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

GitHub's new token-based billing for Copilot, effective June 1, 2026, introduces a 24x price gap between the cheapest and most expensive models, according to an engineer's analysis of the pricing tables. Running the same agent workflow on GPT-5.5 costs $1.85 per task, while using MAI-Code-1-Flash costs just $0.28 — a 7x difference that compounds across a typical workday. The analysis shows that model selection, not just usage volume, is now the primary driver of a developer's monthly Copilot bill, with GPT-5.4 nano offering 50 million input tokens for $10 versus GPT-5.5's 2 million.

On June 1, 2026, GitHub flipped the switch on a new billing model for Copilot. The headlines that hit my Twitter feed: 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. MAI-Code-1-Flash over GPT-5.5 for a heavy agent run costs $0.28 instead of $1.85.| 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. 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. 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. For a 3-line bug fix, you do not need Opus or GPT-5.5. The cheap model gets the same answer 7x cheaper. 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. 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. 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: Here's how I'd think about whether your specific situation gets cheaper or more expensive: python 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." 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. Concrete actions, by plan: Pro users $10/mo : MAI-Code-1-Flash as your default model. Pro+ users $39/mo : Business/Enterprise admins: This isn't a GitHub-specific story. It fits a pattern that's playing out across AI providers in 2026: 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 https://tokenmix.ai does. Disclosure: I work on the research side. Full cited breakdown of the Copilot pricing tables is on the original article https://tokenmix.ai/blog/github-copilot-ai-credits-billing-2026 . 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.