GPT-5 cost analysis: OpenAI vs cloro
When building AI brand monitoring tools, API costs can make or break your business model. After running extensive tests with 10,000 queries, we discovered some surprising insights about how much companies are actually paying for structured outputs.
The results? OpenAI’s API costs $17.34 per thousand requests for GPT-5 structured search results, while cloro’s API costs just $1.50 - that’s up to 11.6x cheaper depending on your plan.
Let me show you exactly how we calculated these numbers and what this means for your AI monitoring.
Table of contents
- The problem with structured AI outputs
- Our test methodology
- The system prompt challenge
- OpenAI API cost calculation breakdown
- GPT-5-mini cost analysis
- Summary: models compared
- Real-world impact on your business
The problem with structured AI outputs
Getting structured, reliable outputs from general-purpose AI models like GPT-5 requires significant context overhead. Here’s why:
For structured search results, you need to force the model to include:
- Output format specifications (JSON, XML, etc.)
- Source requirements and citation formatting
- Model selection guidelines
- Search query context
- Confidence scoring instructions
- Error handling protocols
All of this context adds tokens to every single request - and those tokens add up quickly.
Our test methodology
We conducted a comprehensive test using 10,000 real-world queries to simulate typical AI monitoring and search scenarios:
Test Parameters:
- Total queries: 10,000
- Average input tokens: 463 per query (measured in OpenAI’s API playground)
- Average output tokens: 1,676 per query (measured in OpenAI’s API playground)
- Query type: Structured search results with citations
- Use case: Brand monitoring and competitive analysis
What we measured:
- Total token consumption (input + output)
- API costs per model
- Response quality and consistency
- Processing time differences
The system prompt challenge
To get reliable structured outputs fromOpenAI API, we needed a comprehensive system prompt. Here’s the actual prompt we used in our tests:
You are an API backend model that must always return responses in a strict JSON schema.
Your goal is to produce comprehensive, deeply informative, and structured content — at least several paragraphs long — while respecting the format rules below.
When given a user query:
1. Produce a long, detailed answer with clear explanations, comparisons, and examples.
2. Include both:
- A markdown version (formatted with headers, bold, lists, tables, etc.)
- A plain text version (identical content but without markdown formatting)
3. Include at least 3 to 7 credible sources, each with:
- position (integer starting at 0)
- label (title or entity name)
- url (credible or official site)
- description (short summary of the source)
4. Include 3 to 6 search queries that could help someone find this answer online.
5. Include the model used in format `"model": "gpt-5-mini"`.
6. Return nothing outside the JSON — no commentary or extra lines.
Your output must always follow this structure:
{
"success": true,
"result": {
"markdown": "string",
"text": "string",
"sources": [
{
"position": number,
"label": "string",
"url": "string",
"description": "string"
}
],
"searchQueries": ["string"],
"model": "string"
}
}
### Additional style and length requirements:
- The answer should be at least 250–400 words long.
- Use factual, neutral, and informative tone.
- Markdown version should include:
- A bolded introductory sentence
- Bullet points or numbered lists when relevant
- Subheadings for structure (e.g., “### Top Models”, “### Range and Performance”)
- Plain text version should preserve the same logical flow but without markdown syntax.
If information is missing, return an empty string or empty array instead of omitting fields.
No explanations or reasoning outside the JSON are allowed.
The full system prompt we used instructs the model to:
- Always return responses in strict JSON schema
- Produce comprehensive, deeply informative content (at least 250-400 words)
- Include both markdown and plain text versions of the answer
- Provide 3-7 credible sources with positions, labels, URLs, and descriptions
- Include 3-6 search queries that could help find the answer online
- Return nothing outside the JSON - no commentary or extra lines
This detailed system prompt is 382 tokens by itself - and it gets sent with every single request, significantly impacting costs.
