Claude API vs GPT-4 API Pricing Comparison 2025

If you're comparing Claude API and GPT-4 API pricing, the short answer is: both bill per token (input and output priced separately), both offer a cheaper "mini" tier and a pricier flagship tier, and the real cost difference in your bill usually comes less from the sticker price and more from output length, caching, and how many retries your app does. Below is a breakdown of how the tiers stack up and how to actually estimate what you'll pay.

Pricing on both platforms changes periodically, so treat the numbers here as a snapshot for comparison purposes and always check Anthropic's and OpenAI's official pricing pages before budgeting a production workload.

How the pricing model works on both platforms

Anthropic (Claude) and OpenAI (GPT-4) use the same basic unit: cost per million tokens, split into input tokens (what you send) and output tokens (what the model generates). Output tokens are always more expensive than input tokens on both platforms, typically by a factor of 4-5x.

Rough per-million-token figures at the time of writing:

Claude:

• Claude 3.5 Haiku — ~$0.80 input / ~$4.00 output
• Claude 3.5 Sonnet — ~$3.00 input / ~$15.00 output
• Claude 3 Opus — ~$15.00 input / ~$75.00 output

GPT-4 family:

• GPT-4o mini — ~$0.15 input / ~$0.60 output
• GPT-4o — ~$2.50 input / ~$10.00 output
• GPT-4 Turbo — ~$10.00 input / ~$30.00 output

Two things jump out. First, the "mini" tiers are dramatically cheaper than the flagship tiers on both sides — if your task is simple (classification, extraction, short summarization), the model choice matters less than picking the right tier. Second, Claude's mid-tier (Sonnet) and OpenAI's mid-tier (GPT-4o) sit in a similar price band, while Claude's top tier (Opus) is roughly 50% more expensive than GPT-4 Turbo on input and over double on some output comparisons.

Why the sticker price isn't the whole story

Three factors change your real monthly bill more than the base rate:

Output length. Since output tokens cost 4-5x more than input, a model that tends to write longer, more verbose answers for the same prompt will cost more even at an identical per-token rate. This is prompt-dependent and worth testing on your actual use case rather than assuming.

Prompt caching. Both Anthropic and OpenAI offer caching for repeated system prompts or long context (think: a large set of instructions or a document you reuse across many calls). Cached input tokens are billed at a steep discount — often 90% off — which matters a lot if your app sends the same large context repeatedly (RAG pipelines, agents with long tool definitions, multi-turn chat with a big system prompt).

Batch processing. If your workload isn't latency-sensitive (nightly summarization jobs, bulk classification, data enrichment), both platforms offer batch APIs at roughly half the standard rate. This is often the single biggest lever for cutting cost on non-interactive workloads.

A simple way to estimate your actual cost

Instead of comparing list prices directly, run this math for your own workload:

monthly_cost = (avg_input_tokens * input_rate +
                avg_output_tokens * output_rate)
               * requests_per_day * 30

Run 20-50 representative requests against each candidate model, log the actual input/output token counts (both APIs return usage data in every response), and plug real numbers into the formula. Token counts for the same prompt can differ meaningfully between model families because tokenizers aren't identical — this alone can shift the comparison by 10-20%.

Example request body with Claude's Messages API, where usage metadata comes back automatically:

curl https://api.anthropic.com/v1/messages \
  -H "x-api-key: $ANTHROPIC_API_KEY" \
  -H "anthropic-version: 2023-06-01" \
  -H "content-type: application/json" \
  -d '{
    "model": "claude-3-5-sonnet-latest",
    "max_tokens": 500,
    "messages": [{"role": "user", "content": "Summarize this ticket in 3 bullets."}]
  }'

The response includes usage.input_tokens and usage.output_tokens — log these across a sample of real requests before committing to a model tier.

When Claude tends to win on cost, and when GPT-4 does

If your workload is long-context (document analysis, large codebases, big RAG payloads with repeated context), Claude's caching combined with its mid-tier pricing often comes out ahead because the input side — which dominates long-context workloads — is where caching discounts hit hardest.

If your workload is high-volume, short-prompt, and latency-sensitive (chat support, quick classification, autocomplete-style features), the mini tiers on both sides are close enough that the deciding factor is usually output quality and latency rather than price.

For agentic workloads with heavy tool use, compare total round-trip cost, not just per-call cost — an agent that takes 3 tool-calling turns to finish a task costs 3x the token overhead of a single well-structured prompt. Claude's tool use format is documented at /docs/tools if you're estimating multi-turn agent costs on that side.

Where SubToAPI fits into the cost picture

If you land on Claude as your model and are moving from the Claude.ai subscription into programmatic access, SubToAPI turns your existing Claude access into a standard HTTPS API with its own key (sub_live_...), streaming support, and usage metadata per request — so you can see exactly how many input/output tokens each endpoint or team member is consuming without building that tracking yourself. Plans start at €9/month (Solo), with Team at €19/seat and Scale at €49/seat for larger usage and more seats — see /pricing. Setup takes a few minutes; the quickstart is at /docs/quickstart.

This doesn't change Anthropic's underlying token rates, but it does make the real cost of a Claude-based project easier to track across a team, which is often the missing piece when comparing "API pricing" in the abstract versus what you'll actually spend once multiple developers and production traffic are involved.

Questions

Is Claude API cheaper than GPT-4 API? It depends on the tier. Claude's mid-tier (Sonnet) and OpenAI's GPT-4o are priced similarly; Claude's top tier (Opus) is generally more expensive than GPT-4 Turbo. The cheapest tiers on both sides (Haiku vs. GPT-4o mini) are close, with GPT-4o mini often slightly cheaper on paper.

Does prompt caching change the comparison? Yes, significantly for long-context workloads. Both platforms discount cached input tokens heavily (often ~90%), so an app reusing a large system prompt or document context can see Claude or GPT-4 come out much cheaper than the base rate suggests — test with your actual prompts.

What's the best way to compare costs for my specific app? Don't rely on list prices alone. Run real requests against both APIs, log the usage fields returned in each response, and multiply by your actual daily volume. Token counts differ between tokenizers, so real measurement beats theoretical comparison every time.