Claude Sonnet 4.6 vs GPT-5.4
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 Sonnet 4.6
OpenAI
GPT-5.4
These two are close enough that price alone won't decide it: pick <strong>GPT-5.4</strong> if your prompts already fit in 400K and you want the simpler no-write-fee caching model, and pick <strong>Claude Sonnet 4.6</strong> if you need the 1M context window or run a large reused system prefix that its cheap, explicit cache reads can amortize.
The headline gap is small and lives entirely on the input side. Output is identical at $15.00/1M, so the only difference is input: $3.00 (Sonnet) vs $2.50 (GPT-5.4). On a typical 2,000-in / 500-out request that's $0.0135 vs $0.0125 each — at 1M requests/month, $13,500 vs $12,500, a flat $1,000 (about 7%) in GPT-5.4's favor. Batch halves both: $6,750 vs $6,250 for the same volume, same $500/mo gap. Because output dominates the bill here (the 500 output tokens cost $7,500 of each model's total), shrinking the input price barely moves the needle. If your traffic is output-heavy, treat these two as a tie and decide on capability instead.
Caching is where the structural difference shows. GPT-5.4 charges no cache-write fee and reads cached tokens at $0.25/1M; Sonnet 4.6 charges a one-time write premium of $3.75/1M but then reads at $0.30/1M. For a stable 50K-token system prefix reused thousands of times a day, both collapse input cost by roughly 90% versus re-sending it raw — GPT-5.4 is marginally cheaper per read and avoids the write penalty, so for hot, frequently-reused prefixes it edges ahead. Sonnet's caching only needs the prefix to be reused enough times to earn back that write fee, which happens fast at scale. See prompt-caching ROI and batch API for the breakeven math, or model your own numbers in the migration simulator.
Two things tilt the comparison beyond the rate card. First, context: Sonnet 4.6 carries a 1M-token window vs GPT-5.4's 400K, so whole-repo reasoning, long transcripts, or large document sets fit without chunking — and Claude tokenizes English at roughly 0.8x the GPT count, so the same text is fewer billed tokens, partly offsetting the higher input rate. Second, ecosystem: GPT-5.4 plugs into OpenAI's tooling, JSON-schema outputs, and the widest SDK coverage, while Sonnet is the stronger default for agentic coding and XML-structured prompts. We publish no benchmark scores here on purpose — capability claims age badly. Run both on a few hundred of your own production prompts (count your real token mix first) before committing; a 7% price gap is noise next to a quality or retry-rate difference on your actual workload.
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 Sonnet 4.6 | GPT-5.4 |
|---|---|---|
| Input (standard) | $3.00 | $2.50 |
| Output (standard) | $15.00 | $15.00 |
| Input (batch) | $1.5000 | $1.2500 |
| Output (batch) | $7.5000 | $7.5000 |
| Cache write | $3.7500 | — |
| Cache read | $0.3000 | $0.2500 |
| Context window | 1000K | 400K |
Sources: https://platform.claude.com/docs/en/about-claude/pricing · https://developers.openai.com/api/docs/pricing
When Claude Sonnet 4.6 is the better pick
Choose Claude Sonnet 4.6 when inputs exceed 400K tokens (whole codebases, long transcripts, large document bundles) or when agentic coding and XML-structured prompting are core to your pipeline — and its 1M window plus ~0.8x tokenization quietly narrows the input-price gap on text-heavy work.
- Input rate: $3.0000/1M tokens (standard)
- Output rate: $15.0000/1M tokens (standard)
- Batch API available: 50% off — input $1.5000/1M, output $7.5000/1M
- Prompt caching: reads at $0.3000/1M, writes at $3.7500/1M
- Context window: 1000K tokens
When GPT-5.4 is the better pick
Choose GPT-5.4 when your context comfortably fits in 400K and you want the lower input rate, no cache-write fee, and the broadest OpenAI tooling/SDK ecosystem — especially for output-light, high-volume request patterns where its ~7% input edge actually compounds.
- Input rate: $2.5000/1M tokens (standard)
- Output rate: $15.0000/1M tokens (standard)
- Batch API available: 50% off — input $1.2500/1M, output $7.5000/1M
- Prompt caching: reads at $0.2500/1M (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 Sonnet 4.6: (2,000 × $3.0000 + 500 × $15.0000) ÷ 1,000,000 × 1,000,000 = $13,500.00/month
- GPT-5.4: (2,000 × $2.5000 + 500 × $15.0000) ÷ 1,000,000 × 1,000,000 = $12,500.00/month
At this volume and token mix, GPT-5.4 is 7% cheaper than the alternative on standard rates — a difference of $1,000.00/month. Over a full year that compounds to $12,000.00 in savings, which is meaningful even before factoring in batch or caching optimisations.
Scenario B — Batch API enabled (50% off, where supported):
- Claude Sonnet 4.6 batch: $6,750.00/month (saving $6,750.00 vs. standard)
- GPT-5.4 batch: $6,250.00/month (saving $6,250.00 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 Sonnet 4.6 and GPT-5.4 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 Sonnet 4.6 or GPT-5.4?
At 1M requests/month with 2,000 input tokens and 500 output tokens per request, GPT-5.4 costs $12,500.00 versus Claude Sonnet 4.6 at $13,500.00 — a difference of $1,000.00 per month (7%). Enabling the batch API (where available) cuts those figures by 50% for workloads that tolerate up to 24-hour turnaround.
Does Claude Sonnet 4.6 or GPT-5.4 support batch API pricing?
Both Claude Sonnet 4.6 and GPT-5.4 support batch API pricing at 50% off standard rates, in exchange for up to 24-hour result latency. Claude Sonnet 4.6 batch input is $1.50/1M and batch output is $7.50/1M. GPT-5.4 batch input is $1.25/1M and batch output is $7.50/1M. 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 Sonnet 4.6 and GPT-5.4?
Claude Sonnet 4.6 supports prompt caching with cache reads at $0.3/1M and cache writes at $3.75/1M. GPT-5.4 supports prompt caching with cache reads at $0.25/1M (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 Sonnet 4.6 or GPT-5.4?
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.