Cost and performance comparison
GPT-5.6 Luna vs Gemini 2.0 Flash
Model AOpenAI
GPT-5.6 Luna
- Cost / 1M Tokens
- $0.50
- 70% in · 30% out mix
- Value Index (score÷cost)
- 170.0
- Higher = better value
- Speed
- 98/100
- Context
- 1.1M
- Tier
- Fast Tier
List price from the OpenAI pricing page, verified October 12, 2026.
Check the sourceModel BGoogle
Gemini 2.0 Flash
- Cost / 1M Tokens
- $0.19
- 70% in · 30% out mix
- Value Index (score÷cost)
- 444.1
- Higher = better value
- Speed
- 99/100
- Context
- 1.0M
- Tier
- Fast Tier
Last list price before the model was retired.
In-depth analysis
GPT-5.6 Luna vs Gemini 2.0 Flash: Detailed Comparison
GPT-5.6 Luna is OpenAI's lightweight-tier language model with a 1.1M-token context window, excelling at data extraction. Gemini 2.0 Flash from Google is a lightweight-tier model supporting 1.0M tokens in context, with standout performance in data extraction.
GPT-5.6 Luna costs 2.7x what Gemini 2.0 Flash does for the same text. That is a steep premium, and it buys a 10-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-5.6 Luna is priced at $0.20/M input tokens and $1.20/M output tokens. Gemini 2.0 Flash costs $0.10/M input and $0.40/M output.
On iOPTERA's editorial scores, GPT-5.6 Luna leads with 84/100 for coding and 85/100 for reasoning, against Gemini 2.0 Flash's 78/100 and 81/100.
Capability breakdown
Across the five core benchmark categories, here is how GPT-5.6 Luna and Gemini 2.0 Flash stack up head to head:
Best model by task
- coding: GPT-5.6 Luna wins with 84/100
- reasoning: GPT-5.6 Luna wins with 85/100
- creative tasks: GPT-5.6 Luna wins with 84/100
- vision/multimodal: Gemini 2.0 Flash wins with 88/100
Estimated monthly cost at scale
For 10M + 2M tokens of text a month (counted the way OpenAI's tokenizer counts), GPT-5.6 Luna runs about $4.40 while Gemini 2.0 Flash runs about $1.73 — Gemini 2.0 Flash saves roughly $2.67 (61%) every month.
What actually decides it
GPT-5.6 Luna and Gemini 2.0 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-5.6 Luna and Gemini 2.0 Flash are 17 months apart, which is more than one generation in this market. Benchmark comparisons across that gap flatter the older model: it was measured against the evaluations that existed at the time. Treat Gemini 2.0 Flash's scores as a floor for what it does well and be sceptical of a close-looking result.
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 — 99/100 versus 98/100. Neither model will feel noticeably quicker in an interactive product, so latency is not a reason to choose between them.
One practical asymmetry: GPT-5.6 Luna offers a batch API at 50% off standard rates, and Gemini 2.0 Flash does not. For anything that does not need an answer immediately — nightly enrichment, backfills, evaluation runs — that discount can be worth more than the difference in list price, and it is easy to overlook when comparing headline rates.
GPT-5.6 Luna is 2.7x the price of Gemini 2.0 Flash. Reserve it for the requests that actually need it and route the rest to Gemini 2.0 Flash — 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-5.6 Luna | Gemini 2.0 Flash |
|---|---|---|
| List price (input / output) | $0.20 / $1.20 | $0.10 / $0.40 |
| Cached input | $0.0210% of the input price | — |
| Batch API | 50% off input and output | — |
| Long prompts | Over 272K tokens: $0.40 / $1.80 | — |
| Input with 80% cache hits | $0.056 | — |
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-5.6 Luna
- $0.08
- Gemini 2.0 Flash
- $0.04
Business email
One typical email (~200 words)
- GPT-5.6 Luna
- $5.40
- Gemini 2.0 Flash
- $2.59
Code file
50-line Python script
- GPT-5.6 Luna
- $8.00
- Gemini 2.0 Flash
- $3.84
Prices shown are for 100,000 runs of each workload — one run costs a fraction of a cent on both GPT-5.6 Luna and Gemini 2.0 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-5.6 Luna
$15.00/mo
$180.00/yr
Gemini 2.0 Flash
$5.47/mo
$65.66/yr
Annual Savings
$114.34 saved per year
Gemini 2.0 Flash cheaper · $9.53/mo
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