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 | Qwen-Image-2.1-Pro alibaba/qwen-image-2.1-pro | |
|---|---|---|
| Provider | Deepseek | Alibaba |
| Family | DeepSeek | |
| Modality | text | image |
| Context window | 1,048,576 tok | — |
| Max output | 32,768 tok | — |
| Released | 2026-10-17 | 2026-10-10 |
| Input price | $0.300 /1M | $0.040 /img |
| Output price | $1.20 /1M | — |
| Cache read | — | — |
| Tools | yes | — |
| Streaming | yes | — |
| Vision | yes | — |
| JSON mode | yes | — |
| Reasoning | 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 Qwen-Image-2.1-Pro — same client, same key
client.chat.completions.create(
model="alibaba/qwen-image-2.1-pro",
messages=[{"role":"user","content":"hello"}],
)