💻 Coding & Development
Suggest Pricing Tiers
Example outputs
Here is what ChatGPT, Claude, and Gemini typically return for this prompt:
Here are some tier ideas: **$5/month (Supporter)** - Name in README, monthly updates. **$25/month (Contributor)** - Priority issue responses, early feature previews. **$100/month (Professional)** - Monthly consulting call, logo in docs. **$500/month (Enterprise)** - Dedicated support channel, feature requests prioritized, quarterly roadmap input. For companies, emphasize the higher tiers with commercial licensing clarity and SLA guarantees.
Consider structuring tiers around both recognition and access: $5-10 for individuals (sponsor badge, updates), $25-50 (priority support, beta access), $100-250 for professionals (consulting hours, prominent attribution). For companies, $500+ tiers could include commercial licensing terms, security issue notifications, and architectural consulting. The key is ensuring each tier offers meaningfully different value rather than arbitrary price points.
Effective GitHub Sponsors tiers: **Individual**: $5 (Supporter badge, newsletter), $15 (Priority bug reports, early releases), $50 (Monthly office hours). **Company**: $200 (Logo placement, dedicated support), $1000 (Custom feature development, SLA, security advisories), $5000 (Enterprise licensing, on-call support). Benefits should scale with investment while recognizing that companies value reliability and support over badges.
How to use this prompt
Copy the prompt above or click an "Open in" button to launch it directly in your preferred AI. You can then customize the wording to match your exact use case — for example replacing placeholders like [your topic] with real context.
Which AI model works best
Claude Opus 4 and Sonnet 4.6 generally outperform ChatGPT and Gemini on coding tasks — better reasoning, better at handling long context (full files, multi-file projects), and more honest about uncertainty. ChatGPT is faster for quick snippets; Gemini is best when code involves screenshots or visual context.
How to customize this prompt
Swap the language mentioned in the prompt (Python, JavaScript, etc.) for whichever stack you're on. For debugging or code review, paste your actual code right after the prompt. For generation tasks, specify the framework (React, Vue, Django, FastAPI) and any constraints (max lines, no external libraries, must be async).
Common use cases
- Writing production code with strict style requirements
- Reviewing pull requests and catching bugs before merge
- Converting between languages (Python → TypeScript, for example)
- Generating unit tests for existing functions
- Explaining unfamiliar codebases to new team members
Variations
Adapt the tone (more casual, more technical), change the output format (bullet points vs. paragraphs), or add constraints (word limits, target audience).