OpenAI vs Anthropic: Complete Cost Breakdown

OpenAI and Anthropic both sell a ladder of models from cheap-and-fast to expensive-and-capable, and the actual dollar gap between them depends far more on which rung you pick from each ladder than on which company you pick. This is a straight price comparison of their current API lineups, with the token math worked out for a few realistic workload shapes — not a claim about which provider is "better," which this piece doesn't attempt to measure.

The Current Lineups, Side by Side

Both providers publish several tiers rather than one model, roughly spanning nano/mini, mid-tier, and flagship-or-reasoning classes. Prices below are pulled live from the same catalog that powers our calculators, so this table can't drift out of sync with the rest of the site:

A few things worth noticing before the worked examples: Anthropic's output-to-input price ratio is consistently 5x across its lineup ($1 → $5 on Claude Haiku 4.5, $5 → $25 on Claude Opus 5), while OpenAI's ratio varies more by model — $0.15 → $0.6 on GPT-4o mini (4x) versus $10 → $50 on GPT-6 Astra (5x). Neither pattern is inherently cheaper — it just means the provider comparison flips depending on your input:output token ratio, which is exactly why the worked scenarios below use different ratios on purpose.

Worked Cost Examples

Three workload shapes, each with its token assumptions stated up front so you can swap in your own numbers. These are illustrative assumptions, not measurements of any real deployment.

Scenario A: High-Volume Support Chatbot

Assumptions: 50,000 conversations/month, 400 input tokens and 150 output tokens per conversation (a system prompt plus a few turns of history against a short customer question and answer).

• GPT-4o mini: $7.50/month
• Claude Haiku 4.5: $57.50/month
• GPT-4.1 mini: $20.00/month
• Claude Sonnet 5: $115.00/month

At this token ratio (mostly input, short output), the nano/mini-class models from both providers land closest together — the gap between GPT-4o mini and Claude Haiku 4.5 here is $50.00/month, small relative to the mid-tier options.

Scenario B: Long-Document Analysis

Assumptions: 2,000 calls/month, 40,000 input tokens (a lengthy document or report) and 1,200 output tokens (a structured summary) per call — the opposite ratio from Scenario A, dominated by input volume.

• GPT-4.1: $179.20/month
• Claude Sonnet 4.6: $276.00/month
• GPT-4o: $224.00/month
• Claude Opus 5: $460.00/month
• Claude Opus 4.6: $460.00/month

Input-heavy workloads amplify the input price specifically, which is why a model with a low input price but a steep output multiplier (most Anthropic models) can still come out ahead here even though it looked more expensive in an output-heavy scenario.

Scenario C: Agentic Coding Task

Assumptions: 5,000 calls/month, 3,000 input tokens (codebase context, tool results) and 2,500 output tokens (generated code and reasoning) per call — a roughly even ratio, weighted toward output, typical of multi-step coding or agent tasks. See our agent cost breakdown for how tool-call loops compound this further.

• o4-mini (Reasoning): $71.50/month
• Claude Sonnet 5: $155.00/month
• o3 (Reasoning): $130.00/month
• Claude Fable 5.1: $775.00/month

Reasoning-tier models on both sides bill their internal reasoning as output tokens, so the 2,500-token assumption above is likely conservative for genuinely hard tasks — see LLM pricing explained for the reasoning-token mechanics in detail.

When Each Provider's Tiers Make Sense

• Short, high-volume, low-complexity turns (classification, routing, simple chat replies) — the nano/mini tiers on either side are usually interchangeable on cost; the deciding factor is more likely to be latency or how the model handles your specific prompt format than price.
• Input-heavy workloads (long documents, large tool-call contexts, RAG over big chunks) — the input price matters more than the output price, so re-run the comparison weighted toward input tokens rather than trusting an output-heavy scenario like Scenario A above.
• Output-heavy workloads (long-form generation, verbose code, detailed reports) — the output price and the model's tendency toward verbosity both matter; a model that's cheaper per output token but writes longer responses can lose its advantage in practice.
• Multi-step reasoning and agentic tasks — both providers offer a reasoning-oriented tier (o3 (Reasoning)/o4-mini (Reasoning) on OpenAI, Claude Fable 5.1 on Anthropic) priced well above their standard tiers; budget for wider variance here since reasoning token volume depends on problem difficulty, not prompt length.
• Mixed workloads — most real products run several of the above simultaneously. Consider routing simple requests to a cheap model and escalating only the harder slice, as covered in how to pick the cheapest model that's still good enough.

