Gemini vs GPT: Pricing, Models & When to Use Each
Gemini and GPT both come as tiered families, so the useful comparison is tier against tier rather than one flagship against another. Google spans Pro, Flash and Flash-Lite; OpenAI spans GPT-5.4, GPT-5.5 and the GPT-5.6 Sol / Terra / Luna line. Here is every text model from both families on ATP, with prices, capabilities, documented context, and a calculator for the same workload on each.
Gemini models on ATP
GPT models on ATP
Lowest Gemini price
Lowest GPT price
Models & prices
Every Gemini and GPT model ATP serves today. Rates are USD per 1M tokens (USD 1 = 100 credits); the bar shows each model's output rate against the most expensive model on this page.
Gemini· Google
Input $2.00 · Output $12.00 / 1M tokens
Input $1.50 · Output $9.00 / 1M tokens
Input $1.50 · Output $7.50 / 1M tokens
Input $0.50 · Output $3.00 / 1M tokens
Input $0.30 · Output $2.50 / 1M tokens
GPT· OpenAI
Input $30.00 · Output $180.00 / 1M tokens
Input $5.00 · Output $30.00 / 1M tokens
Input $5.00 · Output $30.00 / 1M tokens
Input $2.50 · Output $15.00 / 1M tokens
Input $2.50 · Output $15.00 / 1M tokens
Input $1.00 · Output $6.00 / 1M tokens
- —: not published by the vendor.
Same workload, every model
Set the traffic once; every model on this page is priced at its current ATP price. No discounts are assumed.
Workload
Applied only to models with a cache-read price.
Monthly cost at current prices
| Model | Per month |
|---|---|
Gemini 3.5 Flash LiteGemini | $245.00Lowest on this page |
Gemini 3 Flash (Preview)Gemini | $350.00 |
GPT-5.6 LunaGPT | $700.00 |
Gemini 3.6 FlashGemini | $975.00 |
Gemini 3.5 FlashGemini | $1,050.00 |
Gemini 3.1 Pro (Preview)Gemini | $1,400.00 |
GPT-5.6 TerraGPT | $1,750.00 |
GPT-5.4GPT | $1,750.00 |
GPT-5.6 SolGPT | $3,500.00 |
GPT-5.5GPT | $3,500.00 |
GPT-5.4 ProGPT | $21,000.00 |
USD prices · estimate only · the cache share applies only to models with a cache-read price
Lowest on this page: Gemini 3.5 Flash Lite $245.00
When to pick which
Where each family tends to fit, based on what the vendors document. Facts beyond our prices are cited under Sources.
Pick Gemini for
- Multimodal pipelines that mix text, images, video, audio and PDFs in one request — Google documents these inputs for Gemini 3.5 Flash and the Gemini 3 line.
- Cost-sensitive agent loops at Flash rates. Google pitches Gemini 3 Flash as Pro-grade reasoning at Flash latency and reports 78% on SWE-bench Verified (vendor-reported).
- Repository-scale code understanding and long documents inside one session: Google documents a 1M-token context for Gemini 3.1 Pro (Preview) and 3.5 Flash.
- Tunable reasoning per call — Gemini 3.x exposes thinking levels (minimal to high), so one model id can serve both quick and deliberate requests.
Pick GPT for
- Coding agents and computer-use automation built around OpenAI tooling. OpenAI positions GPT-5.4 for coding agents, computer use and long-horizon tool chains, and says GPT-5.5 uses significantly fewer tokens than GPT-5.4 on the same Codex tasks.
- Production business traffic at a mid rate: OpenAI describes GPT-5.6 Terra as GPT-5.5-competitive at roughly half the cost.
- Frontier reasoning where retries are expensive — GPT-5.6 Sol and GPT-5.4 Pro are OpenAI's top tiers; route only the hardest calls there.
- Very long documents with a documented number: OpenAI documents a 1,050,000-token context for GPT-5.4.
Use both under one key
You rarely have to choose just one. On ATP both families sit behind one endpoint and one project key — so you can route each task to the model that fits and still keep one budget, one allowlist and one log.
