Claude Opus 4.7 vs GPT-5.5
Predict your real monthly bill. Toggle batch API and prompt caching to see how discounts and cache hits change the math for your exact workload. Pricing verified against official provider pages — May 2026.
Anthropic
Claude Opus 4.7
OpenAI
GPT-5.5
At an identical $5.00 input, this comes down to output volume and context length: pick <strong>Opus 4.7</strong> if your responses are long or your prompts exceed 400K tokens, pick <strong>GPT-5.5</strong> if you are already standardized on OpenAI's tooling and your outputs stay short.
The two models meet at $5.00 / 1M input, so the entire price gap lives on the output side: Opus 4.7 bills $25 against GPT-5.5's $30 per 1M output tokens. On a 2,000-in / 500-out request that is a 10% edge for Opus ($22,500/mo vs $25,000/mo at 1M requests, about $30k/year). But output is where the spread widens — flip to a generation-heavy 2K-in / 2K-out shape and the same 1M requests cost roughly $60,000 on Opus versus $70,000 on GPT-5.5, a $10k/month delta. The lighter your output, the less any of this matters; the chattier your model, the more Opus's $5 output discount compounds.
Batch halves both sides for either model (Opus 2.50/12.50, GPT-5.5 2.50/15.00), so it shifts the absolute numbers but not the verdict — the 20% output gap survives. Caching is where the mechanics diverge. Both read cached tokens at $0.50/1M, but Anthropic charges a one-time $6.25/1M cache write while OpenAI has no write fee. With a large, stable prefix reused across many calls, Opus's write premium is recovered after roughly two reads (each cached read saves $4.50 vs the $5.00 base rate), after which the two are effectively tied per cached token. See the caching ROI guide if your prefix turns over often. The real structural difference is context: Opus carries 1M tokens to GPT-5.5's 400K, so whole-repository or multi-document prompts that fit Opus in one call may force chunking or RAG on GPT-5.5 — a hidden cost that doesn't show up in the per-token table.
On capability, treat these as peer frontier models and resist any ranking we could hand you — we publish no benchmark scores and you should not trust ones that aren't from your own data. GPT-5.5's advantage is ecosystem gravity: Azure regions, the Assistants/Responses surface, fine-tuning, and the deepest third-party SDK coverage. Opus's advantage is the long window plus Claude's caching and tool-use behavior. Run both on 200-500 of your real prompts, measure quality, p95 latency, and the actual billed cost (model the swap here) before committing — the right answer is workload-specific, not a leaderboard.
Cost Calculator
Pricing snapshot (as of May 2026)
The table below shows per-1M-token rates sourced from the official Anthropic and OpenAI pricing pages, last verified on 21 May 2026. All figures are in USD.
| Rate type | Claude Opus 4.7 | GPT-5.5 |
|---|---|---|
| Input (standard) | $5.00 | $5.00 |
| Output (standard) | $25.00 | $30.00 |
| Input (batch) | $2.5000 | $2.5000 |
| Output (batch) | $12.5000 | $15.0000 |
| Cache write | $6.2500 | — |
| Cache read | $0.5000 | $0.5000 |
| Context window | 1000K | 400K |
Sources: https://platform.claude.com/docs/en/about-claude/pricing · https://developers.openai.com/api/docs/pricing
When Claude Opus 4.7 is the better pick
Choose Claude Opus 4.7 for output-heavy or long-context work — agentic coding over whole repositories, long-document synthesis, or any pipeline whose prompts exceed 400K tokens — where the $25 output rate and 1M window cut both cost and chunking complexity.
- Input rate: $5.0000/1M tokens (standard)
- Output rate: $25.0000/1M tokens (standard)
- Batch API available: 50% off — input $2.5000/1M, output $12.5000/1M
- Prompt caching: reads at $0.5000/1M, writes at $6.2500/1M
- Context window: 1000K tokens
When GPT-5.5 is the better pick
Choose GPT-5.5 when you are already invested in OpenAI's stack (Azure data residency, fine-tuning, Assistants/Responses, mature SDKs) and your workloads are input-heavy with short outputs, where the $30 output premium barely registers.
- Input rate: $5.0000/1M tokens (standard)
- Output rate: $30.0000/1M tokens (standard)
- Batch API available: 50% off — input $2.5000/1M, output $15.0000/1M
- Prompt caching: reads at $0.5000/1M (automatic, no write fee)
- Context window: 400K tokens
Real-world example: 1M requests/month at 2K input + 500 output tokens
Assume a production workload of 1 million API calls per month, each consuming 2,000 input tokens and generating 500 output tokens. This is a realistic profile for a mid-size SaaS product with active users across time zones — a customer-support bot, a document-analysis pipeline, or an AI-assisted search feature.
