// head_to_head
Gemini 3.5 Flash Lite vs GPT-5.4 Mini
Published LiveBench scores across all seven categories, live list pricing, context windows, and the measured cost of a point of capability — for both models, side by side.
GPT-5.4 Mini scores higher, Gemini 3.5 Flash Lite costs less — it depends on your workload.
GPT-5.4 Mini is ahead by 2.4 points overall, and Gemini 3.5 Flash Lite lists 2.0× cheaper per blended million tokens. Whether 2.4 points is worth that depends on how much a wrong answer costs you. Gemini 3.5 Flash Lite also leads on measured cost per point of capability, at $0.0379 per point.
Gemini 3.5 Flash Lite
- Blended / 1M
- $0.850
- Context
- 1.0M
- Released
- Jul 21, 2026
- Overall score
- 63.9
openai
GPT-5.4 Mini
- Blended / 1M
- $1.69
- Context
- 400K
- Released
- Mar 17, 2026
- Overall score
- 66.4
Specs and pricing
| Metric | Gemini 3.5 Flash Lite | GPT-5.4 Mini |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 63.9 | 66.4win |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0379win | $0.1871 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.850win | $1.69 |
| Input price / 1M | $0.300win | $0.750 |
| Output price / 1M | $2.50win | $4.50 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.030win | $0.075 |
| Context window | 1.0Mwin | 400K |
| Max output tokens | 66K | 128Kwin |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Gemini 3.5 Flash Lite on top, GPT-5.4 Mini below, both out of 100.
What each one costs to run
Per-token prices are hard to feel. These are monthly list costs for both models across five workload shapes, using each provider's published cached-input rate where there is one.
| Workload | Gemini 3.5 Flash Lite | GPT-5.4 Mini |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $252.56/mo | $491.40/mo |
| RAG assistant 8K in / 600 out × 100K requests | $282.00/mo | $600.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $288.80/mo | $582.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $474.00/mo | $1,054/mo |
| Bulk classification 500 in / 20 out × 5M requests | $865.00/mo | $1,988/mo |
Which should you pick?
You are running this at volume
Gemini 3.5 Flash Lite
Lowest measured cost per point of capability at $0.0379 per point — the gap compounds with every request.
Quality matters more than the bill
GPT-5.4 Mini
Highest overall LiveBench score of the two at 66.4.
The workload is coding or agentic work
Gemini 3.5 Flash Lite
Leads on agentic coding — 45.3 against 41.7.
You need to fit large documents in one call
Gemini 3.5 Flash Lite
Wider context window — 1.0M against 400K.
Gemini 3.5 Flash Lite vs GPT-5.4 Mini FAQ
Which is better, Gemini 3.5 Flash Lite or GPT-5.4 Mini?
GPT-5.4 Mini scores higher, Gemini 3.5 Flash Lite costs less — it depends on your workload. GPT-5.4 Mini is ahead by 2.4 points overall, and Gemini 3.5 Flash Lite lists 2.0× cheaper per blended million tokens. Whether 2.4 points is worth that depends on how much a wrong answer costs you. Gemini 3.5 Flash Lite also leads on measured cost per point of capability, at $0.0379 per point.
Is Gemini 3.5 Flash Lite cheaper than GPT-5.4 Mini?
Gemini 3.5 Flash Lite is cheaper. On a 3:1 input:output blend, Gemini 3.5 Flash Lite lists at $0.850 per million tokens and GPT-5.4 Mini at $1.69 — Gemini 3.5 Flash Lite is 2.0× cheaper. Input and output are priced separately — Gemini 3.5 Flash Lite charges $0.300 in and $2.50 out, GPT-5.4 Mini charges $0.750 and $4.50 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Gemini 3.5 Flash Lite vs GPT-5.4 Mini: which scores higher on benchmarks?
Gemini 3.5 Flash Lite scores 63.9 and GPT-5.4 Mini scores 66.4 overall on LiveBench, the mean of its seven categories. That is a 2.4-point lead for GPT-5.4 Mini. Category scores differ from the overall figure — a model can lead on reasoning and trail on coding, which the per-category table above breaks out.
Which gives better value for money, Gemini 3.5 Flash Lite or GPT-5.4 Mini?
Gemini 3.5 Flash Lite. Cost per point divides the measured dollars LiveBench spent running the benchmark by the score it earned, so it captures something token pricing misses: a reasoning model can emit many times more tokens than its per-token rate suggests. Gemini 3.5 Flash Lite works out at $0.0379 per point and GPT-5.4 Mini at $0.1871.
Does Gemini 3.5 Flash Lite or GPT-5.4 Mini have a bigger context window?
Gemini 3.5 Flash Lite has the larger context window: 1.0M for Gemini 3.5 Flash Lite against 400K for GPT-5.4 Mini. Note that a window you can fill is not a window you should fill — retrieval quality usually degrades well before the limit, and you pay for every token you put in it.
Do Gemini 3.5 Flash Lite and GPT-5.4 Mini support prompt caching?
Both publish a cached-input rate: $0.030 per million for Gemini 3.5 Flash Lite and $0.075 for GPT-5.4 Mini, against full input rates of $0.300 and $0.750. On a workload with a long stable prefix — a system prompt, a tool schema, a retrieved corpus — that changes the economics more than the headline price does.
Related comparisons
How these numbers are produced
- Price — provider list price from OpenRouter, refreshed every 15 minutes. “Blended” is a 3:1 input:output mix.
- Scores — LiveBench release 2026-06-25, using their own category map. Each model shows its strongest published run. A blank means “not evaluated”, never “bad”.
- Cost per point — the measured dollars LiveBench spent on the run, divided by the score it earned.
- “Win” — awarded only past a threshold: one full point on a benchmark score, 10% on a price, 25% on a context window. Anything tighter reports as a tie, because effort settings alone move a LiveBench score by more than that.
Published benchmarks rank models on someone else's tasks. Before committing, see LLM & agent evaluation for building an eval on your own.