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 | Kimi K3 moonshot/kimi-k3 | |
|---|---|---|
| Provider | Deepseek | Moonshot |
| Family | DeepSeek | |
| Modality | text | text |
| Context window | 1,048,576 tok | 1,048,576 tok |
| Max output | 32,768 tok | 32,768 tok |
| Released | 2026-10-17 | 2026-07-16 |
| Input price | $0.300 /1M | $3.00 /1M |
| Output price | $1.20 /1M | $15.00 /1M |
| Cache read | — | — |
| Tools | yes | yes |
| Streaming | yes | yes |
| Vision | yes | 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.
Kimi K3 is Moonshot's flagship 2.8 trillion-parameter model, built on Kimi Delta Attention (a hybrid linear attention mechanism) with Attention Residuals. It offers native visual understanding, always-on reasoning, and a 1M-token context window for long-horizon coding, knowledge work, and deep reasoning tasks.
# 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 Kimi K3 — same client, same key
client.chat.completions.create(
model="moonshot/kimi-k3",
messages=[{"role":"user","content":"hello"}],
)