Scenario A — Standard pricing, no optimisations:
- Claude Opus 4.7: (2,000 × $5.0000 + 500 × $25.0000) ÷ 1,000,000 × 1,000,000 = $22,500.00/month
- GPT-5.5: (2,000 × $5.0000 + 500 × $30.0000) ÷ 1,000,000 × 1,000,000 = $25,000.00/month
At this volume and token mix, Claude Opus 4.7 is 10% cheaper than the alternative on standard rates — a difference of $2,500.00/month. Over a full year that compounds to $30,000.00 in savings, which is meaningful even before factoring in batch or caching optimisations.
Scenario B — Batch API enabled (50% off, where supported):
- Claude Opus 4.7 batch: $11,250.00/month (saving $11,250.00 vs. standard)
- GPT-5.5 batch: $12,500.00/month (saving $12,500.00 vs. standard)
The batch API is well-suited for nightly analytics pipelines, content moderation queues, data-labelling jobs, and any workload that can tolerate asynchronous processing with up to 24-hour turnaround. It is incompatible with real-time interactive use cases such as customer-facing chat or streaming completions.
Use the interactive calculator above to model your specific token mix, request volume, and caching strategy. Real production costs typically run 10–30% above median estimates due to prompt variability, retry logic, and usage spikes.
Migration considerations
Switching between Claude Opus 4.7 and GPT-5.5 is not always a drop-in model swap. Differences in API shape, prompt conventions, tokeniser behaviour, and context-window limits can require non-trivial engineering work. Here is what to audit before migrating production traffic.
- Move your top-level
systemparameter intomessages[0]withrole: "system". - Switch from XML-style prompt delimiters to markdown formatting — GPT-4o is tuned on headers, bullet points, and code fences.
- Remove
cache_controlbreakpoints from your request body; OpenAI applies prompt caching automatically on eligible repeated prefixes with no explicit configuration. - Mind the context-window change between the two models (shown in the pricing table above): if it shrinks, reintroduce chunking or RAG for long payloads; if it grows, you can simplify that logic.
- Always test on your own production distribution rather than relying solely on public benchmarks, which measure average performance across diverse tasks that may not match your use case.
Frequently asked questions
Which is cheaper at 1M requests/month — Claude Opus 4.7 or GPT-5.5?
At 1M requests/month with 2,000 input tokens and 500 output tokens per request, Claude Opus 4.7 costs $22,500.00 versus GPT-5.5 at $25,000.00 — a difference of $2,500.00 per month (10%). Enabling the batch API (where available) cuts those figures by 50% for workloads that tolerate up to 24-hour turnaround.
Does Claude Opus 4.7 or GPT-5.5 support batch API pricing?
Both Claude Opus 4.7 and GPT-5.5 support batch API pricing at 50% off standard rates, in exchange for up to 24-hour result latency. Claude Opus 4.7 batch input is $2.50/1M and batch output is $12.50/1M. GPT-5.5 batch input is $2.50/1M and batch output is $15.00/1M. Batch is well-suited for nightly analytics, content moderation queues, embedding generation, and any workload that can tolerate asynchronous processing.
How does prompt caching compare between Claude Opus 4.7 and GPT-5.5?
Claude Opus 4.7 supports prompt caching with cache reads at $0.5/1M and cache writes at $6.25/1M. GPT-5.5 supports prompt caching with cache reads at $0.5/1M (no separate write fee). Prompt caching delivers the largest savings when you have a large, stable system prompt reused across thousands of requests per day — a 50,000-token knowledge-base system prompt reused 10,000 times can cut input costs by 80–90%.
Which model has lower latency — Claude Opus 4.7 or GPT-5.5?
Latency depends on region, time of day, request size, and infrastructure routing — not just model architecture. In general, smaller models (the lower-priced model in this pair) tend to return the first token faster because they require fewer compute cycles per forward pass. For latency-critical production workloads, benchmark with your own representative prompt and output length distribution using p50/p95/p99 metrics rather than synthetic averages. Provider infrastructure also varies: OpenAI has more global edge regions via Azure, while Google Vertex AI and Anthropic offer fewer but growing geographic options.
Can I trust this calculator for production budgeting?
This calculator uses pricing verified against the official provider pricing pages as of May 2026. It is suitable for planning and estimating monthly spend. For production budgets, always cross-check against your provider dashboard, account for any committed-use discounts or enterprise pricing you have negotiated, and add a 10–20% buffer for unexpected usage spikes. Token counts in the calculator are per-request estimates — actual production variance (longer user queries, retry logic, error recovery) can push real costs 15–30% above median estimates.