If you’ve logged into GitHub this week and seen a polite green banner “GitHub Copilot moving towards AI credit” — That wasn’t a software update. This was a new challenge for your finance team. From June 1, 2026, the price of Copilot is going to increase by almost 9 times in most Indian enterprises. We tested the options so you don’t have to look the hard way.
What’s actually changing on June 1st?
Copilot is removing and switching out the old “Premium Request” meter GitHub AI Credits. Starting June 1, every interaction is billed by token — inputs, outputs, even bits cached in memory — at the same API rates Anthropic, OpenAI, and Google charge GitHub.
Headline plan prices appear unchanged. CoPilot Pro+ is still there USD 39 per month, Business USD 19, Enterprise USD 39. Hunt? Each plan includes only that dollar amount of AI credits. Spend your US$19 by lunch on the 3rd, and the rest of the month is on your credit card.
why now?
GitHub’s own Entry: : “Today, a quick chat question and a multi-hour autonomous coding session can cost a user the same amount. GitHub has absorbed the rising estimated costs behind that usage, but the current premium request model is no longer sustainable.“ The second signal comes from Anthropic – Cloud Opus 4.7 (April 16, 2026) ships with a new tokenizer that emits 35% more tokens for a single signal, and starting June 15, Cloud Code and Agent SDKs get their own metered credit pool. Translation: CoPilot’s wholesale costs went up, and now yours has too.
At Hindustan Times, our preview bill on the new GitHub model is roughly estimated 9 times our April expenses – And we’re not a small team.
Want to check the damage yourself?
Go to GitHub → Your Organization → Billing Overview → Preview your usage.
How the Top AI Coding Models Really Compare in 2026
We benchmarked five families that matter to Indian development teams. Quality first, cost second, discretion third.
Coding Quality – SWE-Bench Verified and LiveCodeBench (May 2026)
Summary: Public leaderboards put the GPT-5.5 codec far ahead, but in our own internal tests on HT’s stack – Java, HTML, MongoDB, Flutter, Swift, Kotlin, React frontends and ML pipelines – Cloud Opus 4.7, whether called directly or through Copilot, beats Codex on tasks our engineers actually do every day. Codex is still ahead on the longer terminal-agent benchmarks; Opus leads our work.
How did we benchmark costs?
To calculate costs across all models, we used the same prompts across all models to create a new customer support microservice with detailed requirements. The results were surprising in terms of both token usage and overall cost. In terms of quality, Opus 4.7 emerged as the winner, while Gemini 3.1 Pro came last.
A similar experiment was conducted to enhance the functionality of existing services where thousands of tokens are sent to the AI model along with the current repos content to implement a feature. Here, DeepSeek was the clear winner in cost due to the lower cost of cached input..
The catch: DeepSeek is about 2x slower than Opus – but you don’t have to use DeepSeek’s own API
We won’t pretend it’s not slow. DeepSeek’s official API thinks before responding, and our tasks take about twice as long per response as Opus.
But – and this is the lesson from the two-week pilot work – the new SDLC is not a single-task. Engineers run an agent on service A while debugging service B and reviewing PRs on service C. When you parallelize three tasks, a 2x slower agent doesn’t just slow down the developer by 2x – it slows down the project by maybe 10-15%. The wall-clock cost is real, but it’s far from a deal-breaker, and the bill savings outweigh it.
For engineering organizations with over 400 developers and a serious appetite for control, DeepSeek v4 Pro is MIT licensed And the weight on the hugging face is public. You can improve it, modify it and deploy it commercially without any restrictions.
Don’t Trust Flat-Rate Plans Without Doing the Math
Cloud Code Max (USD 100 / USD 200 per month). The USD 100 tier gives a senior engineer about 15-35 hours of Opus per week and 88,000 tokens per 5 hour window. For a heavy user, this is fine. For a 150-engineer organization, you’re looking at US$100,000-150,000 per year just to keep the lights on.
