I've shipped production features against both APIs in the same week. Here's what I actually learned — not a benchmark chart, but the decisions you'll face when it's real code and real costs.
The headline differences that actually matter
The specs you'll read elsewhere: Claude's context window is enormous (up to 200k tokens on Sonnet), OpenAI's GPT-4o sits at 128k. Claude tends to follow complex, multi-constraint instructions more reliably; GPT-4o has a larger third-party ecosystem and more examples in the wild. Both support streaming, tool use, and vision. Neither is universally better.
What the specs don't tell you: the character of each model's output is different enough that you can feel it in user testing. Claude produces longer, more cautious, better-structured prose by default. GPT-4o is snappier and more willing to give you a short answer when that's what you asked for. For a chat interface that's a real difference in perceived quality.
Pricing in practice
Both APIs price on input + output tokens, and both have tiered models from cheap-and-fast to expensive-and-capable. The number that matters more than the list price is your input/output ratio.
Claude's large context window makes it attractive for document-heavy workflows — summarising a 100-page PDF in one call rather than chunking it. But long context calls also mean long input token counts, so price-per-call climbs fast if you're stuffing the whole context every time. Prompt caching (both providers offer it, with different mechanics) is the lever that makes large-context calls affordable in production — you pay once to cache a long system prompt or document, then a fraction of that on every subsequent call that hits the cache.
For apps where most calls are short (chat, classification, short-form generation), the per-token difference between providers is small enough that latency and reliability matter more than price.
Tool use and structured output
Both APIs support tool/function calling, but the implementation details diverge. OpenAI's function calling has been around longer and the community has built a lot of abstractions on top of it. Claude's tool use is well-designed and expressive — the tool_use / tool_result message structure is clean to work with — but you'll find fewer pre-written recipes when something goes wrong.
For structured output specifically (making sure the model returns valid JSON in a schema you control), OpenAI has a response_format: {type: "json_schema"} mode that's worth knowing about. Claude's equivalent is tool calling with a schema — you define a tool that the model "calls" and the input is your structured output. Both work; the OpenAI approach is slightly more ergonomic when you just want a JSON blob.
Streaming
Both support SSE-based streaming with similar event shapes. The practical difference I noticed: Claude's streaming can have a longer time-to-first-token on complex prompts, because it tends to think longer before starting to output. OpenAI is faster out of the gate on most prompts. In a UI where you're showing a typing effect, TTFT is what users perceive — a 200ms difference is visible.
For internal pipelines where users aren't watching tokens arrive, this doesn't matter.
Rate limits and reliability
OpenAI has a more granular, well-documented rate-limit tier system. If you're scaling fast, the path from free tier to enterprise limits is well-charted. Anthropic's limits are competitive but the tooling around monitoring and requesting increases is less mature.
Both providers have had reliability incidents. Neither is something you should rely on for 99.9% SLA without a fallback. This is the strongest argument for a provider abstraction layer in your codebase — not for switching models constantly, but for having a fallback key when your primary provider has a bad hour.
When I reach for Claude
- Long documents: summarisation, extraction, analysis over content that won't fit in 128k. The 200k window genuinely changes what's possible.
- Instruction-heavy prompts: when my system prompt is 2,000 words of nuanced rules, Claude follows them more consistently.
- Writing quality matters: Claude's prose output is noticeably more polished, which matters when users see the output directly.
When I reach for OpenAI
- Ecosystem integrations: more third-party tools, more example code, more Stack Overflow answers for when something breaks.
- Speed-first chat: lower TTFT for conversational UI.
- Structured output with JSON schema: the
json_schemaresponse format is a nice ergonomic. - GPT-4o multimodal: vision + text + audio in one model when you need it.
The answer for a new project
Don't pick one and lock in hard. Build a thin provider abstraction — a function that calls either API behind the same interface — and start with whichever fits your first use case. You'll swap providers for specific features within six months; the bet that ages well is the abstraction, not the API.
If you're forced to pick: Claude for anything document-intensive or instruction-heavy, OpenAI for a conventional chat product where ecosystem maturity matters.
FAQ
Is Claude more expensive than GPT-4o? It depends on the call. For short calls, pricing is competitive. For long-context calls where you're sending thousands of tokens of input, enable prompt caching — both providers offer it, and it's the difference between an expensive call and a cheap one.
Can I use both APIs in the same app? Yes — and you should design for it from the start. A simple adapter interface over both clients lets you route different features to the best model without rewriting call sites.
Does Claude support function calling? Yes, as tool use. The API shape is slightly different from OpenAI's but well-documented. Any framework like Vercel AI SDK abstracts both behind one interface.
Which has better uptime? Both have had incidents. Don't depend on either for hard SLA without a provider-level fallback.