None of this is a statement about which provider produces better answers for your task — that's workload-specific, changes as both companies ship new models, and isn't something this post measures. Run your own quality check against a sample of your real inputs before committing to either lineup.

Run This Comparison on Your Own Numbers

The scenarios above use assumed token counts to illustrate the mechanics. To get a number that actually matches your product:

1. 1. Open the Token Cost Calculator. Enter your real input and output token counts per request and your expected request volume, then compare the resulting monthly cost across every model in the catalog at once — including every OpenAI and Anthropic tier from the table above.
2. 2. Shortlist candidates in the Model Selector. Pick two or three models you're actually deciding between — say one from each provider's mid-tier — and compare them side by side on the same volume assumptions, without re-entering your numbers for each one.
3. 3. Re-run with your actual input:output ratio. If your product does both short chat turns and occasional long-document calls, run the calculators once for each shape rather than averaging them into one misleading blended number — as Scenarios A and B above show, the cheaper provider can flip depending on the ratio.

Caveats

• Prices change. Both providers have adjusted pricing and shipped new tiers multiple times in the past year. Treat every number on this page as a snapshot, not a guarantee — the table above reflects our catalog as of the date at the top of this post.
• Verify on the provider's own page before budgeting a production workload. Check OpenAI's pricing page and Anthropic's pricing page directly for the current, authoritative rates, including any regional, volume, or long-context surcharge details not captured in a simple per-1M-token table.
• Quality differences are workload-specific and not measured here. This post compares list prices and token math only. It makes no claim about which model answers better on your task — that requires running your own eval against your own data, as covered in how to pick the cheapest model that's still good enough.
• Prompt caching and batch discounts aren't reflected in the scenarios above. Both providers offer reduced rates for cached or batched requests; if your workload fits either pattern, your real bill will likely be lower than these standard-rate estimates — see LLM pricing explained for how those discounts work.

FAQ

Is Anthropic always more expensive than OpenAI, or the other way around?

Neither, consistently. Both providers span a wide price range across their own tiers, and the cheaper choice between the two depends on which tier you compare and on your input:output token ratio, not on the provider name. Comparing one company's flagship model to the other's budget model — a common mistake in informal comparisons — will always favor whichever side you picked the cheap model from, which tells you nothing useful. Always compare tiers of similar intended capability, and re-run the comparison on your own token ratio rather than trusting a single published ratio like the ones used in the scenarios above.

Should I pick a provider and standardize on it, or mix both?

Plenty of production systems use both providers for different tasks — a cheap model from whichever provider is cheaper for the input:output ratio of each specific task, rather than a single default carried across every call. The operational cost of maintaining two API integrations is real but usually small once the abstraction is written once; most teams find it's worth it once monthly spend is more than a token amount. Below that, the simplicity of a single provider and a single API surface is often worth more than the marginal savings.

Does context window size affect which provider is cheaper?

Not directly — you're billed for tokens actually sent and received on each call, not for the size of the context window a model supports. A larger context window mainly changes what you're able to send, which indirectly affects cost only if it leads you to send more input per call than you otherwise would. See LLM pricing explained for more on this distinction.

How often should I redo this comparison?

Whenever either provider ships a new model in the tier you're using, and at minimum once a quarter — pricing on both sides has moved more than once in the past year, and a comparison that was decisive six months ago may no longer hold. Bookmark the Token Cost Calculator rather than this post's numbers if you want to stay current — it always reflects the latest prices in our catalog.

For the mechanics behind every number on this page, see LLM pricing explained.