The comparison most articles make is a one-liner: GPT-4o costs $2.50/$10 per million tokens (input/output), Claude Sonnet costs $3/$15. GPT-4o wins, next topic. That framing is wrong in practice because it assumes you are sending random tokens with no structure. Real workloads have a ratio of input to output tokens, a portion of stable context that can be cached, and sometimes a latency tolerance that unlocks batch pricing. Each of those dimensions shifts effective cost by 30 to 60 percent, and they interact.
The prices cited here are as of August 2025. LLM pricing has been declining; verify current rates at OpenAI pricing and Anthropic pricing before making budget decisions. For batch API pricing, see OpenAI batch processing and Anthropic batch API documentation.
GPT-4o pricing in practice
GPT-4o (as of August 2025) charges $2.50 per million input tokens and $10 per million output tokens, a 4:1 ratio. OpenAI applies automatic prompt caching on the longest repeated prefix of your request, discounting cached input tokens to $1.25 per million with no changes to your code or API calls. The batch API (async, up to 24-hour turnaround) cuts both input and output prices by 50%.
The key limitation is that GPT-4o's cache discount is a flat 50%. You cannot get more than that on input, regardless of how aggressively you structure your prompts. Automatic caching also requires that the cached prefix be at least 1,024 tokens, so short system prompts do not qualify. For workloads with large static context (RAG retrievals, long system prompts, document summarization with the same instructions), that 50% ceiling matters.
OpenAI is privately held with a deep integration partnership with Microsoft, which holds a significant equity stake and runs GPT-4o inference through Azure. If your organization has policies around vendor concentration or cloud provider dependency, this is worth noting. GPT-4o mini ($0.15/$0.60 per million tokens) covers the same API surface at a fraction of the cost if output quality is acceptable.
Claude pricing in practice
Claude Sonnet (as of August 2025) charges $3 per million input tokens and $15 per million output tokens, a 5:1 ratio. Prompt caching requires explicit opt-in: you mark cache breakpoints in your requests using cache_control: {"type": "ephemeral"}, which tells Anthropic to store that prefix. Cache writes cost $3.75 per million tokens (a 25% premium on regular input), and cache reads cost $0.30 per million tokens, a 90% discount versus the list rate.
That asymmetry is the crux of the Claude cost model. Writing the cache costs slightly more; reading it is dramatically cheaper than OpenAI's cached rate ($0.30 vs $1.25 per million). Workloads that hit a warm cache frequently, such as applications with a long system prompt that is identical across thousands of daily requests, can see effective input costs below $0.50 per million for the cached portion.
The limitation is operational: you have to manage cache breakpoints explicitly. If your system prompt changes frequently, or if your application does not hold enough volume to amortize the cache-write cost within the cache TTL (5 minutes by default, longer for certain use cases), caching can slightly increase your bill rather than reduce it. Anthropic has Amazon and Google as significant investors; Claude runs on AWS infrastructure and is available through Amazon Bedrock.
The workload scenarios that change the math
Three representative workload shapes cover most production use cases. All numbers below use the August 2025 prices stated above and assume a warm cache for scenarios with caching applied.
Scenario 1: Output-heavy (1K input / 3K output per call) Typical for chatbots generating long responses, code generation, and long-form drafting. The 75% output share amplifies the output price difference.
Scenario 2: Cache-heavy (5K cached system prompt + 200 token query + 500 token output) Typical for agent loops with a large, stable instruction set, or any app where the same lengthy context appears in every request.
Scenario 3: Extraction with stable context (8K cached document + 2K query + 100 token output) Typical for document Q&A, classification pipelines, and structured data extraction where most of the input is reusable context.
| Workload | GPT-4o (list) | GPT-4o (cached) | Claude Sonnet (list) | Claude Sonnet (cached) |
|---|---|---|---|---|
| Output-heavy (1K in / 3K out) | $0.0325/call | n/a | $0.0480/call | n/a |
| Cache-heavy (5K cached / 200 fresh / 500 out) | $0.01175/call | $0.01175/call | $0.0225/call | $0.0096/call |
| Extraction (8K cached / 2K fresh / 100 out) | $0.0210/call | $0.0160/call | $0.0315/call | $0.0099/call |
Reading the table: GPT-4o is 32% cheaper on output-heavy workloads at list price, and there is no caching lever to close that gap because the 75% output share dominates. For cache-heavy workloads, Claude's 90% cache-read discount flips the result: Claude ends up 18% cheaper than GPT-4o with caching enabled. For extraction with stable context, Claude is 38% cheaper once prompt caching is applied, despite listing at a higher base rate.
