Comparison

OpenAI vs Anthropic API pricing comparison

Headline per-million rates mislead when input/output mix and cache hit rate differ. Set one workload, then compare OpenAI GPT and Anthropic Claude models ranked by estimated monthly spend.

Related: providers/openai api pricing · providers/anthropic claude api cost

Calculator

Step 1

Describe your workload

Start with a preset or dial in your own numbers.

Synced Jun 29, 2026

1K messages / mo

1K tokens avg

Step 2

Estimated monthly spend

$67.50

Rank #16 of 63 · save $67.44/mo vs #1

Input $22.50 Cached 0.34M tok Output $45.00
Total tokens1.00M
Messages1K
Avg tok/msg1K

High-quality reasoning agents and premium customer support flows.

Step 3

Compare all models

16 models priced for your workload

Best value

20
GPT-5.4 nanoOpenAI
Verified$0.40
30
Claude Haiku 3.5Anthropic
Verified$1.36
33
GPT-5.4 miniOpenAI
Verified$1.46
38
Claude Haiku 4.5Anthropic
Verified$1.70
50
GPT-5.4OpenAI
Verified$4.87
51
Claude Sonnet 4.6Anthropic
Verified$5.09
52
Claude Sonnet 4.5Anthropic
Verified$5.09
53
Claude Sonnet 4Anthropic
Verified$5.09
54
Claude Sonnet 3.7Anthropic
Needs review$5.09
57
Claude Opus 4.6Anthropic
Verified$8.48
58
Claude Opus 4.5Anthropic
Verified$8.48
59
Claude Opus 4.7Anthropic
Verified$10.00
60
GPT-5.5OpenAI
Verified$11.25
61
Claude Opus 4.1Anthropic
Verified$25.44
62
Claude Opus 4Anthropic
Verified$25.44
63
GPT-5.5 ProOpenAI
Verified$67.50

How costs are calculated

Prices from ai-provider-pricing-validated.json, validated Jun 2026. Confirm on official provider pages before billing decisions.

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Model Cost Comparison · Built by Lazige · Methodology

How we calculate cost

Monthly estimate = (input tokens × input $/MTok) + (cached tokens × cached $/MTok when published) + (output tokens × output $/MTok), scaled to your message volume. See the methodology for validation sources and update cadence.

Use cases

Common workload patterns teams model here

Illustrative scenarios — not customer testimonials. Each card shows how a typical team shape (support bot, RAG, code assistant, or agent) maps to the calculator presets.

“A support-bot preset with 50k messages/month surfaced three budget models in one pass — faster than copying rates from five pricing pages.”
B2B SaaS support botHigh volume · short replies · 55% cache
“Raising the cached-input slider made our RAG estimate realistic. We moved retrieval-heavy traffic to a cheaper model without changing reply quality.”
Document Q&A / RAGRetrieval-heavy · 65% cache
“PMs use the embed on internal docs to sanity-check model spend before vendor requests — everyone shares the same workload baseline.”
Platform / internal toolingMixed presets · stakeholder decks
“Before scaling an agent workflow, comparing monthly cost across every provider for the same token mix avoided over-provisioning on day one.”
Tool-calling agentAgent preset · multi-step I/O
“Finance teams grasp token mix faster with one screenshot from the ranking table — useful when justifying a move off a default premium model.”
Cost review / FinOpsBoard prep · usage doubles scenario
“Quarterly Bedrock vs Vertex vs direct API reviews start here — normalize the math before opening vendor spreadsheets.”
Cloud architecture reviewMulti-cloud comparison
“The code-assistant preset was a realistic starting point for a copilot MVP; we adjusted tokens after a pilot week and stayed within 10% of the estimate.”
IDE / code copilotCode preset · long context
“Gemini Flash placed top three for our exact cache ratio on a high-volume FAQ bot — easy to miss in a static pricing table.”
High-volume FAQSupport preset · high cache