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.
| Deepseek-V4.1-Flash-1017 deepseek/deepseek-v4.1-flash-1017 | GLM-5.3-Prime zai-org/glm-5.3-prime | |
|---|---|---|
| Provider | Deepseek | Z.ai |
| Family | DeepSeek | |
| Modality | text | text |
| Context window | 1,048,576 tok | 1,000,000 tok |
| Max output | 32,768 tok | 131,072 tok |
| Released | 2026-10-17 | 2026-09-21 |
| Input price | $0.300 /1M | $2.80 /1M |
| Output price | $1.20 /1M | $8.80 /1M |
| Cache read | — | — |
| Tools | yes | yes |
| Streaming | yes | yes |
| Vision | yes | — |
| JSON mode | yes | yes |
| Reasoning | yes | yes |
| Prompt caching | — | — |
We introduce DeepSeek-V4.1-Flash, a multimodal Mixture-of-Experts (MoE) model with 552B backbone parameters and support for contexts of up to one million tokens. The model natively processes images and text, and generates text autoregressively.
# 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 Deepseek-V4.1-Flash-1017
client.chat.completions.create(
model="deepseek/deepseek-v4.1-flash-1017",
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"}],
)