LLM Economics

OpenAI vs Claude for RAG Pipelines: Cost Comparison

Compare OpenAI and Claude on the same workload and see which is cheaper.

Shared workload

For this workload, OpenAI is cheaper by ~ $720.00/year.

Cost comparison

Side-by-side metrics

MetricOpenAIClaude
Monthly cost$75.00$135.00
Yearly cost$900.00$1,620.00
Daily cost$2.50$4.50
Yearly saving$720.00 saved

OpenAI

Estimated yearly cost
$900.00/year

(2,000 × $1.25 + 500 × $10) / 1M × 10,000 req × 12

Already optimal — check cache & batch
OpenAI GPT-5 is already the lowest-cost comparable option for this workload. Focus on Prompt Cache and Batch API discounts to reduce costs further.
Prompt Cache not enabledEnable Prompt Cache

Enabling Prompt Cache with a 60% hit ratio could save $162.00/yr (18% reduction). Fix your system prompt at the start of every request and reuse it across calls.

$162.00
saved / year
GPT-5 Mini could replace GPT-5Consider model switch

Switching to GPT-5 Mini cuts estimated cost by 80% — saving $720.00/yr. Run an A/B test against a representative eval set before changing production traffic.

$720.00
saved / year
Monthly cost
$75.00/month
Daily cost
$2.50/day
Cost per request
$0.007500
Model
GPT-5
Input rate
$1.25/MTok
Output rate
$10/MTok

Cost breakdown

ItemMonthlyYearly
Input spend (2,000 tok/req × 10,000 req × $1.25/MTok)$25.00$300.00
Output spend (500 tok/req × 10,000 req × $10/MTok)$50.00$600.00
Total monthly spend$75.00$900.00

Comparison

OptionMonthlyYearly
OpenAI GPT-5currentcheapest$75.00$900.00
Claude Claude Sonnet 4.6$135.00$1,620.00
Gemini Gemini 2.5 Pro$75.00$900.00

Pricing sources

Last verified 2026-06-30 · openai.com/api/pricing openai.com/api/pricing · platform.claude.com/docs/about-claude/pricing platform.claude.com/docs/about-claude/pricing · ai.google.dev/gemini-api/docs/pricing ai.google.dev/gemini-api/docs/pricing

Industry Benchmark

Output price vs. peer average ($/1M tokens)Industry avg: 11.67 $/1M
You are at the 43th percentile

Pricing sources

Last verified 2026-06-30 · openai.com/api/pricing openai.com/api/pricing · platform.claude.com/docs/about-claude/pricing platform.claude.com/docs/about-claude/pricing · ai.google.dev/gemini-api/docs/pricing ai.google.dev/gemini-api/docs/pricing

Claude

Estimated yearly cost
$1,620.00/year

(2,000 × $3 + 500 × $15) / 1M × 10,000 req × 12

Switch to OpenAI GPT-5 — save $720.00/yr
At $1,620.00/yr, you're paying $720.00/yr above the optimal for this workload. OpenAI GPT-5 handles the same volume at $900.00/yr — 44.4% less, comparable quality for most use cases. Every month you wait costs $60.00.
$720.00
saved / year (44.4%)
Prompt Cache not enabledEnable Prompt Cache

Enabling Prompt Cache with a 60% hit ratio could save $388.80/yr (24% reduction). Fix your system prompt at the start of every request and reuse it across calls.

$388.80
saved / year
Monthly cost
$135.00/month
Daily cost
$4.50/day
Cost per request
$0.013500
Model
Claude Sonnet 4.6
Input rate
$3/MTok
Output rate
$15/MTok

Cost breakdown

ItemMonthlyYearly
Input spend (2,000 tok/req × 10,000 req × $3/MTok)$60.00$720.00
Output spend (500 tok/req × 10,000 req × $15/MTok)$75.00$900.00
Total monthly spend$135.00$1,620.00

Comparison

OptionMonthlyYearly
OpenAI GPT-5cheapest$75.00$900.00
Claude Claude Sonnet 4.6current$135.00$1,620.00
Gemini Gemini 2.5 Pro$75.00$900.00

Pricing sources

Last verified 2026-06-30 · openai.com/api/pricing openai.com/api/pricing · platform.claude.com/docs/about-claude/pricing platform.claude.com/docs/about-claude/pricing · ai.google.dev/gemini-api/docs/pricing ai.google.dev/gemini-api/docs/pricing

Industry Benchmark

Output price vs. peer average ($/1M tokens)Industry avg: 11.67 $/1M
You are at the 64th percentile

Pricing sources

Last verified 2026-06-30 · openai.com/api/pricing openai.com/api/pricing · platform.claude.com/docs/about-claude/pricing platform.claude.com/docs/about-claude/pricing · ai.google.dev/gemini-api/docs/pricing ai.google.dev/gemini-api/docs/pricing

Pricing model differences

OpenAI and Claude price input and output tokens at different rates and offer different caching and batch discounts. For output-heavy workloads the output token price dominates; for context-heavy workloads, cached-input pricing matters most. Understanding this split is critical: a chatbot generating long responses will be driven by output cost, while a RAG pipeline with large context windows will be driven by input cost.

When to choose each

Pick the provider whose pricing best fits your token ratio and volume. If your workload is output-heavy (code generation, long-form writing), compare output rates directly. If your workload reuses long system prompts or few-shot examples, compare cached-input pricing — a provider with aggressive caching discounts can be 5-10× cheaper on the input side. Use the shared input above to model your real workload, then check the cheaper option and the annual saving before committing.

Common switching scenarios

Teams commonly switch providers when: (1) they need to cut costs without sacrificing quality — moving from a flagship model to a competitor's mid-tier can save 60-80% while maintaining acceptable output quality; (2) a specific benchmark matters — one provider may lead in coding while another leads in reasoning; (3) context window requirements change — some providers offer 1M+ tokens natively while others require workarounds. Use our Model Migration Calculator to estimate the full switching cost including engineering time.

Beyond price: quality and latency

Cost is only one dimension of an LLM decision. Response latency (time-to-first-token and tokens-per-second), output quality on your specific task, and reliability (uptime, rate limits) all matter. A model that's 30% cheaper but 50% slower may cost more when you factor in user wait time or throughput requirements. Check our LLM Leaderboard for benchmark comparisons, and run A/B tests on your actual prompts before committing to a provider switch.

Compare these models on coding, reasoning, and agentic benchmarks → LLM Leaderboard

Frequently asked questions

Which is cheaper, OpenAI or Claude?

It depends on your token mix and volume. Enter your workload above — the calculator computes both and shows the cheaper option with the exact yearly saving.

How do the pricing models differ?

Each provider prices input and output tokens differently and offers different cached-input and batch discounts. The side-by-side breakdown makes the difference explicit.

Is this based on official pricing?

It uses comparable-tier placeholder pricing from a versioned JSON config. Verify against each provider's official price page; the footer shows the data version.

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OpenAI vs Claude for RAG Pipelines: Cost Comparison