OpenAI Codecs (ChatGPT Pro USD 200, Business USD 30/user). Strong quality, but OpenAI can cost a developer “USD 40 or USD 400” depending on what they do with it.
PS: Open AI is giving away Codex free for enterprise for two months. We have already applied. you can applyHere
Amazon Kiro.Here is the unsung hero. Kiro Root Cloud prompts for Sonnet 4.6/Opus 4.7, gives you full multimodal (screenshots, diagrams, videos), and starts Pro tierUS$20/month with 1000 AI credits. With heavy usage, we have observed that it costs less than other models/tools but more than DeepSeek.
Pro Tip: You can run DeepSeek inside Cloud Code
DeepSeek shipsHuman Compatible APIWhich means you can point the Cloud Code CLI to DeepSeek with a single environment variable swap.
HT Tech Team Recommendation
After a two-week POC between ~50 engineers spanning backend, frontend, Android, iOS, and ML, here’s the stack we’re moving to – and the simple rules we wrote down on the whiteboard:
If you want the best quality without any usage limits, run Cloud Code Opus 4.7 on Max. If you’re cost sensitive – as almost every company is these days – then split the work: Kiro for frontend, DeepSeek for backend, and ML and Codex/Opus for complex work.
Concretely:
- Front-end, mobile, and design-heavy teams → Kiro (Pro USD 20). Screenshot debugging, CLS/LCP work, Figma-to-code, responsive testing, animation – you get Cloud Opus/Sonnet under the hood with full multimodal support, and overage rates are so low that a heavy month can stay in the range of US$50-100 per developer.
- Backend, Platform, and ML Teams → DeepSeek v4 Pro. APIs, services, refactors, test generation, data pipelines, model training code. About 1/20th the cost of Opus at about the same SWE-bench score as Opus 4.6, with cached-token re-reads that are practically free.
- Staff Engineers and High Risk Tasks → Cloud Codes/Codexes. Architectural foundation-setting, complex personalization logic, live event debugging, anything where waiting two extra seconds for a response costs more than a few dollars in tokens.
- For non-engineering work (PM, designers, ops, editorial) → Consumer Cloud subscription (USD 25/month Enterprise tier) is still excellent – Drafting of specifications, summarizing calls, light research. Just be careful: Opus 4.7 quota runs out after about 3-4 enough questions in a session, then you wait for the next reset window or come back to Sonnet. Useful for thinking, not for sustained agentic action.
One migration gotcha that’s worth marking up front: Any custom agents, sub-agents, or playbooks that you create against Copilot’s orchestration model No Do a one-to-one port to Kiro or DeepSeek. Tool definitions, hand-off semantics, and context windows are separate. Plan on one engineer-week per non-trivial agent to re-write and re-test before cutover. Don’t look for it on June 2nd.
Disclosures and Disclaimers
Hindustan Times runs a multi-cloud engineering stack with active commercial relationships across leading hyperscalers and AI model providers – including AWS, Microsoft (GitHub), Google Cloud and Anthropic. The findings in this article reflect our internal pilot evaluation against HT-specific use cases (newsroom platforms, content APIs, recommendation systems, mobile apps, ML personalization). They are not vendor support. Other organizations should run their own assessment according to their own stack, scale, and requirements before making a purchasing decision.
Safety has not been evaluated as part of this exercise. This article ranks the listed models based only on coding quality, cost, and speed. We have not evaluated any of these tools against enterprise security, data-residency, IP-protection, model-output-leakage, prompt-injection-resistance, regulatory compliance, or audit-trail requirements. Especially as DeepSeek is a China-headquartered provider – your security, legal, and compliance teams must independently evaluate whether sending source code, customer data, or proprietary content to any of these endpoints meets your organization’s policy. Self-hosting is the perfect path for teams that can’t push code to a third-party API.
HT will publish an updated version of this article in June, when approximately 150 HT engineers have completed their migration to the new stack and we will have wall-clock data on productivity, cost, and incident impact. Bookmark this page if you want to follow up.
