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PricesCompare → Claude Haiku 4.5 vs Gemini 3.7 Flash

Claude Haiku 4.5 vs Gemini 3.7 Flash: API cost compared

Claude Haiku 4.5 lists at $1.000 in / $5.000 out per 1M tokens. Gemini 3.7 Flash lists at $0.750 in / $3.750. Sticker price only tells you so much — below is what each actually costs across eight production workloads.

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Cheaper on more workloads
Gemini 3.7 Flash
wins 4 of 8
Blended rate gap
1.3x
at 3:1 in:out

Side by side

Claude Haiku 4.5Gemini 3.7 Flash
ProviderAnthropicGoogle
Input per 1M$1.000$0.750
Output per 1M$5.000$3.750
Cached input per 1M$0.100not published
Context window200k1M
Blended per 1M (3:1)$2.000$1.500

Monthly cost by workload

Caching applied at each workload's typical hit rate. This is where the two models actually separate.

WorkloadClaude Haiku 4.5Gemini 3.7 FlashCheaperDifference
Customer support chatbot$128.90$133.12Claude Haiku 4.5$4.225 (3%)
RAG document Q&A$184.00$165.00Gemini 3.7 Flash$19.00 (10%)
Coding agent$92.50$120.00Claude Haiku 4.5$27.50 (23%)
Bulk summarization$1,346$1,050Gemini 3.7 Flash$296.00 (22%)
Structured data extraction$1,745$1,781Claude Haiku 4.5$36.25 (2%)
Long-form content generation$214.60$165.00Gemini 3.7 Flash$49.60 (23%)
Email triage agent$402.50$487.50Claude Haiku 4.5$85.00 (17%)
Translation pipeline$1,405$1,069Gemini 3.7 Flash$336.00 (24%)
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How to read this

Gemini 3.7 Flash is cheaper on the majority of these workloads, but "majority" is not the decision. The output:input price ratio is 5.0 for Claude Haiku 4.5 and 5.0 for Gemini 3.7 Flash. If your traffic is output-heavy — long-form drafting, translation — the model with the lower output price wins regardless of what the input price suggests. If you are input-heavy — RAG, bulk summarization, agents re-reading a codebase — the cached input rate matters more than either headline number.

Cost is also not the only axis. This site does not benchmark quality, latency, or rate limits, and a model that is 3x cheaper but needs two attempts per task is not cheaper. Use these numbers to size a bill, then validate on your own evals.

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