Claude vs DeepSeek Cost Calculator

What it does

Compare the cost of running an LLM workload on Claude (Sonnet, Opus, Haiku) vs DeepSeek (V3.2 / R1) at your actual volume. DeepSeek V3.2 typically scores within 5 quality points of Claude Sonnet on standardized benchmarks while costing roughly 1/10 the per-token price for input and output. For high-volume workloads (~10M+ tokens/day) the savings are substantial — this calculator shows you exactly how much, plus rough quality scores to break ties when costs are close.

The pricing landscape (per 1M tokens, late 2026):

• Claude Sonnet 4.5: ~$3 input / $15 output (with prompt caching, ~10% on cache reads)
• Claude Opus 4: ~$15 input / $75 output (premium, for hardest tasks)
• Claude Haiku 4.5: ~$0.80 input / $4 output (fast / cheap tier)
• DeepSeek V3.2: ~$0.27 input / $1.10 output (chat model, comparable to Sonnet quality)
• DeepSeek R1: ~$0.55 input / $2.19 output (reasoning model, comparable to Sonnet extended-thinking)

So a workload doing 100M input + 30M output tokens per month costs about $750 on DeepSeek V3.2, $750 on Haiku, $750 on Sonnet, $7,500 on Opus — wait, that’s not quite right. Let me redo: Sonnet at 100M input + 30M output = $300 + $450 = $750. DeepSeek V3.2 at the same volume = $27 + $33 = $60. Haiku = $80 + $120 = $200. So DeepSeek V3.2 is roughly 12× cheaper than Sonnet, 3× cheaper than Haiku, ~120× cheaper than Opus for the same workload. The calculator does this math precisely.

Quality vs cost tradeoff: DeepSeek V3.2 benchmarks within 5 points of Sonnet on most standardized tests (MMLU, HumanEval, etc.) — for many workloads the quality is indistinguishable. For others (long-context coherence, nuanced creative writing, complex multi-step reasoning), Claude still leads. Test on your specific workload before committing — but for cost-sensitive applications doing classification, summarization, basic generation, the savings are real.

How to use it

1. Enter your typical input tokens per call (system prompt + user message + any RAG context).
2. Enter typical output tokens per call.
3. Enter calls per day (or per hour, then ×24).
4. The calculator outputs monthly cost for each model and the savings vs your current model.
5. Compare quality scores (rough estimates from public benchmarks) to find the cheapest model that still meets your quality bar.
6. For low-volume hobbyist usage (<$10/month total), differences are noise — use whatever you prefer. For high-volume production, even 5× cost differences add up to meaningful budget.

When to use this tool

• Sizing AI infrastructure costs for a new product or feature.
• Evaluating switching costs vs savings — switching providers has migration overhead, factor that against monthly savings.
• Comparing the major providers' pricing without manually doing token math.
• Setting realistic budgets for AI-heavy workloads.

When not to use it

• When quality is paramount — quality scores in this calculator are public-benchmark approximations, not workload-specific. For high-stakes tasks (medical, legal, code generation for critical systems), test on your data.
• When latency matters — the calculator focuses on token cost. DeepSeek is sometimes slower than Anthropic's API in real-world use; that's a separate consideration.
• When data residency or compliance constraints matter — DeepSeek runs on Chinese infrastructure (with data potentially flowing through PRC jurisdiction); Claude runs on Anthropic's AWS infrastructure. Pick based on your compliance requirements.
• When you need feature parity (vision input, tool use, prompt caching, batch API) — providers differ on what they support; calculator focuses purely on text-token cost.

Common use cases

• Educational use &mdash; demonstrating the underlying concept
• Onboarding a colleague who needs the same calculation/conversion
• Verifying a number or output before passing it on
• Quick calculation during a typical workday