I Did the Math on GitHub Copilot's New AI Credits Billing. The 24x Price Gap Changes Everything.

On June 1, 2026, GitHub flipped the switch on a new billing model for Copilot. The headlines that hit my Twitter feed:

• "GitHub is charging by token now"
• "Copilot autocomplete is no longer free"
• "Your Pro $10/mo just became $30/mo"

Two of those are wrong. One is partially right but completely depends on which model you pick.

I spent an afternoon pulling the actual pricing tables out of GitHub's docs and running the math on 5 real workflows. The numbers are not what the panicked threads say.

TL;DR

• Code completions and next edit suggestions are still included. They do not consume AI Credits. Anyone telling you "every autocomplete now costs money" is wrong.
• Base plan prices did not change. Pro is still $10, Pro+ still $39, Business still $19/user, Enterprise still $39/user.
• What changed: agent workflows now consume AI Credits priced by input/output/cached tokens at each model's published rate.
• The same task costs 24x more or less depending on which model you pick. Picking MAI-Code-1-Flash over GPT-5.5 for a heavy agent run costs $0.28 instead of $1.85.
• Your bill changes by behavior, not by GitHub raising prices. If you route heavy agent tasks through expensive models, costs go up. If you route them through cheap models, costs go down or stay flat.

What actually shipped

Element	Before June 1	After June 1
Code completions	Included	Included (still no Credits used)
Next edit suggestions	Included	Included
Agent workflows	Premium Request Units	AI Credits (token-based)
Pro price	$10/mo	$10/mo
Pro+ price	$39/mo	$39/mo
Business price	$19/user	$19/user
Enterprise price	$39/user	$39/user

The Premium Request Units regime treated every "request" as a unit regardless of how much actual compute it consumed. A 3-second hello-world question and a 10-minute multi-step agent both deducted 1 unit. That math broke as agents got more capable.

Token-based billing reflects what the inference actually cost GitHub. Reasonable on the supply side. Whether it costs YOU more depends entirely on your model choices.

The 24x price gap

Here's the model price table from GitHub's docs, normalized to what $10 buys:

Model	$10 input tokens	$10 output tokens	When you'd actually use it
GPT-5.4 nano	50M	8M	Light Q&A, quick rephrasing
GPT-5 mini	40M	5M	Cheap code assistance
MAI-Code-1-Flash	13.3M	2.22M	Default for routine Copilot tasks
Claude Haiku 4.5	10M	2M	Cheap Claude-flavored assistant
Gemini 3.1 Pro	5M	0.83M	Medium reasoning + long context
Claude Sonnet 4.6	3.33M	0.67M	Serious coding/reasoning
Claude Opus 4.8	2M	0.40M	High-stakes coding
GPT-5.5	2M	0.33M	Frontier reasoning

GPT-5.4 nano gets you 50M input tokens for $10. GPT-5.5 gets you 2M. That's a 25x spread on input alone, 24x on output. The same dev workflow can cost either tier — your routing decisions are now the largest variable in your Copilot bill.

What 5 real workflows cost

I picked workflows that match what I actually do in a normal week. Each row is the same task run on a cheap vs medium vs frontier model.

Workflow 1: Small bug fix (3K input / 1K output)

• MAI-Code-1-Flash: $0.0068 (0.68 credits)
• Claude Sonnet 4.6: $0.024 (2.4 credits)
• GPT-5.5: $0.045 (4.5 credits)

For a 3-line bug fix, you do not need Opus or GPT-5.5. The cheap model gets the same answer 7x cheaper.

Workflow 2: Medium agent step (10K input / 2K output)

• MAI-Code-1-Flash: $0.0165
• Claude Sonnet 4.6: $0.060
• GPT-5.5: $0.110

Workflow 3: Large repo context pass (80K input / 5K output)

• MAI-Code-1-Flash: $0.0825
• Claude Sonnet 4.6: $0.315
• GPT-5.5: $0.550

This is where most Copilot agents live. Reading a chunk of repo context, holding it in working memory, making changes. The 7x difference compounds across a typical workday.

Workflow 4: Heavy iterative agent (250K input / 20K output)

• MAI-Code-1-Flash: $0.2775
• Claude Sonnet 4.6: $1.05
• GPT-5.5: $1.85

This is the run that scared everyone on Twitter. $1.85 for a single agent task IS a lot if you're running 50 of these a day. That's $92.50/day = ~$2,000/mo on one developer's GitHub Copilot bill.

But run the same task on MAI-Code-1-Flash and the daily cost is $13.88 = ~$300/mo. Or stay on Sonnet 4.6 and pay $52.50/day = ~$1,150/mo.

The model choice is the bill.