- One project key — spend and revocation bound to one project
- Model allowlist — only the ids you enable can be called; others return 403
- Project budget — credits are allocated down org → workspace → project, so the allocation is the cap
- Per-request logs and one bill — model, key, project and credits for every call, across both vendors
from openai import OpenAI
# one project key; both ids must be on the project's allowlist
client = OpenAI(base_url="https://api.atptoken.ai/v1", api_key="atp-•••")
def ask(model, prompt):
r = client.chat.completions.create(
model=model, messages=[{"role": "user", "content": prompt}]
)
return r.choices[0].message.content
draft = ask("gemini-3-5-flash", "Summarize this ticket…")
final = ask("gpt-5.5", "Review: " + draft)
# both calls: same log, same credit balance, same budgetOrganization Northwind Labs · 9,842 credits Workspace Product 62% support-bot models: gemini-3-5-flash, gpt-5.5 38% web-app models: gpt-5.5, deepseek-v4-flash Workspace Media lab 100% video-lab limit reached models: seedance-2-0, kling-v3-pro
Questions teams ask
FAQ
- Which is cheaper, Gemini or GPT?
- At ATP prices (USD per 1M tokens), Gemini runs from $0.30 / $2.50 on gemini-3-5-flash-lite to $2.00 / $12.00 on gemini-3-1-pro-preview; GPT runs from $1.00 / $6.00 on gpt-5.6-luna to $30.00 / $180.00 on gpt-5.4-pro. Which one costs less for you depends on your input/output mix and which tier you need — the calculator on this page prices the same workload on every model.
- Which has the longer context window, Gemini or GPT?
- Documented context windows — Gemini: Gemini 3.5 Flash 1.05M, Gemini 3.5 Flash Lite 1.05M. GPT: GPT-5.4 Pro 1.05M, GPT-5.5 1.05M, GPT-5.4 1.05M. —: not published by the vendor.
- Which tiers line up against each other?
- By role rather than by name: Gemini 3.1 Pro (Preview) against GPT-5.4 / GPT-5.5 for demanding work; Gemini 3.5 / 3.6 Flash against GPT-5.6 Terra for production volume; Gemini 3.5 Flash-Lite and 3 Flash (Preview) against GPT-5.6 Luna for the cheapest high-volume calls. Compare their prices in the tables above, then price your own mix in the calculator.
- Can I use Gemini and GPT with one API key?
- Yes. On ATP both families sit behind the same OpenAI-compatible endpoint (https://api.atptoken.ai/v1), and Anthropic-format clients can use /v1/messages. One project key calls any model on that project's allowlist; every request lands in the same per-request log and debits the same credit balance.
- How do I keep a team on the models we approved?
- Use the project's model allowlist: enable only the model ids you approved, and calls to any other id are refused with 403. Give the project its own credit allocation so the budget is the ceiling, and review spend per model in the per-request logs.
Where the claims come from
Rates, capabilities and context on this page come from the ATP model catalog. Other claims cite the sources below.
- Google AI for Developers — Gemini 3 developer guide
- Google AI for Developers — What's new in Gemini 3.5 Flash
- Google — Introducing Gemini 3 Flash
- OpenAI API — GPT-5.4 model docs
- OpenAI — Introducing GPT-5.5
- OpenAI — GPT-5.6: Frontier intelligence that scales with your ambition
- OpenAI community — Introducing GPT-5.6 series: Sol, Terra and Luna
Other comparisons
- textDeepSeek vs ClaudeVolume-priced V4 vs Anthropic's Haiku-to-Fable lineup — rates, context, same-workload cost.
- textQwen vs DeepSeekAlibaba's Max / Plus / Flash tiers vs DeepSeek V4 — tiered vs flat pricing, context, same-workload cost.
- textKimi vs ClaudeMoonshot's open-weight K2 line and K3 vs Claude — agent swarms, coding specialists, rates and cost.
- videoKling vs SeedanceSame prompts, real outputs side by side — plus per-second rates for every Kling and Seedance tier.
Keep reading
Try both on one key — start with USD 5.
Issue a project key, allowlist the models you want to test, and compare them on your own traffic.