Pricing, context window, capabilities, and release date — pulled from each provider's public docs. Both are available via the same AIgateway OpenAI-compatible endpoint; flip the model string to switch.
Both models stream in parallel through your own AIgateway key. Tokens, latency, and cost update as they arrive.
| Qwen3.8-Max alibaba/qwen3.8-max | GLM-5.3-Prime zai-org/glm-5.3-prime | |
|---|---|---|
| Provider | Alibaba | Z.ai |
| Family | Qwen | |
| Modality | text | text |
| Context window | 1,000,000 tok | 1,000,000 tok |
| Max output | 131,072 tok | 131,072 tok |
| Released | 2026-08-02 | 2026-09-21 |
| Input price | $2.00 /1M | $2.80 /1M |
| Output price | $6.00 /1M | $8.80 /1M |
| Cache read | — | — |
| Tools | yes | yes |
| Streaming | yes | yes |
| Vision | yes | — |
| JSON mode | yes | yes |
| Reasoning | yes | yes |
| Prompt caching | — | — |
Alibaba's Qwen 3.8 Max is a 2.4-trillion-parameter MoE flagship built for professional-grade coding and long-horizon autonomous work, capable of delivering complete, production-grade projects spanning 10+ days across legal, financial, design, and other specialized domains. Native visual understanding of images and extended video runs through the full plan-execute-verify cycle, served via DashScope's OpenAI-compatible endpoint.
# pip install aigateway-py openai
# aigateway-py: sub-accounts, evals, replays, jobs, webhook verify.
# openai SDK: chat/embeddings/images/audio — drop-in compat per our SDK's own guidance.
from openai import OpenAI
client = OpenAI(
base_url="https://api.aigateway.sh/v1",
api_key="sk-aig-...",
)
# Try Qwen3.8-Max
client.chat.completions.create(
model="alibaba/qwen3.8-max",
messages=[{"role":"user","content":"hello"}],
)
# Try GLM-5.3-Prime — same client, same key
client.chat.completions.create(
model="zai-org/glm-5.3-prime",
messages=[{"role":"user","content":"hello"}],
)