Claude Haiku 4.5 vs Gemini 3.7 Flash: the cheap-tier comparison
Both of these are the models you route bulk work to — classification, extraction, summarisation, routing, anything you run millions of times. At this price tier the decision is almost entirely arithmetic, and small per-token differences compound fast because the volumes are large — cheap-tier models are where volume amplifies a small price difference, so the monthly table matters more here than anywhere else on the site.
Claude Haiku 4.5 vs Gemini 3.7 Flash: the numbers side by side
| Claude Haiku 4.5 | Gemini 3.7 Flash | |
|---|---|---|
| Gateway rate — input | $0.5 | $0.375 |
| Gateway rate — output | $2.5 | $1.875 |
| Official list — input | $1 | — |
| Official list — output | $5 | — |
| Blended at 1/3 output | $1.17 | $0.875 |
| Output : input price ratio | 5.0× | 5.0× |
USD per 1M tokens. Gateway rates checked 2026-09-16; official list prices as published by each vendor. Gateway rates move with upstream promotions — re-check before committing a budget.
What each costs per month
| Monthly tokens | Claude Haiku 4.5 | Gemini 3.7 Flash | Cheaper |
|---|---|---|---|
| 10M | $11.67 | $8.75 | Gemini 3.7 Flash — 25% less |
| 100M | $117 | $87.5 | Gemini 3.7 Flash — 25% less |
| 1000M | $1,167 | $875 | Gemini 3.7 Flash — 25% less |
Assumes 1/3 of tokens are output and 2/3 input — roughly a chat or coding-agent mix. At a 100M tokens/month workload the gap is $29.17 a month, or $350 a year. Change the split in the calculator — output-heavy workloads widen the gap.
How much tokens each budget buys
| Budget | Claude Haiku 4.5 | Gemini 3.7 Flash | Extra tokens |
|---|---|---|---|
| $100 | 86M | 114M | Gemini 3.7 Flash — 29M more |
| $1,000 | 857M | 1,143M | Gemini 3.7 Flash — 286M more |
Tokens purchasable at the blended rate above. Output tokens cost more than input, so reading these figures as "all input" would overstate them.
Which one to pick
At this tier the choice is arithmetic, so run your own numbers rather than trusting a relative percentage. Two practical points:
- Price the whole pipeline, not the step. A cheap model that needs three retries or a verification pass costs more than a slightly dearer model that gets it right once. The per-token rate is only the first term.
- Watch the output:input ratio. Classification and extraction are input-heavy, so the input rate dominates. Summarisation and generation are output-heavy, so the output rate does. The same two models can rank differently depending on which side of that line your task sits.
If you are running both — a cheap model for bulk work and a frontier model for hard cases — the saving is larger than picking one, because you stop paying frontier rates for mechanical work. See reducing API cost for the routing pattern.
Switching between them
Both models are called through the same endpoint, so this is a model-ID change, not a migration. Setup references: OpenAI & Anthropic SDK · Claude Code · Cursor · reducing API cost.
Related comparisons: GPT vs Gemini · DeepSeek vs Claude · Lowest-cost LLM APIs
FAQ
Is Claude Haiku 4.5 or Gemini 3.7 Flash cheaper?
At current gateway rates Gemini 3.7 Flash is cheaper for a mixed workload — $1.17 versus $0.875 per 1M tokens blended at one third output. The answer depends on your input/output split: output tokens are priced several times higher than input on both models (5x and 5x respectively), so an output-heavy workload widens the gap.
How much does Claude Haiku 4.5 cost per month?
At $1.17 per 1M blended tokens: about $11.67 for 10M tokens a month, $117 for 100M, and $1,167 for 1B. That is pure token spend — there is no subscription on either model.
How much does Gemini 3.7 Flash cost per month?
At $0.875 per 1M blended tokens: about $8.75 for 10M tokens a month, $87.5 for 100M, and $875 for 1B.
Can I switch between them without rewriting my code?
Both are reached through the same gateway endpoint, so switching is a model-ID change in the request rather than a new integration. See the SDK guide for the one-line change, or the Claude Code setup if you are switching inside a coding agent.
Are light models good enough for production traffic?
That depends on your accuracy requirements, not on price. The pricing argument is that light models are cheap enough that a verification pass on top of them is often still cheaper than running frontier models on every request — so the practical approach is to measure accuracy on your own eval set and only escalate the requests that fail.