Legacy nano versus still-listed Lite: sub-cent per million tokens.
gpt-5.4-nano · openai
gemini-2.5-flash-lite · google
Benchmark
| Workload Scenario | GPT-5.4 nano | Gemini 2.5 Flash-Lite | Price Delta |
|---|---|---|---|
| 1M input tokens (raw text) | $0.20 | $0.10 | +$0.10 |
| 1M output tokens (generation) | $1.25 | $0.40 | +$0.85 |
| 1M tokens · 70% input / 30% output mix | $0.515 | $0.19 | +$0.325 |
| Standard chat turn (4K in / 800 out, 50% cached) | $0.00144 | $0.00054 | +$0.0009 |
| Monthly scale (10K requests / day) | $432.00 | $162.00 | +$270.00 |
Negative difference = GPT-5.4 nano is cheaper. Positive = Gemini 2.5 Flash-Lite is cheaper.
Scaling Curve
Total cost of a token volume at 70/30 input/output split (uncached). Log-log scale.
Verdict
Gemini 2.5 Flash-Lite is cheaper on both input and output rates, so it costs less at every input/output mix. Price alone still isn't the whole decision: capability, latency and context limits (GPT-5.4 nano: 1.1M, Gemini 2.5 Flash-Lite: 1M) may justify the premium for your task.
FAQ
On input, Gemini 2.5 Flash-Lite is cheaper ($0.10/M vs $0.20/M). On output, Gemini 2.5 Flash-Lite is cheaper ($0.40/M vs $1.25/M). The same model is cheaper on both sides, so it wins at every mix.
A chat-style request (4,000 input + 800 output tokens, 50% cached) costs $0.00144 on GPT-5.4 nano and $0.00054 on Gemini 2.5 Flash-Lite — Gemini 2.5 Flash-Lite is 2.7× more expensive for that workload.
GPT-5.4 nano supports 1,050,000 tokens (128K max output); Gemini 2.5 Flash-Lite supports 1,048,576 (65.5K max output). GPT-5.4 nano fits 1.0× more context, which matters for long documents and agents.