OpenAI vs Anthropic: Token costs compared for production apps
Choosing an AI model for your production app is increasingly a cost decision as much as a capability one. The gap between the most and least expensive frontier models is now 50× or more, and picking the wrong one can make an otherwise sustainable product economics look terrible.
Here's a practical breakdown of what the major models cost today, and how to think about them for real applications.
Current pricing (June 2026)
All prices are per 1M tokens.
| Model | Input | Output | |-------|-------|--------| | GPT-5.5 | $5.00 | $30.00 | | Claude Opus 4 | $5.00 | $25.00 | | GPT-5.4 | $2.50 | $15.00 | | Claude Sonnet 4.6 | $3.00 | $15.00 | | GPT-4.1 | $2.00 | $8.00 | | GPT-5.4-mini | $0.75 | $4.50 | | GPT-4o | $2.50 | $10.00 | | Claude Haiku 4.5 | $1.00 | $5.00 | | GPT-4.1-mini | $0.40 | $1.60 | | GPT-4.1-nano | $0.10 | $0.40 | | GPT-4o-mini | $0.15 | $0.60 |
What this means in practice
Assume an average user interaction generates 500 input tokens and 300 output tokens.
With GPT-5.5:
(500 × $0.000005) + (300 × $0.000030) = $0.0115 per interaction
With GPT-4.1-nano:
(500 × $0.0000001) + (300 × $0.0000004) = $0.00017 per interaction
That's a 67× cost difference for the same interaction. At 10,000 interactions/month, that's the difference between a $170 bill and a $2.50 bill.
When to use which model
Use the smallest model that works. This sounds obvious but most apps start with GPT-4o out of habit and never benchmark cheaper alternatives.
For most chat, Q&A, and summarisation tasks, GPT-4.1-mini or GPT-4o-mini will get you 90% of the output quality at 10–15% of the cost. Test with your actual prompts, not benchmarks — benchmark tasks are cherry-picked for capability gaps that may not exist in your use case.
Claude Haiku 4.5 is the best value for structured output tasks. It follows instructions reliably, is very fast, and costs significantly less than the mid-tier models.
Reserve the frontier models (GPT-5.5, Claude Opus, GPT-5.4, Claude Sonnet) for tasks that genuinely require them: complex reasoning, code generation for hard problems, long-form writing that needs to be publishable-quality.
The hidden cost: output tokens
The pricing table above reveals something important: output tokens are 4–10× more expensive than input tokens across all providers.
This means your system prompt length matters far less than how verbose your model is. A long system prompt that makes the model respond more concisely is almost always worth it economically.
Practical tips:
- Explicitly instruct the model to be concise where quality allows
- Use streaming so users see output immediately — this reduces perceived latency without affecting costs
- Set a
max_tokenslimit appropriate to your use case
Per-user budgets as a safety net
Even with the right model and good prompt engineering, individual users will vary enormously in how much they cost. The same user who costs $0.10/month on average can cost $50 in a single session if they decide to use your AI feature intensively.
The only reliable way to prevent this is per-user spend tracking with automatic blocking. That's what Nasca does — it watches spend per end-user in real-time and blocks calls once they exceed the budget you define for their tier, before the call is even sent to the AI provider.