// head_to_head
GPT-5.6 Luna vs GLM 5.2
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.
Effectively the same quality — GPT-5.6 Luna is the cheaper way to get it.
The two are within 0.4 points overall, which is inside the range that effort settings alone move a LiveBench score, so treat them as quality-equivalent. GPT-5.6 Luna lists 3.3× cheaper per blended million tokens. When quality ties, cost is the whole decision. GPT-5.6 Luna also leads on measured cost per point of capability, at $0.0911 per point.
openai
GPT-5.6 Luna
- Blended / 1M
- $0.450
- Context
- 1.1M
- Released
- Jul 9, 2026
- Overall score
- 73.6
z-ai
GLM 5.2
- Blended / 1M
- $1.48
- Context
- 1.0M
- Released
- Jun 16, 2026
- Overall score
- 73.2
Specs and pricing
| Metric | GPT-5.6 Luna | GLM 5.2 |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 73.6 | 73.2 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0911win | $0.1260 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.450win | $1.48 |
| Input price / 1M | $0.200win | $0.966 |
| Output price / 1M | $1.20win | $3.04 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.020win | $0.193 |
| Context window | 1.1M | 1.0M |
| Max output tokens | 128K | 131K |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — GPT-5.6 Luna on top, GLM 5.2 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 | GPT-5.6 Luna | GLM 5.2 |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $131.04/mo | $419.08/mo |
| RAG assistant 8K in / 600 out × 100K requests | $160.00/mo | $645.84/mo |
| Coding agent 40K in / 4K out × 20K requests | $155.20/mo | $582.91/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $281.00/mo | $1155.06/mo |
| Bulk classification 500 in / 20 out × 5M requests | $530.00/mo | $2332.20/mo |
Which should you pick?
You are running this at volume
GPT-5.6 Luna
Lowest measured cost per point of capability at $0.0911 per point — the gap compounds with every request.
The workload is coding or agentic work
GLM 5.2
Leads on agentic coding — 51.8 against 48.4.
GPT-5.6 Luna vs GLM 5.2 FAQ
Which is better, GPT-5.6 Luna or GLM 5.2?
Effectively the same quality — GPT-5.6 Luna is the cheaper way to get it. The two are within 0.4 points overall, which is inside the range that effort settings alone move a LiveBench score, so treat them as quality-equivalent. GPT-5.6 Luna lists 3.3× cheaper per blended million tokens. When quality ties, cost is the whole decision. GPT-5.6 Luna also leads on measured cost per point of capability, at $0.0911 per point.
Is GPT-5.6 Luna cheaper than GLM 5.2?
GPT-5.6 Luna is cheaper. On a 3:1 input:output blend, GPT-5.6 Luna lists at $0.450 per million tokens and GLM 5.2 at $1.48 — GPT-5.6 Luna is 3.3× cheaper. Input and output are priced separately — GPT-5.6 Luna charges $0.200 in and $1.20 out, GLM 5.2 charges $0.966 and $3.04 — so the model that looks cheaper flips depending on how output-heavy your workload is.
GPT-5.6 Luna vs GLM 5.2: which scores higher on benchmarks?
GPT-5.6 Luna scores 73.6 and GLM 5.2 scores 73.2 overall on LiveBench, the mean of its seven categories. That gap is inside the range that effort settings alone move a score, so treat them as equivalent on published quality. 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, GPT-5.6 Luna or GLM 5.2?
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. GPT-5.6 Luna works out at $0.0911 per point and GLM 5.2 at $0.1260.
Does GPT-5.6 Luna or GLM 5.2 have a bigger context window?
They are effectively the same — 1.1M for GPT-5.6 Luna and 1.0M for GLM 5.2.
Do GPT-5.6 Luna and GLM 5.2 support prompt caching?
Both publish a cached-input rate: $0.020 per million for GPT-5.6 Luna and $0.193 for GLM 5.2, against full input rates of $0.200 and $0.966. 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.