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 Pro deepseek/deepseek-v4-pro | Gemini 3.5 Flash-Lite google/gemini-3.5-flash-lite | |
|---|---|---|
| Provider | Deepseek | |
| Family | DeepSeek | Gemini 3 |
| Modality | text | text |
| Context window | 131,072 tok | 1,048,576 tok |
| Max output | 32,768 tok | 65,536 tok |
| Released | 2026-08-03 | 2026-07-21 |
| Input price | $2.40 /1M | $0.300 /1M |
| Output price | $4.80 /1M | $2.50 /1M |
| Cache read | — | — |
| Tools | yes | yes |
| Streaming | yes | yes |
| Vision | — | yes |
| JSON mode | yes | yes |
| Reasoning | yes | — |
| Prompt caching | — | — |
DeepSeek V4 Pro is a high-capability reasoning model from DeepSeek, served via Fireworks infrastructure for production-grade inference.
Gemini 3.5 Flash-Lite is a low-latency, cost-effective multimodal model optimized for high-throughput, low-cost execution for subagent tasks and document parsing.
# 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 Pro
client.chat.completions.create(
model="deepseek/deepseek-v4-pro",
messages=[{"role":"user","content":"hello"}],
)
# Try Gemini 3.5 Flash-Lite — same client, same key
client.chat.completions.create(
model="google/gemini-3.5-flash-lite",
messages=[{"role":"user","content":"hello"}],
)