GPT-5.6 Luna vs Gemini 3.7 Flash: the sub-$1 tier
These are the two cheapest mainstream models in the index. Both are priced under a dollar per million input tokens, which puts high-volume workloads (batch extraction, classification, log triage) within reach of small budgets. This pair is about the floor of the market: what it costs to run a genuinely high-volume job without a frontier model budget.
GPT-5.6 Luna vs Gemini 3.7 Flash: the numbers side by side
| GPT-5.6 Luna | Gemini 3.7 Flash | |
|---|---|---|
| Gateway rate — input | $0.1 | $0.375 |
| Gateway rate — output | $0.6 | $1.875 |
| Official list — input | $0.2 | — |
| Official list — output | $1.2 | — |
| Blended at 1/3 output | $0.2667 | $0.875 |
| Output : input price ratio | 6.0× | 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 | GPT-5.6 Luna | Gemini 3.7 Flash | Cheaper |
|---|---|---|---|
| 10M | $2.67 | $8.75 | GPT-5.6 Luna — 70% less |
| 100M | $26.67 | $87.5 | GPT-5.6 Luna — 70% less |
| 1000M | $267 | $875 | GPT-5.6 Luna — 70% 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 $60.83 a month, or $730 a year. Change the split in the calculator (output-heavy workloads widen the gap).
How much tokens each budget buys
| Budget | GPT-5.6 Luna | Gemini 3.7 Flash | Extra tokens |
|---|---|---|---|
| $100 | 375M | 114M | GPT-5.6 Luna — 261M more |
| $1,000 | 3,750M | 1,143M | GPT-5.6 Luna — 2,607M 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
When input pricing drops below a dollar per million tokens, the constraint moves from budget to throughput and accuracy. Practical considerations:
- Volumes here are large by definition. A workload that made sense on a frontier model budget is usually a much larger workload at this tier, re-run the throughput numbers, not just the cost numbers.
- Batch instead of streaming where you can. Long-running batch jobs smooth load and suit cheap models well; interactive latency requirements push you back toward dearer options.
- Watch for tiering. Some vendors charge more above a context threshold, so a large context window is not automatically priced at the headline rate. Check the model page for notes.
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 | GPT-5.6 Luna | Gemini 3.7 Flash | Cheaper | Gap |
|---|---|---|---|---|
| 0% | $0.1 | $0.375 | GPT-5.6 Luna | $0.275 |
| 10% | $0.15 | $0.525 | GPT-5.6 Luna | $0.375 |
| 25% | $0.225 | $0.75 | GPT-5.6 Luna | $0.525 |
| 33% | $0.265 | $0.87 | GPT-5.6 Luna | $0.605 |
| 50% | $0.35 | $1.12 | GPT-5.6 Luna | $0.775 |
| 75% | $0.475 | $1.5 | GPT-5.6 Luna | $1.02 |
| 100% | $0.6 | $1.88 | GPT-5.6 Luna | $1.27 |
Across the whole range one model stays ahead, GPT-5.6 Luna 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: Claude vs Gemini · Claude vs GPT · Lowest-cost LLM APIs
FAQ
Is GPT-5.6 Luna or Gemini 3.7 Flash cheaper?
At current gateway rates GPT-5.6 Luna is cheaper for a mixed workload — $0.2667 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 (6x and 5x respectively), so an output-heavy workload widens the gap.
How much does GPT-5.6 Luna cost per month?
At $0.2667 per 1M blended tokens: about $2.67 for 10M tokens a month, $26.67 for 100M, and $267 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.
What is the cheapest way to run a large batch job?
Price the job at the blended rate, not the input rate, and include retries. If the job is input-heavy and mechanical, the cheapest tier here is usually enough; if it needs verification, cost the verification pass too, because a cheap model plus a second opinion can still beat a dear model used once.
Which is cheaper for a coding agent, GPT-5.6 Luna 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 GPT-5.6 Luna bills about $110.00 and Gemini 3.7 Flash about $375.00, so GPT-5.6 Luna 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 GPT-5.6 Luna and Gemini 3.7 Flash per 1M tokens?
On input it is $0.1 against $0.375 per 1M; on output, $0.6 against $1.88. Blended at one third output the gap is $0.6083 per 1M in favour of GPT-5.6 Luna. 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.