// head_to_head
Gemini 4 Argon vs GPT-5.6 Luna
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.
Gemini 4 Argon scores higher, GPT-5.6 Luna costs less — it depends on your workload.
Gemini 4 Argon is ahead by 18.0 points overall, and GPT-5.6 Luna lists 8.9× cheaper per blended million tokens. Whether 18.0 points is worth that depends on how much a wrong answer costs you. GPT-5.6 Luna also leads on measured cost per point of capability, at $0.4693 per point.
Gemini 4 Argon
- Blended / 1M
- $4.00
- Context
- 1.0M
- Released
- Oct 13, 2026
- Overall score
- 84.1
openai
GPT-5.6 Luna
- Blended / 1M
- $0.450
- Context
- 1.1M
- Released
- Jul 9, 2026
- Overall score
- 66.1
Specs and pricing
| Metric | Gemini 4 Argon | GPT-5.6 Luna |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 84.1win | 66.1 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $1.24 | $0.4693win |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $4.00 | $0.450win |
| Input price / 1M | $2.00 | $0.200win |
| Output price / 1M | $10.00 | $1.20win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.100 | $0.020win |
| Context window | 1.0M | 1.1M |
| 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 4 Argon on top, GPT-5.6 Luna 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 4 Argon | GPT-5.6 Luna |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $1,143/mo | $131.04/mo |
| RAG assistant 8K in / 600 out × 100K requests | $1,440/mo | $160.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $1,336/mo | $155.20/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $2,655/mo | $281.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $5,050/mo | $530.00/mo |
Which should you pick?
You are running this at volume
GPT-5.6 Luna
Lowest measured cost per point of capability at $0.4693 per point — the gap compounds with every request.
Quality matters more than the bill
Gemini 4 Argon
Highest overall LiveBench score of the two at 84.1.
The workload is coding or agentic work
Gemini 4 Argon
Leads on coding — 89.7 against 73.6.
Gemini 4 Argon vs GPT-5.6 Luna FAQ
Which is better, Gemini 4 Argon or GPT-5.6 Luna?
Gemini 4 Argon scores higher, GPT-5.6 Luna costs less — it depends on your workload. Gemini 4 Argon is ahead by 18.0 points overall, and GPT-5.6 Luna lists 8.9× cheaper per blended million tokens. Whether 18.0 points is worth that depends on how much a wrong answer costs you. GPT-5.6 Luna also leads on measured cost per point of capability, at $0.4693 per point.
Is Gemini 4 Argon cheaper than GPT-5.6 Luna?
GPT-5.6 Luna is cheaper. On a 3:1 input:output blend, Gemini 4 Argon lists at $4.00 per million tokens and GPT-5.6 Luna at $0.450 — GPT-5.6 Luna is 8.9× cheaper. Input and output are priced separately — Gemini 4 Argon charges $2.00 in and $10.00 out, GPT-5.6 Luna charges $0.200 and $1.20 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Gemini 4 Argon vs GPT-5.6 Luna: which scores higher on benchmarks?
Gemini 4 Argon scores 84.1 and GPT-5.6 Luna scores 66.1 overall on LiveBench, the mean of its seven categories. That is a 18.0-point lead for Gemini 4 Argon. 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 4 Argon or GPT-5.6 Luna?
GPT-5.6 Luna. 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 4 Argon works out at $1.24 per point and GPT-5.6 Luna at $0.4693.
Does Gemini 4 Argon or GPT-5.6 Luna have a bigger context window?
They are effectively the same — 1.0M for Gemini 4 Argon and 1.1M for GPT-5.6 Luna.
Do Gemini 4 Argon and GPT-5.6 Luna support prompt caching?
Both publish a cached-input rate: $0.100 per million for Gemini 4 Argon and $0.020 for GPT-5.6 Luna, against full input rates of $2.00 and $0.200. 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 2026-09-02, 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.