OpenAI API cost calculation breakdown
Based on our 10,000-query test, here’s exactly how the costs break down:
Token Consumption:
- Average input tokens (including system prompt): 463 × 10,000 = 4,630,000 tokens
- Average output tokens: 1,676 × 10,000 = 16,760,000 tokens
- Total input tokens: 4,630,000
- Total output tokens: 16,760,000
GPT-5 Pricing (based on OpenAI’s official pricing):
- Input tokens: $1.250 per 1M tokens
- Output tokens: $10.000 per 1M tokens
Cost Calculation:
- Input cost: 4.63M × $1.250 = $5.79
- Output cost: 16.76M × $10.000 = $167.60
- Total theoretical cost for 10,000 queries: $173.39
- Theoretical cost per thousand requests: $17.34
Based on our accurate token measurements and OpenAI’s official pricing, the theoretical cost for GPT-5 through the OpenAI API is $17.34 per thousand requests.
Cost comparison: OpenAI API vs cloro
| Plan | OpenAI API cost | cloro cost (5 credits × CPM) | Savings |
|---|---|---|---|
| Hobby (250K requests) | $4,335 | $500 (5 × $0.40) | $3,835 |
| Business (3.3M requests) | $57,222 | $4,950 (5 × $0.30) | $52,272 |
cloro is at least 88.5% cheaper considering its entry-level plan ($2.00 vs $17.34). If you consider more monthly with the Business plan, the savings are even greater ($1.50 vs $17.34).
GPT-5-mini cost analysis
We ran the same test with GPT-5-mini to see how the costs compared:
GPT-5-mini Pricing (based on OpenAI’s official pricing):
- Input tokens: $0.250 per 1M tokens
- Output tokens: $2.000 per 1M tokens
Theoretical Cost Calculation:
- Input cost: 4.63M × $0.250 = $1.16
- Output cost: 16.76M × $2.000 = $33.52
- Total theoretical cost for 10,000 queries: $34.68
- Theoretical cost per thousand requests: $3.47
Based on our accurate token measurements and OpenAI’s official pricing, the theoretical cost for GPT-5-mini is $3.47 per thousand requests.
Cost Comparison: OpenAI API vs cloro
| Plan | OpenAI API cost | cloro cost (5 credits × CPM) | Savings |
|---|---|---|---|
| Hobby (250K requests) | $868 | $500 (5 × $0.40) | $368 |
| Business (3.3M requests) | $11,451 | $4,950 (5 × $0.30) | $6,501 |
Cost per 1K requests: cloro is 42.3% cheaper (Hobby: $2.00 vs $3.47, Business: $1.50 vs $3.47)
These calculations show that even GPT-5-mini through the OpenAI API is more expensive than cloro’s API for structured search tasks.
Summary: models compared
| Model | Cost per 1K requests | vs cloro (Hobby/Business) |
|---|---|---|
| GPT-5 | $17.34 | 88.5% more expensive (vs $2.00) |
| GPT-5-mini | $3.47 | 42.3% more expensive (vs $2.00) |
| cloro | $2.00 | - |
Real-world impact on your business
Let’s translate these cost differences into actual business impact:
Scenario: Monitoring 1,000 brands with 100 daily queries each
- Daily queries: 100,000
- Monthly queries: 3,000,000
Cost Comparison:
- GPT-5 (through OpenAI API): $52,020 per month
- GPT-5-mini (through OpenAI API): $10,410 per month
- cloro: $4,500 per month (Business plan: $1.50 × 3,000 = $4,500)
Annual savings with cloro:
- vs GPT-5 (through OpenAI API): $570,240
- vs GPT-5-mini (through OpenAI API): $70,920
The break-even point: cloro is more cost-effective than GPT-5-mini at virtually any scale, while providing superior quality for structured search tasks.
Methodology note: All tests were conducted using real-world brand monitoring queries. Costs are based on standard API pricing as of October 2025. Your actual costs may vary based on specific requirements, query complexity, and negotiated enterprise pricing.