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 2026-11-01; 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.
Where the answer flips: cost as the output share moves
Both prices above are blended at one third output. That assumption is doing more work than it looks like, because it is the only thing standing between you and the opposite answer. The table below holds volume constant and moves only the output share.
| Output share | Claude Haiku 4.5 | Gemini 3.7 Flash | Cheaper | Gap |
|---|---|---|---|---|
| 0% | $0.5 | $0.375 | Gemini 3.7 Flash | $0.125 |
| 10% | $0.7 | $0.525 | Gemini 3.7 Flash | $0.175 |
| 25% | $1 | $0.75 | Gemini 3.7 Flash | $0.25 |
| 33% | $1.16 | $0.87 | Gemini 3.7 Flash | $0.29 |
| 50% | $1.5 | $1.12 | Gemini 3.7 Flash | $0.375 |
| 75% | $2 | $1.5 | Gemini 3.7 Flash | $0.5 |
| 100% | $2.5 | $1.88 | Gemini 3.7 Flash | $0.625 |
Across the whole range one model stays ahead (Gemini 3).7 Flash is cheaper at every output share from 0% to 100%, so this choice does not depend on your token shape.
Read the table as the reason not to trust a single number. Most published comparisons quote one blended figure, usually at an assumed ratio, and that figure is only valid at that ratio. The gap column is the one to watch: where it narrows towards zero, the decision should be made on something other than price.
What this comparison does not tell you
A price comparison answers one question and quietly implies several others it has not addressed. Before acting on the numbers above, these are the differences that a rate table cannot show:
- Cost per task, not cost per token. A model that needs half as many turns to finish the same job can be cheaper at a higher rate. The only way to know is to run one representative task on each and compare tokens consumed, not rates charged.
- Context window. A shorter window does not produce an error, it produces truncation, which looks like a quality problem and gets misdiagnosed as one. Where your workload depends on long context, this outranks price entirely.
- Latency and throughput. Two models at similar rates can differ enough in response time to change whether a feature is usable. Measure it on your own payloads.
- Availability and rate limits. What you can actually sustain under concurrency is a property of the account and the route, not of the price.
- Capability differences. These pages compare price only. Where a capability difference is decisive, the vendor's own documentation is the source We do not guess at it, and a cheaper model that cannot do the job is not cheaper.
The honest ordering: if the gap column above is small at your output share, stop optimising price and optimise for whichever of the five items above your workload actually depends on.
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.
Which is cheaper for a coding agent, Claude Haiku 4.5 or Gemini 3.7 Flash?
Agent workloads are input-heavy. Roughly 500M input to 100M output a month, because every turn resends the whole context. At that shape Claude Haiku 4.5 bills about $500.00 and Gemini 3.7 Flash about $375.00, so Gemini 3.7 Flash comes out ahead. The same model wins at both shapes, so this particular choice does not hinge on how output-heavy your workload turns out to be.
What is the price difference between Claude Haiku 4.5 and Gemini 3.7 Flash per 1M tokens?
On input it is $0.5 against $0.375 per 1M; on output, $2.5 against $1.88. Blended at one third output the gap is $0.2917 per 1M in favour of Gemini 3.7 Flash. Output carries a multiple of the input rate on both models, so the gap widens or narrows with your own token split rather than staying fixed — price it in the calculator with your real ratio before committing.