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.
| Glm-5.3 zai-org/glm-5.3 | Qwen3.8-27b qwen/qwen3.8-27b | |
|---|---|---|
| Provider | Zai-org | Alibaba Qwen |
| Family | Qwen | |
| Modality | text | text |
| Context window | 1,310,720 tok | 262,144 tok |
| Max output | 131,072 tok | 32,768 tok |
| Released | 2026-08-14 | 2026-08-17 |
| Input price | $1.40 /1M | $0.450 /1M |
| Output price | $4.40 /1M | $3.20 /1M |
| Cache read | — | — |
| Tools | yes | yes |
| Streaming | yes | yes |
| Vision | — | yes |
| JSON mode | yes | yes |
| Reasoning | yes | yes |
| Prompt caching | — | — |
GLM-5.3 is Z.ai's flagship agentic coding model, pairing a 1M-token context window with reasoning, function calling, and structured outputs to power multi-step, tool-driven development workflows.
Qwen 3.8 27B is a 27-billion-parameter instruction-tuned language model from Alibaba's Qwen family, designed for vision, efficient general-purpose text generation and agentic workloads.
# 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 Glm-5.3
client.chat.completions.create(
model="zai-org/glm-5.3",
messages=[{"role":"user","content":"hello"}],
)
# Try Qwen3.8-27b — same client, same key
client.chat.completions.create(
model="qwen/qwen3.8-27b",
messages=[{"role":"user","content":"hello"}],
)