// head_to_head
GPT-5.2 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.
GPT-5.2 scores higher, GLM 5.2 costs less — it depends on your workload.
GPT-5.2 is ahead by 1.5 points overall, and GLM 5.2 lists 3.2× cheaper per blended million tokens. Whether 1.5 points is worth that depends on how much a wrong answer costs you.
openai
GPT-5.2
- Blended / 1M
- $4.81
- Context
- 400K
- Released
- Dec 10, 2025
- Overall score
- 74.6
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-5.2 | GLM 5.2 |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 74.6win | 73.2 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.1289 | $0.1260 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $4.81 | $1.48win |
| Input price / 1M | $1.75 | $0.966win |
| Output price / 1M | $14.00 | $3.04win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.175win | $0.193 |
| Context window | 400K | 1.0Mwin |
| 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-5.2 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-5.2 | GLM 5.2 |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $1426.60/mo | $419.08/mo |
| RAG assistant 8K in / 600 out × 100K requests | $1610.00/mo | $645.84/mo |
| Coding agent 40K in / 4K out × 20K requests | $1638.00/mo | $582.91/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $2721.25/mo | $1155.06/mo |
| Bulk classification 500 in / 20 out × 5M requests | $4987.50/mo | $2332.20/mo |
Which should you pick?
Quality matters more than the bill
GPT-5.2
Highest overall LiveBench score of the two at 74.6.
The workload is coding or agentic work
GLM 5.2
Leads on agentic coding — 51.8 against 50.3.
You need to fit large documents in one call
GLM 5.2
Wider context window — 1.0M against 400K.
GPT-5.2 vs GLM 5.2 FAQ
Which is better, GPT-5.2 or GLM 5.2?
GPT-5.2 scores higher, GLM 5.2 costs less — it depends on your workload. GPT-5.2 is ahead by 1.5 points overall, and GLM 5.2 lists 3.2× cheaper per blended million tokens. Whether 1.5 points is worth that depends on how much a wrong answer costs you.
Is GPT-5.2 cheaper than GLM 5.2?
GLM 5.2 is cheaper. On a 3:1 input:output blend, GPT-5.2 lists at $4.81 per million tokens and GLM 5.2 at $1.48 — GLM 5.2 is 3.2× cheaper. Input and output are priced separately — GPT-5.2 charges $1.75 in and $14.00 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-5.2 vs GLM 5.2: which scores higher on benchmarks?
GPT-5.2 scores 74.6 and GLM 5.2 scores 73.2 overall on LiveBench, the mean of its seven categories. That is a 1.5-point lead for GPT-5.2. 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-5.2 or GLM 5.2?
They are close. GPT-5.2 costs $0.1289 per point of overall capability and GLM 5.2 costs $0.1260, a difference small enough that workload shape will matter more than the rate.
Does GPT-5.2 or GLM 5.2 have a bigger context window?
GLM 5.2 has the larger context window: 400K for GPT-5.2 against 1.0M for GLM 5.2. 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 GPT-5.2 and GLM 5.2 support prompt caching?
Both publish a cached-input rate: $0.175 per million for GPT-5.2 and $0.193 for GLM 5.2, against full input rates of $1.75 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.