GPT-5.6 Luna vs Gemini 3.1 Flash-Lite
OpenAI's GPT-5.6 Luna and Google's Gemini 3.1 Flash-Lite both target production language workloads, but they price and behave differently. Here is the side-by-side.
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The short answer
GPT-5.6 Luna is the cheaper option — roughly 1.3x less on a blended workload, and it suits high-volume work on a tight budget. Gemini 3.1 Flash-Lite justifies its premium when you need ultra-cheap high-volume tasks. Only GPT-5.6 Luna does native reasoning, which matters for multi-step tasks but adds billable thinking tokens.
OpenAI • GPT-5.6
$0.20 / $1.20
/ 1M tokens (input / output)
After an 80% price cut in July 2026, Luna became the value outlier among frontier-family models: it outperforms the previous generation's top-end Opus tier on coding evals while costing about a fiftieth of Fable 5 per input token. This is the model to route bulk traffic through in a tiered architecture.
Google • Gemini 3.1
$0.25 / $1.50
/ 1M tokens (input / output)
Google's efficiency tier and a long-term stable target, priced for workloads measured in millions of calls rather than thousands. It gives up the deeper reasoning of 3.5 Flash, so it suits mechanical tasks — classification, extraction, moderation — where a million tokens of context still helps but chain-of-thought does not.
Input price
Gemini 3.1 Flash-Lite is 25% more expensive than GPT-5.6 Luna on input tokens.
Output price
Gemini 3.1 Flash-Lite is 25% more expensive than GPT-5.6 Luna on output tokens — usually the side that dominates the bill.
Monthly cost at three workload sizes
Standard (non-batch, non-cached) rates. Reasoning models will exceed these figures because thinking tokens bill as output.
| Workload | GPT-5.6 Luna | Gemini 3.1 Flash-Lite | Difference |
|---|---|---|---|
| Light — 1M in / 200K out | $0 | $1 | $0 |
| Moderate — 10M in / 2M out | $4 | $6 | $1 |
| Heavy — 100M in / 20M out | $44 | $55 | $11 |
Specification comparison
| Attribute | GPT-5.6 Luna | Gemini 3.1 Flash-Lite |
|---|---|---|
| Input (/ 1M tokens) | $0.20 | $0.25 |
| Output (/ 1M tokens) | $1.20 | $1.50 |
| Cached input | $0.02 | — |
| Context window | 1,048,576 tokens | 1,048,576 tokens |
| Max output | 128,000 tokens | 65,536 tokens |
| Native reasoning | Yes | No |
| Knowledge cutoff | 2026-02 | 2025-01 |
| Relative latency | low | low |
| Open weights | No | No |
| API model ID | gpt-5.6-luna | gemini-3.1-flash-lite |
| Status | stable | stable |
GPT-5.6 Luna: Cut 80% from the $1/$6 launch price on 2026-07-30.
Choose GPT-5.6 Luna if…
- Classification and content moderation
- High-frequency agent subtasks
- Bulk summarization
- Cost-sensitive chat features
Choose Gemini 3.1 Flash-Lite if…
- Content moderation at volume
- Classification and routing
- Lightweight summarization
- Cheap long-context retrieval
Frequently asked
Is GPT-5.6 Luna or Gemini 3.1 Flash-Lite cheaper?
GPT-5.6 Luna is cheaper. On a blended 3:1 input-to-output workload it costs about 1.3x less than Gemini 3.1 Flash-Lite.
Which has the larger context window, GPT-5.6 Luna or Gemini 3.1 Flash-Lite?
Both accept up to 1.05M tokens, so context is not a differentiator here.
Should I use GPT-5.6 Luna or Gemini 3.1 Flash-Lite?
Pick GPT-5.6 Luna for high-volume work on a tight budget. Pick Gemini 3.1 Flash-Lite for ultra-cheap high-volume tasks. If cost dominates the decision, GPT-5.6 Luna wins; if you need the capability ceiling, benchmark both on your own evals before committing.