GLM-5.2 vs Qwen3.7 Max
Pricing & Specs
Zhipu AI vs Qwen (Alibaba), side by side from a live registry. Specs below, then benchmark both on your own task.
TL;DR: On a typical task (1K input + 500 output tokens), Qwen3.7 Max is about 1.2x cheaper than GLM-5.2. But per-token price is not cost per result: a cheaper model that needs more tokens, retries, or hand-holding can end up more expensive on your workload. The specs below are facts; which one is better at YOUR task is measurable, not guessable.
API Pricing: GLM-5.2 vs Qwen3.7 Max
| Tokens | GLM-5.2 | Qwen3.7 Max |
|---|---|---|
| Input | $1.4 / 1M | $1.25 / 1M |
| Output | $4.4 / 1M | $3.75 / 1M |
| Cached input | $0.26 / 1M | $0.13 / 1M |
What that means in practice (1K input + 500 output tokens)
Specs Compared
| Spec | GLM-5.2 | Qwen3.7 Max |
|---|---|---|
| Context window | 262.144K tokens | 1M tokens |
| Max output tokens | - | - |
| Input modalities | text | text |
| Knowledge cutoff | - | - |
| Latency (measured) | - | - |
| Reasoning model | Yes | Yes |
| Tool / function calling | Yes | Yes |
| JSON mode | Yes | Yes |
| Prompt caching | No | Yes |
When to Choose Which
FAQ
Which is cheaper, GLM-5.2 or Qwen3.7 Max?
GLM-5.2 costs $1.4/$4.4 per 1M input/output tokens; Qwen3.7 Max costs $1.25/$3.75. On a typical task (1K input + 500 output tokens), Qwen3.7 Max is about 1.2x cheaper than GLM-5.2.
Which has the bigger context window, GLM-5.2 or Qwen3.7 Max?
GLM-5.2 has a 262.144K-token context window; Qwen3.7 Max has 1M tokens.
Is GLM-5.2 better than Qwen3.7 Max?
It depends on the task. Generic leaderboards won't tell you which one wins on YOUR workload. OpenMark lets you benchmark GLM-5.2 and Qwen3.7 Max head to head on your own task with real API calls, no API keys needed, free tier available.
GLM-5.2 or Qwen3.7 Max for YOUR Task?
Spec tables can't answer that. Run both head to head on your actual workload:
real API calls, ranked results with accuracy, cost, and latency. Free tier available.