// head_to_head
Qwen3.7 Max 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 — GLM 5.2 is the cheaper way to get it.
The two are within 0.0 points overall, which is inside the range that effort settings alone move a LiveBench score, so treat them as quality-equivalent. GLM 5.2 lists 49% cheaper per blended million tokens. When quality ties, cost is the whole decision. The two cost measures disagree here, which is worth knowing: GLM 5.2 has the lower sticker price, but Qwen3.7 Max earns each point of capability for less — $0.0971 against $0.1260 — because per-token rates do not predict how many tokens a model actually spends on a task.
qwen
Qwen3.7 Max
- Blended / 1M
- $2.21
- Context
- 1M
- Released
- May 21, 2026
- Overall score
- 73.1
z-ai
GLM 5.2
- Blended / 1M
- $1.48
- Context
- 1.0M
- Released
- Jun 16, 2026
- Overall score
- 73.2
Specs and pricing
| Metric | Qwen3.7 Max | GLM 5.2 |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 73.1 | 73.2 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0971win | $0.1260 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $2.21 | $1.48win |
| Input price / 1M | $1.48 | $0.966win |
| Output price / 1M | $4.42 | $3.04win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.295 | $0.193win |
| Context window | 1M | 1.0M |
| Max output tokens | 131K | 131K |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Qwen3.7 Max 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 | Qwen3.7 Max | GLM 5.2 |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $623.04/mo | $419.08/mo |
| RAG assistant 8K in / 600 out × 100K requests | $973.50/mo | $645.84/mo |
| Coding agent 40K in / 4K out × 20K requests | $873.20/mo | $582.91/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $1747.88/mo | $1155.06/mo |
| Bulk classification 500 in / 20 out × 5M requests | $3540.00/mo | $2332.20/mo |
Which should you pick?
You are running this at volume
Qwen3.7 Max
Lowest measured cost per point of capability at $0.0971 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 43.6.
Qwen3.7 Max vs GLM 5.2 FAQ
Which is better, Qwen3.7 Max or GLM 5.2?
Effectively the same quality — GLM 5.2 is the cheaper way to get it. The two are within 0.0 points overall, which is inside the range that effort settings alone move a LiveBench score, so treat them as quality-equivalent. GLM 5.2 lists 49% cheaper per blended million tokens. When quality ties, cost is the whole decision. The two cost measures disagree here, which is worth knowing: GLM 5.2 has the lower sticker price, but Qwen3.7 Max earns each point of capability for less — $0.0971 against $0.1260 — because per-token rates do not predict how many tokens a model actually spends on a task.
Is Qwen3.7 Max cheaper than GLM 5.2?
GLM 5.2 is cheaper. On a 3:1 input:output blend, Qwen3.7 Max lists at $2.21 per million tokens and GLM 5.2 at $1.48 — GLM 5.2 is 49% cheaper. Input and output are priced separately — Qwen3.7 Max charges $1.48 in and $4.42 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.
Qwen3.7 Max vs GLM 5.2: which scores higher on benchmarks?
Qwen3.7 Max scores 73.1 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, Qwen3.7 Max or GLM 5.2?
Qwen3.7 Max. 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. Qwen3.7 Max works out at $0.0971 per point and GLM 5.2 at $0.1260.
Does Qwen3.7 Max or GLM 5.2 have a bigger context window?
They are effectively the same — 1M for Qwen3.7 Max and 1.0M for GLM 5.2.
Do Qwen3.7 Max and GLM 5.2 support prompt caching?
Both publish a cached-input rate: $0.295 per million for Qwen3.7 Max and $0.193 for GLM 5.2, against full input rates of $1.48 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.