Cost and performance comparison
GPT-6 Luna vs Gemini 3.8 Flash
Model AOpenAI
GPT-6 Luna
- Cost / 1M Tokens
- $0.22
- 70% in · 30% out mix
- Value Index (score÷cost)
- 395.5
- Higher = better value
- Speed
- 97/100
- Context
- 1.1M
- Tier
- Fast Tier
List price from the OpenAI pricing page, verified October 12, 2026.
Check the sourceModel BGoogle
Gemini 3.8 Flash
- Cost / 1M Tokens
- $1.65
- 70% in · 30% out mix
- Value Index (score÷cost)
- 56.9
- Higher = better value
- Speed
- 95/100
- Context
- 1.0M
- Tier
- Fast Tier
List price from the Google pricing page, verified October 12, 2026.
Introductory price until December 31, 2026; $1.50 / $7.50 from the day after.
Check the sourceIn-depth analysis
GPT-6 Luna vs Gemini 3.8 Flash: Detailed Comparison
GPT-6 Luna is OpenAI's lightweight-tier language model with a 1.1M-token context window, excelling at data extraction. Gemini 3.8 Flash from Google is a lightweight-tier model supporting 1.0M tokens in context, with standout performance in vision/multimodal.
Gemini 3.8 Flash costs 7.3x what GPT-6 Luna does for the same text. That is a steep premium, and it buys a 8-point lead on combined coding and reasoning. Whether that trade is worth it depends entirely on how much of your traffic actually needs the harder model — for most workloads the honest answer is a small fraction of it, which is an argument for routing rather than for picking one. GPT-6 Luna is priced at $0.10/M input tokens and $0.50/M output tokens. Gemini 3.8 Flash costs $0.75/M input and $3.75/M output.
On iOPTERA's editorial scores, Gemini 3.8 Flash leads with 90/100 for coding and 91/100 for reasoning, against GPT-6 Luna's 86/100 and 87/100.
Capability breakdown
Across the five core benchmark categories, here is how GPT-6 Luna and Gemini 3.8 Flash stack up head to head:
Best model by task
- coding: Gemini 3.8 Flash wins with 90/100
- reasoning: Gemini 3.8 Flash wins with 91/100
- creative tasks: Gemini 3.8 Flash wins with 88/100
- vision/multimodal: Gemini 3.8 Flash wins with 94/100
Estimated monthly cost at scale
For 10M + 2M tokens of text a month (counted the way OpenAI's tokenizer counts), GPT-6 Luna runs about $2.00 while Gemini 3.8 Flash runs about $14.55 — GPT-6 Luna saves roughly $12.55 (86%) every month.
What actually decides it
GPT-6 Luna and Gemini 3.8 Flash come from different labs, which means different tokenizers, different API shapes, and a second vendor relationship. The same English text does not produce the same token count on both, so a price-per-million comparison understates the difference — measure your own prompts on each before treating the headline rates as the full story.
GPT-6 Luna and Gemini 3.8 Flash were released within 0 months of each other, so they are competing on the same evaluations under roughly the same conditions. That makes a direct benchmark comparison meaningful here in a way it usually is not — neither model has the advantage of being measured on a newer, easier set of tests.
Context is effectively a tie: 1.1M against 1.0M. Neither model unlocks a document size the other cannot handle, so this axis should not enter the decision. If you were hoping context would break the tie for you, it will not.
Throughput is close enough to ignore — 97/100 versus 95/100. Neither model will feel noticeably quicker in an interactive product, so latency is not a reason to choose between them.
Gemini 3.8 Flash is 7.3x the price of GPT-6 Luna. Reserve it for the requests that actually need it and route the rest to GPT-6 Luna — that hybrid beats either model used alone on cost per useful answer.
What you actually pay
The list price is only the start. Prompt caching, batch processing and long prompts change the bill, and each provider sets them differently. Prices per 1M tokens.
| Pricing term | GPT-6 Luna | Gemini 3.8 Flash |
|---|---|---|
| List price (input / output) | $0.10 / $0.50 | $0.75 / $3.75 |
| Cached input | $0.0110% of the input price | $0.07510% of the input price |
| Batch API | 50% off input and output | 50% off input and output |
| Long prompts | Over 272K tokens: $0.20 / $0.75 | No surcharge across the 1.05M window |
| Input with 80% cache hits | $0.028 | $0.21 |
With 80% of input served from the cache, GPT-6 Luna costs 86% less than Gemini 3.8 Flash for the same input text, counting each model's tokenizer.The tokenizer factor for Gemini 3.8 Flash is our estimate until it is measured.
The last row assumes 80% of input tokens are read from the cache, as in a chat app that re-sends a long system prompt or RAG over the same documents. Cache-write and storage charges are not included.
Gemini also bills cache storage by the hour.
Checked on October 8, 2026 against the pricing pages of OpenAI, Google.
Capability scores
iOPTERA editorial scores across 5 categories — informed by public benchmarks, not measured by us
Coding
Reasoning
Extraction
Creative
Vision
Speed Score
Context Window
What Is a Token?
Models don't read words — they process tokens.
A token is roughly 4 characters of English text (~¾ of a word). Your API bill is priced per million tokens — understanding this directly reduces your costs.
Short phrase
"Hello, world!"
- GPT-6 Luna
- $0.04
- Gemini 3.8 Flash
- $0.29
Business email
One typical email (~200 words)
- GPT-6 Luna
- $2.70
- Gemini 3.8 Flash
- $19.64
Code file
50-line Python script
- GPT-6 Luna
- $4.00
- Gemini 3.8 Flash
- $29.10
Prices shown are for 100,000 runs of each workload — one run costs a fraction of a cent on both GPT-6 Luna and Gemini 3.8 Flash, so the number only becomes meaningful at production volume. Input tokens only; add your output volume in the calculator below.
How to check your token usage
response.usage.total_tokensEvery API response includes a usage object. Sum total_tokens across all calls to get your monthly figure, then use the calculator below.
Your Cost Calculator
Enter your actual monthly token usage to see real savings
Quick Presets
GPT-6 Luna
$6.60/mo
$79.20/yr
Gemini 3.8 Flash
$48.02/mo
$576.18/yr
Annual Savings
$496.98 saved per year
GPT-6 Luna cheaper · $41.42/mo
Related Comparisons
Explore similar model pairs to find your best fit