The crossover point is roughly when cached tokens make up more than 40% of your total input volume and your output-to-input ratio is below 1:1. Below that threshold, GPT-4o holds the cost advantage. Above it, Claude wins.
Batch pricing applies on top of these figures: both providers offer 50% off for async batch jobs. If your workload tolerates a 24-hour SLA, run a batch and apply the caching discount on top of that for Claude. The combined effect can reduce Claude Sonnet costs to around $0.005 per call for extraction workloads with heavy cache utilization.
The enforcement gap
Both providers offer dashboards showing spend after the fact. OpenAI's usage limits and Anthropic's API quotas are account-level controls set in advance, not per-user or per-project controls that adapt to real-time budget state. Neither provider can tell your application "this specific end user has spent $4.80 of their $5 monthly budget and the next call would exceed it" before the call fires.
This matters most in multi-tenant SaaS products, where cost attribution is per customer, not per account. If user A runs an unusually expensive session with a long context and high output volume, the cost lands on your API bill whether you intended to allow it or not. Post-hoc logging tells you what happened; it does not stop it.
| Layer | What it provides | Enforcement timing | Per-user limits |
|---|---|---|---|
| OpenAI (GPT-4o) | Model inference + usage logs | After the call | No |
| Anthropic (Claude) | Model inference + usage logs | After the call | No |
| noburn.dev | Pre-flight cost enforcement | Before the call fires | Yes |
The distinction is not about observability. Tools like Helicone, Langfuse, and LangSmith all log what was spent after the fact, which is useful for analysis. Pre-flight enforcement is a different mechanism: it estimates token cost client-side before the API call is made and blocks the request if the user or project is over budget.
FAQ
Does prompt caching work automatically with Claude?
No. Anthropic's prompt caching requires you to explicitly mark which parts of your prompt should be cached using cache_control blocks in the messages or system prompt. OpenAI's caching for GPT-4o is automatic on the longest repeated prefix, requiring no code changes, but the discount is capped at 50% versus Claude's 90% for cache reads.
Which model is better for RAG applications? For RAG pipelines where retrieved context is large and reused across many queries, Claude's caching economics are significantly better because the 90% cache-read discount applies to that large stable context chunk. GPT-4o auto-caching gives you 50% off the same tokens. If your retrieved context changes with every query (no stable prefix), this advantage disappears and GPT-4o's lower list prices apply.
Does switching models mid-project make sense based on cost alone? Rarely. The cost difference between Claude and GPT-4o on a given workload is 20-40% in the scenarios above. Switching models requires re-testing output quality, adjusting prompts (the models have different instruction-following behavior and formatting tendencies), and potentially rewriting integration code. The engineering cost of a migration usually exceeds six months of the pricing delta for anything below $5,000/month in API spend.
How does batch pricing interact with context caching? For Claude, batch discounts and caching discounts stack. A batch request reading from a warm cache pays 50% off the list cache-read price, bringing Claude Sonnet cache reads down to $0.15 per million tokens. For OpenAI, batch pricing also applies on top of cached input rates, bringing GPT-4o cached input to $0.625 per million tokens. The Claude number is still lower for cache-read-dominated workloads.
Is GPT-4o mini or Claude Haiku a better drop-in for cost reduction? GPT-4o mini ($0.15/$0.60 per million) and Claude Haiku ($0.80/$4.00 per million, as of August 2025) sit in different price tiers. GPT-4o mini is substantially cheaper at list rate. The same caching logic applies: Claude Haiku's cache-read discount still gives it an edge for high-cache-ratio workloads, but at these price points the absolute dollar differences are small. The decision should be driven by output quality for your specific task.
Where noburn fits in this stack
Neither OpenAI nor Anthropic provides per-user budget enforcement before an API call fires. noburn.dev sits between your application and the model provider: it estimates token cost client-side, checks it against the user's or project's remaining budget, and blocks the request if the limit would be exceeded, before any tokens reach the API. This works with both GPT-4o and Claude through native SDK wrappers for the OpenAI SDK, the Anthropic SDK, LiteLLM, LangChain, LangGraph, and the Vercel AI SDK. For multi-tenant SaaS products where you need to enforce per-customer spend limits and optionally pass usage costs through to Stripe billing, noburn handles both the enforcement and the billing side without requiring a separate metering service. The free tier covers 50,000 requests per month. Documentation and SDKs are at noburn.dev/docs.