AI Index

GLM-4.5-AirvsQwen3.7-Plus

Both language models, compared at each maker’s own API list prices. Every figure links to its source.

Compare at your usage

Every model priced for the same request
Prompt per request
Cache hits

8,000 tokens in, 2,000 out

GLM-4.5-Air on Z.ai APIQwen3.7-Plus on Alibaba Cloud Model Studio
Rate appliedBase rateBase rate
Inputper 1M tokens$0.20 $0.276
Cache readper 1M tokens$0.030 $0.056
Outputper 1M tokens$1.10 $1.101
Cost per request$0.0038$0.00441
Per 1,000 requests$3.80$4.41

Every model is priced for the same request at its maker’s own list prices (each with its source and date), so bold marks the cheapest. A model that can’t serve the request is named, not ranked; a condition its seller doesn’t price says which rate was used.

Cost per request as prompts grow

2K tokens out, list price on each maker’s API. Qwen3.7-Plus charges $0.826 / $3.301 above 256K tokens. A line ends at the model’s context window.

0$0.20$0.40$0.60$0.800250K500K750K1Mprompt tokens per request256K8KQwen3.7-Plus$0.00441 at 8KGLM-4.5-Air$0.0038 at 8K
  • GLM-4.5-Air · Z.ai API
  • Qwen3.7-Plus · Alibaba Cloud Model Studio

Where each stands among all scored models

Each line runs from the lowest to the highest score on the board; the tick is the median.

LMArena Text#117 vs #46 of 163Qwen3.7-Plus: 1,456GLM-4.5-Air: 1,373LMArena Text · Coding#116 vs #53 of 163Qwen3.7-Plus: 1,500GLM-4.5-Air: 1,427LMArena Text · Chinese#110 vs #42 of 161Qwen3.7-Plus: 1,503GLM-4.5-Air: 1,405LMArena Text · Creative writing#118 vs #48 of 163Qwen3.7-Plus: 1,431GLM-4.5-Air: 1,327
  • GLM-4.5-Air
  • Qwen3.7-Plus

Key differences

GLM-4.5-AirQwen3.7-Plus
InputMaker’s official API · global · USD
$0.20 / 1M tokens Pricing · as of Nov 1, 2026
$0.276 / 1M tokens Alibaba Cloud Model Studio pricing · as of Nov 2, 2026
OutputMaker’s official API · global · USD
$1.10 / 1M tokens Pricing · as of Nov 1, 2026
$1.101 / 1M tokens Alibaba Cloud Model Studio pricing · as of Nov 2, 2026
Context
128,000 智谱 BigModel：模型概览in Chinese · as of Nov 4, 2026
1,000,000 Qwen3.7 Plus model · as of Nov 2, 2026
Max output
96,000 智谱 BigModel：模型概览in Chinese · as of Nov 4, 2026
131,072 Qwen3.7 Plus model · as of Nov 2, 2026
Input modalities
Text Z.ai LLM GLM-4.5 overview · as of Nov 1, 2026
Text, Image, Video Qwen3.7 Plus model · as of Nov 2, 2026
Cache readMaker’s official API · global · USD
$0.030 / 1M tokens Pricing · as of Nov 1, 2026
$0.056 / 1M tokens Qwen3.7 Plus model · as of Nov 2, 2026

These differences use verified vendor specifications or each maker’s official API on the same region, currency, meter and billing conditions. Different conditions remain side by side in the full table; they do not establish which model is cheaper. Declared features describe support, not measured quality or a recommendation. Missing evidence does not mean a feature is unsupported.

Full comparison

GLM-4.5-Air Zhipu AI Qwen3.7-Plus Alibaba Qwen
Overview
ReleasedJul 28, 2025 Jun 1, 2026
Open weightsYes —
Knowledge cutoffApr 2025 Apr 2025
Specs
Context128K 1M
Max output96K 131.07K
Input modalitiesText Text, Image, Video
Output modalitiesText Text
ReasoningYes Yes
Reasoning can be offYes Yes
Max thinking budgetNot stated 262.14K
Tool useYes Yes
Structured outputNot stated Yes
Pricing · per 1M tokens
Input price$0.20 $0.276Also priced under other conditions: Prompts over 256K tokens
Output price$1.10 $1.101Also priced under other conditions: Prompts over 256K tokens
Cache read price$0.030 $0.056Also priced under other conditions: Explicit cache
Cache write price— $0.344Explicit cache
Blended price (3 input : 1 output)$0.425 $0.48225
Pricing under conditions · per 1M tokens
Input price, long promptsNot offered $0.826Prompts over 256K tokens
Output price, long promptsNot offered $3.301Prompts over 256K tokens
Cited scores
Epoch Capabilities Index— 147.8
LMArena Text1,373 1,456
LMArena Text · Coding1,427 1,500
LMArena Vision— 1,278
LMArena Text · Chinese1,405 1,503
Epoch AI · GPQA Diamond— 87.9%
Epoch AI · FrontierMath Tiers 1–3— 34.4% reasoning off

Headline prices may come from different channels, regions or tiers. Check each source before choosing a model; missing facts stay blank.

Model details and sources

Cited scores