GPT vs Claude: Which is Cheaper?
Side-by-side cost comparison based on your actual usage pattern.
GPT-5.6 Terra
U$ 80.00/mo
Claude Sonnet 5
U$ 105/mo
How this comparison works
This tool runs the same usage pattern — your input tokens, output tokens, and requests per month — through both OpenAI's and Anthropic's pricing to show which model is cheaper for your specific workload. Cost comparisons based on list price alone can be misleading, because the two providers don't always compete at the same capability tier: comparing GPT-5.6 Luna to Claude Haiku is a fairer fight than comparing GPT-5.6 Luna to Claude Sonnet. Use the breakdown to see not just which model is cheaper, but by how much, so you can decide whether the price gap is worth a difference in output quality.
Compare tiers, not brands
The most common mistake in GPT vs Claude comparisons is crossing capability tiers: putting GPT-5.6 Luna against Claude Sonnet makes OpenAI look dramatically cheaper, and putting GPT-5.6 Terra against Claude Haiku does the reverse. The fair pairings are budget tier against budget tier — GPT-5.6 Luna versus Claude Haiku — and workhorse against workhorse, GPT-5.6 Terra versus Claude Sonnet. Within a matched tier the per-token gaps narrow considerably, and the decision starts to hinge on your workload's shape: output-heavy workloads amplify differences in output rates, while input-heavy ones (RAG, summarization, long documents) make the input rate and caching discounts the numbers that matter. To price each side of a matched pair in isolation, use the OpenAI and Claude calculators with the same token profile.
Same prompt, different token counts
Per-token prices are not directly comparable, because the two providers tokenize text differently. The same prompt can produce a noticeably different token count on OpenAI's tokenizer than on Anthropic's, so a model that looks cheaper per million tokens can bill more tokens for identical traffic. Default behavior differs too: without explicit length instructions, the models produce answers of different verbosity, and those extra tokens are billed at each provider's output rate — the expensive one.
This is why the honest unit of comparison is cost per request under your real prompts, not list price per million tokens. Paste a representative prompt into the token calculator to see the count and cost side by side across models before trusting any headline number.
A fair evaluation recipe
When the price gap looks meaningful, validate it in four steps. Fix a representative set of real tasks from your product. Cap output length identically on both models so verbosity doesn't contaminate the result. Measure tokens per completed task rather than per request — retries and follow-up corrections count against the model that caused them. Then compute cost per successful task, not cost per token: a model that is 20% cheaper per token but fails twice as often is the expensive option. Switching providers also has a one-time cost — prompts need rework, evals need rebuilding — so small gaps rarely justify a migration, while a consistent 2-3x gap on your matched tier usually does. For the full tier-mapping and cost-per-task framework, see Claude Sonnet 5 vs GPT-5.6: a fair cost comparison; if budget models are on your shortlist, the cheapest LLM APIs of 2026 and the real cost of DeepSeek cover that end of the table.
Three workloads run on both families
A high-volume classifier. 800 tokens in, 50 out, 100,000 calls a month — 80M input and 5M output tokens. On GPT-5.6 Luna ($0.20 / $1.20 per million) that is $16 + $6 = $22. On Claude Haiku 4.5 ($1.00 / $5.00) it is $80 + $25 = $105. Before the 30 July repricing this pair was a tie at roughly $110 each; Luna is now about 4.8× cheaper, which is large enough that it is worth re-running your evaluation rather than assuming the old parity still holds.
A drafting assistant. 2,000 tokens in, 1,200 out, 30,000 calls — 60M input, 36M output. GPT-5.6 Terra ($2.00 / $12.00): $120 + $432 = $552. Claude Sonnet 5 ($3.00 / $15.00): $180 + $540 = $720, or about $480 while the launch promo runs at $2.00 / $10.00. So Terra is the cheaper workhorse at list price, but Sonnet's promo still wins until it expires on 31 August 2026 — a genuinely temporary ranking.
A long-document analyst. 150,000 tokens in, 2,000 out, 500 calls — 75M input, 1M output. GPT-5.6 Sol ($5.00 / $30.00): $375 + $30 = $405. Claude Opus 4.8 ($5.00 / $25.00): $375 + $25 = $400. Nearly identical, but only GPT-5.6 could take a document twice that size in one call. Model your own shape in the OpenAI and Claude calculators.
Frequently asked questions
Is the cheaper model always the better choice?
Not necessarily. Price is only one factor — output quality, reasoning ability, context window size, latency and tooling support all matter. Use this comparison as a cost input to a broader decision, not the only criterion.
Why do the price gaps change so often?
Both OpenAI and Anthropic adjust pricing as they release new model versions or tiers. This page pulls live pricing on a 24-hour cache, so gaps you see today may shift as either provider updates rates.
Can I compare more than GPT and Claude?
This page focuses on the GPT vs Claude decision since it's the most common comparison developers ask about, but the token and cost calculators above also include Gemini, GLM, Kimi and Nemotron pricing if you want to widen the comparison.
Is it worth switching providers to save 10-20%?
Usually not on its own. Prompt rework, new failure modes and re-testing consume most of a thin margin. Switching pays off when the gap is large and sustained for your specific workload shape, or when a capability difference compounds the savings.
Do batch and caching discounts change which side wins?
They can flip the result. Both providers offer roughly half-price batch processing, and their caching discounts differ in mechanics and depth. If your workload is batch-friendly or reuses long prefixes, compare the discounted paths — not just the list prices shown here.
Which family is cheaper overall?
Neither, as a general statement — it depends on the shape of your workload. Input rates are close tier for tier, so on input-heavy work like document analysis the two families land within a few percent of each other. Claude is consistently cheaper on output, which favours generation-heavy work. The honest answer comes from pricing your actual token mix rather than comparing headline numbers.
How should I map tiers between the two families?
By role rather than by name. GPT-5.6 Sol and Claude Opus 4.8 are the flagship reasoning tiers. GPT-5.6 Terra and Claude Sonnet 5 are the workhorses most production traffic should sit on. GPT-5.6 Luna and Claude Haiku 4.5 are the high-volume budget tiers. Comparing a flagship against another vendor's budget tier is the most common way these comparisons go wrong.
Does the context window difference matter for cost?
Only if you use it. GPT-5.6 carries a 1.05M-token window against Claude's 200k, but you pay per token sent, not per token of window — an unused window is free. The difference matters when a single document genuinely exceeds 200k tokens and you would otherwise have to chunk it, which adds both engineering work and roughly 10–25% in overhead tokens.
Curious where these numbers come from? Read our pricing methodology.