GPT vs Claude: Which is Cheaper?
Side-by-side cost comparison based on your actual usage pattern.
Same cost for this workload: at matched tiers the two providers now charge identical list prices per token. The real difference will come from how each tokenizer counts your text, how verbose each model is, and long-prompt surcharges (above 272K tokens on GPT-6, above 100K on Claude Haiku 5.5).
GPT-6.1 Sol
U$ 70.00/mo
Claude Sonnet 5.5
U$ 70.00/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-6 Luna to Claude Haiku 5.5 is a fairer fight than comparing GPT-6 Luna to Claude Sonnet 5.5. 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-6 Luna against Claude Sonnet 5.5 makes OpenAI look dramatically cheaper, and putting GPT-6.1 Sol against Claude Haiku 5.5 does the reverse. The fair pairings are flagship against flagship — GPT-6 Astra versus Claude Fable 5.1 — workhorse against workhorse, GPT-6.1 Sol versus Claude Sonnet 5.5, and budget against budget, GPT-6 Luna versus Claude Haiku 5.5. Since October 2026 each of those pairs carries identical list prices, so the per-token comparison ends in a tie and the decision hinges on your workload's shape: how many tokens each tokenizer produces for your text, how verbose each model is by default, whether your prompts cross a long-context threshold (272K on GPT-6, 100K on Haiku 5.5), and how much of the prompt you can cache. 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-6 Luna ($0.10 / $0.50 per million) that is $8 + $2.50 = $10.50. On Claude Haiku 5.5, at the same rate, also $10.50. One generation earlier the same job cost $22 on GPT-5.6 Luna and $105 on Haiku 4.5 — a 4.8× gap that has now closed completely.
A drafting assistant. 2,000 tokens in, 1,200 out, 30,000 calls — 60M input, 36M output. GPT-6.1 Sol ($2.00 / $10.00): $120 + $360 = $480. Claude Sonnet 5.5 at the same rate: $480. The previous workhorse, GPT-5.6 Terra ($2.00 / $12.00), would cost $552 — so if you are still on Terra, moving to either current model saves about 13% before you touch a prompt.
A long-document analyst. 150,000 tokens in, 2,000 out, 500 calls — 75M input, 1M output. Here the thresholds decide: GPT-6 Luna stays on its standard rate and costs $8, while every 150K-token prompt pushes Claude Haiku 5.5 onto its long-prompt rate ($0.50 / $2.50), for $40. Double the documents to 300,000 tokens and the advantage flips at the workhorse tier: GPT-6.1 Sol crosses 272K and costs $615, while Claude Sonnet 5.5 keeps its flat rate at $310. The calculator above uses standard rates, so model long prompts in the OpenAI and Claude calculators with the surcharge in mind.
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-6 Astra and Claude Fable 5.1 are the flagship reasoning tiers, both at $10.00 / $50.00. GPT-6.1 Sol and Claude Sonnet 5.5 are the workhorses most production traffic should sit on, both at $2.00 / $10.00. GPT-6 Luna and Claude Haiku 5.5 are the high-volume budget tiers, both at $0.10 / $0.50. Claude Opus 5.5 ($4.00 / $20.00) has no direct GPT-6 counterpart; its closest price match is the previous GPT-5.6 Sol. 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?
Much less than it used to. GPT-6 offers a 1.05M-token window and every current Claude model 1M — only the older Claude Haiku 4.5 stops at 200k — and you pay per token sent, not per token of window. What matters now is where each model's long-prompt surcharge starts: GPT-6 bills the whole request at 2× input and 1.5× output above 272K tokens, Claude Haiku 5.5 jumps to five times its base rate above 100K, while Claude Fable 5.1, Opus 5.5 and Sonnet 5.5 charge a flat rate across the window. The calculator above uses standard rates, so add the surcharge yourself if your prompts cross those lines.
Curious where these numbers come from? Read our pricing methodology.