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 | MiniMax H3 Max Text to Video minimax/h3-max | |
|---|---|---|
| Provider | Zai-org | MiniMax |
| Family | ||
| Modality | text | video |
| Context window | 1,310,720 tok | — |
| Max output | 131,072 tok | — |
| Released | 2026-08-14 | 2026-09-16 |
| Input price | $1.40 /1M | $0.025 /sec |
| Output price | $4.40 /1M | — |
| Cache read | — | — |
| Tools | yes | — |
| Streaming | yes | — |
| Vision | — | — |
| JSON mode | yes | — |
| Reasoning | 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.
fal's H3 Max is a post-trained variant of MiniMax H3, tuned for stronger prompt adherence and better aesthetics while co-optimized with our custom inference stack for higher throughput with no compromises on output quality
# 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 MiniMax H3 Max Text to Video — same client, same key
client.chat.completions.create(
model="minimax/h3-max",
messages=[{"role":"user","content":"hello"}],
)