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 | O3 openai/o3 | |
|---|---|---|
| Provider | Deepseek | OpenAI |
| Family | DeepSeek | o-series |
| Modality | text | text |
| Context window | 1,048,576 tok | 200,000 tok |
| Max output | 32,768 tok | 100,000 tok |
| Released | 2026-10-17 | 2026-05-22 |
| Input price | $0.300 /1M | $2.00 /1M |
| Output price | $1.20 /1M | $8.00 /1M |
| Cache read | — | — |
| Tools | yes | — |
| Streaming | yes | 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.
o3 is OpenAI’s general-purpose reasoning model, balancing strong analytical performance with reasonable latency and cost.
# 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 O3 — same client, same key
client.chat.completions.create(
model="openai/o3",
messages=[{"role":"user","content":"hello"}],
)