"Claude or GPT" is still the first question every team building on an LLM API asks, and the answer usually arrives as vibes. Here are the numbers instead. I maintain a catalog of model prices and lifecycles, so this is the current frontier-flagship comparison, pulled from each provider's own pricing page.
The two flagships, side by side
| Claude Opus 4.8 (Anthropic) | GPT-5.5 (OpenAI) | |
|---|---|---|
| Input / 1M tokens | $5.00 | $5.00 |
| Output / 1M tokens | $25.00 | $30.00 |
| Cached input / 1M | $0.50 | $0.50 |
| Context window | 1,000,000 | 1,050,000 |
| Status | GA | GA |
The input price and the cached-input price are identical ($5.00 and $0.50). The context windows are effectively tied at ~1M tokens. The one number that differs is output: GPT-5.5 charges 20% more ($30 vs $25 per 1M).
Where that 20% actually matters
Output-heavy vs input-heavy workloads land in different places, so a single "which is cheaper" verdict is wrong. Two concrete monthly examples:
Input-heavy (RAG / long-context Q&A) — say 10M input + 2M output:
- Claude Opus 4.8: (10 × $5) + (2 × $25) = $100/mo
- GPT-5.5: (10 × $5) + (2 × $30) = $110/mo
Output-heavy (agents / code generation) — say 2M input + 8M output:
- Claude Opus 4.8: (2 × $5) + (8 × $25) = $210/mo
- GPT-5.5: (2 × $5) + (8 × $30) = $250/mo
So they're within ~10% on retrieval-style traffic, but Claude is ~16% cheaper once your workload is dominated by generated tokens — which is exactly the shape of agentic coding and long-form generation. If you're input-heavy, it's close enough that other factors (tokenizer, tool-use reliability, latency) decide it.
One caveat that trips everyone up: tokens aren't a shared unit
Price-per-token comparisons quietly assume both models count tokens the same way. They don't. Anthropic and OpenAI use different tokenizers, so the same paragraph of English can be a different number of tokens on each. A 5–15% difference in token count for the same text is normal, and it moves the real bill in the same direction as the price difference. Treat these figures as the starting point, then measure your own prompts on both.
The third option people skip: Google undercuts both
If you're anchored on "Claude vs GPT," you're comparing the two most expensive frontier options and ignoring the cheapest-per-quality tier:
| Model | Input / 1M | Output / 1M | Status |
|---|---|---|---|
| Gemini 3.1 Pro | $2.00 | $12.00 | Preview |
| Gemini 3.5 Flash | $1.50 | $9.00 | GA |
| Gemini 3.1 Flash-Lite | $0.25 | $1.50 | GA |
On that same input-heavy 10M-in/2M-out workload, Gemini 3.5 Flash costs ~$33/mo — roughly a third of either flagship — and Gemini 3.1 Flash-Lite is ~$5.50/mo. They won't match Opus 4.8 or GPT-5.5 on the hardest reasoning, but for classification, extraction, summarization, and most chat, paying frontier prices is a choice, not a requirement.
And if raw cost is the only axis, open-weight and Chinese-lab models go lower still — DeepSeek-V4-Flash is ~$0.14/$0.28, Llama 3.1 8B ~$0.02/$0.03.
The short version
- Input-heavy work: Opus 4.8 and GPT-5.5 are within ~10% — pick on capability/ergonomics, not price.
- Output-heavy work (agents, code): Claude Opus 4.8 is ~16% cheaper thanks to the $25 vs $30 output rate.
- Cost-sensitive work: don't default to either flagship — Gemini Flash is ~3× cheaper, and the value tier below that is another order of magnitude down.
- Always re-measure token counts on your own prompts; the per-token price is only half the bill.
I keep the full side-by-side (and every other pair) updated here: aimodelwatch.dev/compare/claude-opus-4-8-vs-gpt-5-5. Prices change and models get deprecated without much warning — there's a free email alert on the site if you want a heads-up when a model you use changes price or gets a retirement date.
Numbers verified 2026-07-05 against platform.openai.com and ai.google.dev. Spot a figure that's drifted? Tell me — accuracy is the whole point.
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