Workflow 5: Review-heavy task (100K input / 40K output)

• MAI-Code-1-Flash: $0.255
• Claude Sonnet 4.6: $0.900
• GPT-5.5: $1.700

How much you actually get included

Your monthly plan now comes with AI Credits. Here's how far they go:

Plan	Monthly fee	AI Credits/mo	Value in $
Pro	$10	1,500	$15
Pro+	$39	7,000	$70
Max	$100	20,000	$200
Business	$19/user	1,900/user (pooled)	$19/user
Enterprise	$39/user	3,900/user (pooled)	$39/user
Business (promo Jun 1 - Sep 1)	$19/user	3,000/user	$30/user
Enterprise (promo Jun 1 - Sep 1)	$39/user	7,000/user	$70/user

Two things to notice:

1. Pro at $10 includes $15 of credits. You're net-up if you use the included credits.
2. Business/Enterprise customers get a 3-month promo doubling their pool. GitHub knows the transition is going to spike anxiety. They built in cover.

The "Will I pay more?" decision tree

Here's how I'd think about whether your specific situation gets cheaper or more expensive:

def will_you_pay_more(your_workflow):
    # Code completions are still included. If that's 90% of your usage:
    if "mostly autocomplete" in your_workflow:
        return "No change. Continue paying base plan."

    # Agent workflows on cheap models actually got cheaper:
    if "agent workflows on MAI-Code-1-Flash or nano" in your_workflow:
        return "Same or lower bill. Included credits often cover usage."

    # Heavy agent runs on frontier models = the big risk:
    if "frequent agent runs on GPT-5.5 or Opus 4.8" in your_workflow:
        return f"BIGGER BILL. Each heavy run costs ~$1-2. " \
               f"Set up budget caps NOW."

    # The middle tier is where most devs live:
    return "Marginal change. Watch for first month's bill, adjust model routing."

Cost control levers that actually work

Five things I'm doing this week to keep my Copilot bill predictable:

Lever	Effort	Saving	How
Default to MAI-Code-1-Flash for routine tasks	Low	50-90%	Set in Copilot model picker
Limit max_tokens on agent runs	Low	20-70%	Output dominates cost on long tasks
Use cached context (system prompts)	Medium	50-90% on reuse	Cached input is 10x cheaper
Set hard user-level budgets	Low	Prevents bill surprises	GitHub Docs → budgets
Route by task complexity	Medium	30-80%	Cheap model for simple, escalate when needed

The user-level budget cap is the most important one if you're on Business or Enterprise. The pool gets shared, and one heavy user can blow through it for the team. Set per-user caps and "stop usage when budget reached" so nobody surprises you with a $200/day spike.

What I'd do if I were on Copilot today

Concrete actions, by plan:

Pro users ($10/mo):

1. You're getting $15 value in credits. Net-up if you use them.
2. Pick MAI-Code-1-Flash as your default model.
3. Don't worry about autocompletes — they're still free.
4. Run through your first month's usage report at end of June to see your real consumption.

Pro+ users ($39/mo):

1. You get 7,000 credits = $70 value. Still net-up.
2. If you're doing heavy agent work, default to Sonnet 4.6 instead of GPT-5.5 — gets you 3-5x more agent steps for the same credits.
3. Same advice on autocomplete: still free.

Business/Enterprise admins:

1. Set per-user budget caps before anyone runs a heavy agent. This is the single most important configuration change.
2. Use the June 1 - Sep 1 promo (extra 1,100-3,100 credits/user) to measure baseline usage before the promo expires.
3. Look at your top 10% of usage users — they'll be the ones running frontier models on long-context tasks. Have a conversation about routing.
4. Read the models and pricing docs carefully before September 1.

The bigger picture

This isn't a GitHub-specific story. It fits a pattern that's playing out across AI providers in 2026:

• Doubao (ByteDance, May 4) — Chinese consumer AI introduces 3-tier paid subscription
• Anthropic Mythos — premium tier above Opus, projected $25/$125 per million tokens
• GitHub Copilot (today) — usage-based agent billing
• OpenAI — multiple tier launches with Pro tiers at $200/mo

The free-or-flat-rate era is winding down. Every major AI surface is moving to "you pay for what you actually consume." The trade-off: cheaper for light users, more expensive for power users, and your routing decisions become the largest variable in your bill.

The right response is not panic — it's instrumentation. Know what each task type costs on each model, default to cheap models for routine work, and put caps on top users. GitHub's billing change is the cleanest "what this actually costs" surface I've seen so far.

If you want to swap between OpenAI / Anthropic / Google models through one OpenAI-compatible endpoint with config-driven routing (so you can change defaults without code changes), that's roughly what TokenMix does. Disclosure: I work on the research side. Full cited breakdown of the Copilot pricing tables is on the original article.

Bottom line

GitHub didn't quietly raise your bill. They changed the surface so your routing decisions show up in the bill. Pick cheap models by default, set budget caps, and your bill goes down. Pick expensive models without thinking, and you'll get surprised.

Either way, the era of "1 Copilot request = 1 unit regardless of cost" is over. Everywhere.

What's your Copilot routing strategy looking like after June 1? Drop a comment.