Time to First Token · GLM-5.3 (max)
1507ms
Time to First Token · Qwen3.8 Max
1819ms
GLM-5.3 (max) vs Qwen3.8 Max: The Ultimate Performance & Pricing Comparison
Deep dive into reasoning, benchmarks, and latency insights.
The Final Verdict in the GLM-5.3 (max) vs Qwen3.8 Max Showdown
The current catalog does not contain complete performance evidence for both models, so this page does not declare an overall winner. Use the available fields as comparison signals and validate the models on your own workload.
Model Snapshot
Key decision metrics at a glance.
GLM-5.3 (max)Qwen3.8 Max
6.0
Reasoning6.0
7.0
Coding7.0
5.0
Multimodal5.0
7.0
Long Context7.0
$2.15
Blended Price / 1M tokens$3
—
P95 Latency—
79.593
Tokens per second46.689
Machine-readable comparison data
| Model | Metric | Value | Unit | Source / snapshot |
|---|---|---|---|---|
| GLM-5.3 (max) | Reasoning | 6.0 | benchmark or capability score | Artificial Analysis · current catalog |
| Qwen3.8 Max | Reasoning | 6.0 | benchmark or capability score | Artificial Analysis · current catalog |
| GLM-5.3 (max) | Coding | 7.0 | benchmark or capability score | Artificial Analysis · current catalog |
| Qwen3.8 Max | Coding | 7.0 | benchmark or capability score | Artificial Analysis · current catalog |
| GLM-5.3 (max) | Multimodal | 5.0 | benchmark or capability score | Artificial Analysis · current catalog |
| Qwen3.8 Max | Multimodal | 5.0 | benchmark or capability score | Artificial Analysis · current catalog |
| GLM-5.3 (max) | Long Context | 7.0 | benchmark or capability score | Artificial Analysis · current catalog |
| Qwen3.8 Max | Long Context | 7.0 | benchmark or capability score | Artificial Analysis · current catalog |
| GLM-5.3 (max) | Blended Price / 1M tokens | $2.15 | USD per 1M tokens | Artificial Analysis · current catalog |
| Qwen3.8 Max | Blended Price / 1M tokens | $3 | USD per 1M tokens | Artificial Analysis · current catalog |
| GLM-5.3 (max) | P95 Latency | — | milliseconds | Artificial Analysis · current catalog |
| Qwen3.8 Max | P95 Latency | — | milliseconds | Artificial Analysis · current catalog |
| GLM-5.3 (max) | Tokens per second | 79.593 | tokens per second | Artificial Analysis · current catalog |
| Qwen3.8 Max | Tokens per second | 46.689 | tokens per second | Artificial Analysis · current catalog |
Data provided by Artificial Analysis; live values use the current catalog.
Overall Capabilities
This radar chart visually maps the core capabilities (reasoning, coding, math proxy, multimodal, long context) of `GLM-5.3 (max)` vs `Qwen3.8 Max`.
GLM-5.3 (max)Qwen3.8 Max
Benchmark Breakdown
This grouped bar chart provides a side-by-side comparison for each benchmark metric.
74.871.8
ARTIFICIAL ANALYSIS CODING59.558.1
ARTIFICIAL ANALYSIS INTELLIGENCEGLM-5.3 (max)Qwen3.8 Max
Speed & Latency
Lower time to first token is better; higher tokens per second is better.
Tokens per Second · GLM-5.3 (max)
79.593
Tokens per Second · Qwen3.8 Max
46.689
The Economics of GLM-5.3 (max) vs Qwen3.8 Max
Pricing Breakdown
Compare input and output pricing in USD per 1M tokens.
$1.4$2
Input Pricing$4.4$6
Output PricingGLM-5.3 (max)Qwen3.8 Max
Real-World Cost Scenario
Per run: 1M input tokens + 250k output tokensGLM-5.3 (max)$2.5
Qwen3.8 Max$3.5
GLM-5.3 (max) costs $1 less per run
Your Questions about the GLM-5.3 (max) vs Qwen3.8 Max Comparison
Is `GLM-5.3 (max)` a direct replacement for `Qwen3.8 Max`?
The current GLM-5.3 (max) vs Qwen3.8 Max data does not establish a universal replacement. Compare the available metrics, then validate quality, latency, reliability, and cost on your own workload.
For coding, which is better in the `GLM-5.3 (max) vs Qwen3.8 Max` debate?
The benchmark data in our GLM-5.3 (max) vs Qwen3.8 Max comparison shows GLM-5.3 (max) has a clear advantage, scoring 74.8 on LiveCodeBench versus 71.8.
How was this `GLM-5.3 (max) vs Qwen3.8 Max` comparison conducted?
Our GLM-5.3 (max) vs Qwen3.8 Max comparison uses published benchmark, pricing, speed, and latency fields provided by Artificial Analysis. Missing values remain unavailable, and the result should be checked against your workload.