Claude API vs OpenAI API: A Real Cost Comparison
The short answer
At the model tier most teams actually use in production — Claude 3.5 Sonnet versus GPT-4o — the two APIs are close enough in price per token that the choice usually comes down to task fit, not cost. Where the real cost gaps show up are at the extremes: Claude's smaller models (Haiku) and OpenAI's smaller models (GPT-4o mini) are both dramatically cheaper than their flagship siblings, and the top-tier reasoning models (Claude Opus, OpenAI o1) cost several times more than the mid-tier options. The cheapest API call for a given task is rarely the flagship model from either vendor — it's picking the smallest model that still meets your quality bar.
This article breaks down the per-token numbers, the hidden costs (context, caching, output weighting), and gives a worked example so you can run your own numbers instead of trusting a single headline figure.
Pricing by model tier
Prices below are per million tokens, input / output, at the time of writing. Both Anthropic and OpenAI change pricing periodically, so treat this as a comparison framework rather than a locked-in number — always check the official pricing pages before budgeting a large workload.
Claude (Anthropic):
- 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
OpenAI:
- GPT-4o mini: ~$0.15 input / $0.60 output
- GPT-4o: ~$2.50 input / $10.00 output
- o1: ~$15.00 input / $60.00 output
Two things jump out immediately:
- OpenAI's mini tier is cheaper than Claude's equivalent small model. GPT-4o mini undercuts Claude 3.5 Haiku by roughly 5x on input and 6x on output.
- At the flagship/reasoning tier, pricing converges. Claude Opus and OpenAI o1 land in a similar band, with Opus slightly cheaper on output and o1 cheaper on input.
If your workload is high-volume and low-complexity (classification, extraction, short chat responses), the mini-tier gap matters a lot. If your workload is low-volume and high-complexity (long-form reasoning, code generation, multi-step agents), the flagship-tier convergence matters more — and quality differences will likely outweigh the price difference anyway.
Output tokens cost more than input tokens — on both platforms
A detail that trips up a lot of cost estimates: output tokens are priced 4–6x higher than input tokens on both Claude and OpenAI. This means a prompt-heavy, response-light workload (e.g., document summarization with a short summary) is much cheaper per call than a prompt-light, response-heavy workload (e.g., long-form content generation).
Practically, this means your cost model should weight generated tokens more heavily than you might intuitively expect. A system that generates verbose responses — explanations, chain-of-thought, repeated formatting — will cost noticeably more than one that's tuned to produce concise output, even with identical input sizes.
Prompt caching changes the math
Both vendors offer prompt/context caching that discounts repeated input tokens significantly — often 90% or more off the standard input rate for cached portions of a prompt. If your application sends the same system prompt, tool definitions, or reference documents on every call (a common pattern for RAG and agent systems), caching can be the single biggest lever in your cost comparison, bigger than the base per-token rate difference between vendors.
When comparing costs between Claude and OpenAI for a real workload, don't just multiply token counts by list price — check whether your usage pattern benefits from caching on each platform, and factor that discount in before concluding one is cheaper than the other.
A worked example
Assume a workload of 10,000 requests/month, each with 1,500 input tokens and 500 output tokens, no caching:
- Claude 3.5 Sonnet: (1,500 × 10,000 × $3/1M) + (500 × 10,000 × $15/1M) = $45 + $75 = $120/month
- GPT-4o: (1,500 × 10,000 × $2.50/1M) + (500 × 10,000 × $10/1M) = $37.50 + $50 = $87.50/month
- Claude 3.5 Haiku: (1,500 × 10,000 × $0.80/1M) + (500 × 10,000 × $4/1M) = $12 + $20 = $32/month
- GPT-4o mini: (1,500 × 10,000 × $0.15/1M) + (500 × 10,000 × $0.60/1M) = $2.25 + $3 = $5.25/month
At this volume, dropping to the mini tier on either platform saves far more than switching vendors at the flagship tier does. This is the pattern worth internalizing: tier selection, not vendor selection, is usually the bigger cost lever.
Beyond list price: how you're billed matters too
Raw per-token pricing is only part of the real cost. Also factor in:
- Minimum billing increments and rate limits that force retries or over-provisioning.
- Multiple API keys and billing dashboards if your team uses both vendors for different tasks — this adds operational overhead that doesn't show up in a token-cost spreadsheet.
- Flat-fee access models. If you or your team already has Claude subscription access, routing it through a wrapper like SubToAPI gives you an HTTPS API with application keys, streaming, and usage metadata on a flat per-seat plan (from €9/month), rather than metered pay-per-token billing. For teams with predictable Claude usage, that can be cheaper and simpler to forecast than token-metered API billing — see /pricing for the plan breakdown and /docs/quickstart to see how the API surface works.
When cost shouldn't decide it
Cost comparisons matter, but they're not the only axis. Claude models tend to be favored for long-context work, careful instruction-following, and tool use with structured outputs (see /docs/tools). OpenAI models have a broader ecosystem of fine-tuning options and a very cheap mini tier for high-volume simple tasks. If your workload is mixed, many teams end up running both — routing cheap, high-volume tasks to whichever mini model is cheapest, and reserving flagship models for the subset of requests that actually need the extra capability.
Questions
Is Claude cheaper than OpenAI overall? Not universally. OpenAI's mini-tier models are cheaper than Claude's equivalent small models, while Claude Opus and OpenAI's top reasoning model are priced similarly. The cheaper choice depends on which model tier your task actually requires.
Does prompt caching make a big difference in cost comparisons? Yes — for workloads that repeat system prompts, tool definitions, or reference documents across calls, caching discounts on cached input tokens can outweigh the base price difference between vendors entirely.
Should I pick a model based on cost or capability? Start with capability: find the smallest model from either vendor that reliably meets your quality bar, then compare costs within that tier. Picking cheap-but-insufficient models usually costs more in retries, fallback logic, and lost output quality than it saves.