilot public docs — model availability by plan, Auto behavior, and whatever relative pricing signals GitHub has published
  2. This repo's UBB framing — how to think about the June 1, 2026 usage-based billing shift for Business and Enterprise: AI-credit metering and budgets by enterprise, org, cost center, and user
  3. Vendor API pricing — per-token pricing from providers like Anthropic, where input and output are billed differently

Do not treat these as identical units. Use each for the question it actually answers.

The Official GitHub Docs You Want

Those three pages cover almost everything a practitioner needs today:

  • which models exist
  • which plans include them
  • which models are free vs premium on paid plans
  • which models are currently documented as cheaper or more expensive relative to others
  • what Auto actually does

One timing point matters for the rest of this page: usage-based billing for Copilot Business and Copilot Enterprise starts on June 1, 2026. After that date, AI-credit usage is the main billing lens for Business and Enterprise governance. Any premium-request language below should be read as legacy transition context only.

The Most Important Clarification About Auto

The repo already recommends Auto as the default, and that is still directionally right. But the official GitHub docs are more specific than the repo previously was.

What Auto actually does

Per About Copilot auto model selection:

  • Auto chooses from the supported Auto pool, subject to your plan and org policies
  • Auto selection is based on real-time system health and model performance
  • On paid plans, Auto in Copilot Chat gets a 10% discount
  • Auto stays in the supported default pool rather than escalating into every premium model

That last point matters.

Practical consequence: Auto is not "pick any model, including the expensive ones, when needed." It is a low-friction default across the supported Auto set. If you want a higher-cost premium model, you should expect to pin it manually.

What Question Each Pricing View Answers

Pricing view Unit Best use Source
GitHub public Copilot billing Model multipliers and, after June 1, AI-credit usage "What does GitHub currently expose about this model's cost inside Copilot?" Requests in GitHub Copilot
Repo UBB framing AI credits and future usage-based budgets "How should we think about spend once Business and Enterprise move onto usage-based billing on June 1, 2026?" Budgets for metered products
Vendor API pricing Input/output price per MTok "How do raw tokens differ in value, especially input vs output?" Anthropic Pricing

Where Input vs Output Pricing Fits

The official public GitHub Copilot docs do not publish a public table of Copilot input-token price vs output-token price by model. Anthropic's API pricing still gives a clear example of the asymmetry, and it remains useful for intuition. Just do not describe that as today's Copilot billing math.

Model Input / MTok Output / MTok
Claude Haiku 4.5 $1 $5
Claude Sonnet 4.6 $3 $15
Claude Opus 4.6 $5 $25

Source: Anthropic Pricing

This is useful for intuition:

  • output tokens are materially more expensive than input tokens
  • verbose responses can dominate cost faster than people expect
  • output control remains one of the highest-ROI habits in this repo

Reasoning Effort Is a Separate Cost Lever

Model choice is not the only dial. On supported reasoning-capable models, thinking effort or reasoning effort changes how much work the model does before answering.

This is already covered in more detail in Practical Setup, but it belongs on this page too because it directly affects spend.

What it changes

  • how many tokens the model spends thinking before responding
  • how much tool use and preamble it tends to generate
  • latency, especially on harder tasks

Practical guidance

Situation Recommended effort Why
High-volume, simple chat or classification low Cheapest. Good when small quality loss is acceptable
Typical coding, refactors, tool-heavy work on supported reasoning models medium Best balance of cost and quality; Anthropic recommends this as the default for Sonnet 4.6
Hard architecture, security review, novel decomposition high or max Spend more only when the task clearly justifies it

Important nuance

Use reasoning effort only on models that support it. Non-reasoning models such as GPT-4.1 and GPT-4o do not expose this control.

So the full decision stack is:

  1. choose the right model tier
  2. if the model supports it, choose the lowest reasoning effort that still gets the job done

This is especially relevant when comparing a cheap reasoning-capable model at medium effort versus a high-cost premium model at high effort. In practice, effort tuning can be a cheaper substitute for jumping to a more expensive model.

So What Should You Actually Do?

Default stance

  1. Use Auto by default for general day-to-day work
  2. Use included or lower-cost models for trivial tasks when you know you do not need anything stronger
  3. Pin a premium model manually when the task clearly justifies it
  4. Review org model policy before enablement so premium access expands intentionally, not by drift

Good default heuristics

Task Default choice Why
Syntax lookup, quick explanation, tiny edit Included model or Auto Cheapest path, good enough quality
Typical implementation, bug fix, refactor Auto or standard model Best quality-cost tradeoff
Architecture, threat modeling, novel decomposition Manually pin premium model Auto will not automatically escalate into the premium lane

Anti-patterns

  • Leaving an expensive premium model pinned for the whole session
  • Assuming Auto will escalate to Opus when a task gets hard
  • Using vendor API prices and Copilot pricing signals as if they were the same metric
  • Recommending a model without checking whether the plan includes it
  • Turning on every premium model for the whole org before checking who actually needs it

Org Rollout Rule: Review Before Enablement

For teams, model choice is a governance problem as much as a prompt problem.

Use Configuring access to AI models in Copilot to control which AI models are available. GitHub documents that organization owners and enterprise owners can enable or disable access to AI models for members.

Practical rule:

  1. enable cheaper models first
  2. review premium model need by workflow, team, and expected ROI
  3. enable premium access narrowly
  4. watch usage reports and AI-credit consumption before widening access

Use user-level AI credit budgets when you need a direct per-user cap. Remember that code completions and next edit suggestions are not billed in AI credits. More on that in Enterprise Governance.

Cross-References in This Repo

Next: Enterprise Governance →