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Qwen3.6 Plus 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.

OpenRouter + LiveBenchAll comparisonsFull leaderboard

GLM 5.2 scores higher, Qwen3.6 Plus costs less — it depends on your workload.

GLM 5.2 is ahead by 4.3 points overall, and Qwen3.6 Plus lists 2.0× cheaper per blended million tokens. Whether 4.3 points is worth that depends on how much a wrong answer costs you.

qwen

Qwen3.6 Plus

Blended / 1M
$0.731
Context
1M
Released
Apr 2, 2026
Overall score
68.9
reasoningtool callingimage inputvideo input

z-ai

GLM 5.2

Blended / 1M
$1.48
Context
1.0M
Released
Jun 16, 2026
Overall score
73.2
reasoningtool callingprompt caching

Specs and pricing

MetricQwen3.6 PlusGLM 5.2
LiveBench overall

Mean of the seven LiveBench category scores, 0–100. Higher is better.

68.973.2win
Cost per point

Measured benchmark spend divided by overall score — dollars per point of capability.

$0.1262$0.1260
Blended price / 1M

3:1 input:output mix, the usual shape of production traffic.

$0.731win$1.48
Input price / 1M$0.325win$0.966
Output price / 1M$1.95win$3.04
Cached input / 1M

Price of an input token served from the prompt cache, where the provider publishes one.

$0.193
Context window1M1.0M
Max output tokens66K131Kwin

Benchmarks by category

An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Qwen3.6 Plus on top, GLM 5.2 below, both out of 100.

Agentic coding
41.4
51.8
Coding
78.2
79.7
Reasoning
75.8
78.6
Mathematics
83.7
89.8
Data analysis
69.9
73.7
Language
75.0
76.2
Instruction following
58.3
62.3

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.

WorkloadQwen3.6 PlusGLM 5.2
Support chatbot

1.2K in / 400 out × 200K requests

$234.00/mo$419.08/mo
RAG assistant

8K in / 600 out × 100K requests

$377.00/mo$645.84/mo
Coding agent

40K in / 4K out × 20K requests

$416.00/mo$582.91/mo
Document extraction

20K in / 1.5K out × 50K requests

$471.25/mo$1155.06/mo
Bulk classification

500 in / 20 out × 5M requests

$1007.50/mo$2332.20/mo
Run these two through the cost calculator

Which should you pick?

Quality matters more than the bill

GLM 5.2

Highest overall LiveBench score of the two at 73.2.

The workload is coding or agentic work

GLM 5.2

Leads on agentic coding — 51.8 against 41.4.

You are cost-constrained

Qwen3.6 Plus

Cheaper on blended list price at $0.731 per million tokens.

Qwen3.6 Plus vs GLM 5.2 FAQ

Which is better, Qwen3.6 Plus or GLM 5.2?

GLM 5.2 scores higher, Qwen3.6 Plus costs less — it depends on your workload. GLM 5.2 is ahead by 4.3 points overall, and Qwen3.6 Plus lists 2.0× cheaper per blended million tokens. Whether 4.3 points is worth that depends on how much a wrong answer costs you.

Is Qwen3.6 Plus cheaper than GLM 5.2?

Qwen3.6 Plus is cheaper. On a 3:1 input:output blend, Qwen3.6 Plus lists at $0.731 per million tokens and GLM 5.2 at $1.48 — Qwen3.6 Plus is 2.0× cheaper. Input and output are priced separately — Qwen3.6 Plus charges $0.325 in and $1.95 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.

Qwen3.6 Plus vs GLM 5.2: which scores higher on benchmarks?

Qwen3.6 Plus scores 68.9 and GLM 5.2 scores 73.2 overall on LiveBench, the mean of its seven categories. That is a 4.3-point lead for GLM 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, Qwen3.6 Plus or GLM 5.2?

They are close. Qwen3.6 Plus costs $0.1262 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 Qwen3.6 Plus or GLM 5.2 have a bigger context window?

They are effectively the same — 1M for Qwen3.6 Plus and 1.0M for GLM 5.2.

Do Qwen3.6 Plus and GLM 5.2 support prompt caching?

GLM 5.2 publishes a cached-input rate of $0.193 per million tokens against a full input rate of $0.966. The catalogue lists no separate cached rate for Qwen3.6 Plus, which means the provider does not price it separately here — not that caching is unavailable.

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.
  • ScoresLiveBench 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.