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 | Kimi-K2.7-Code moonshot/kimi-k2.7-code | |
|---|---|---|
| Provider | Deepseek | Moonshot |
| Family | DeepSeek | Kimi |
| Modality | text | text |
| Context window | 131,072 tok | 262,144 tok |
| Max output | 4,096 tok | 16,384 tok |
| Released | 2026-06-08 | 2026-06-12 |
| Input price | $1.74 /1M | $0.950 /1M |
| Output price | $3.48 /1M | $4.00 /1M |
| Cache read | — | — |
| Tools | — | yes |
| Streaming | yes | yes |
| Vision | — | yes |
| JSON mode | — | yes |
| Reasoning | — | yes |
| Prompt caching | — | — |
DeepSeek V4 Pro is a high-capability reasoning model from DeepSeek, served via Fireworks infrastructure for production-grade inference.
Kimi K2.7 is a frontier-scale open-source 1T parameter model with a 262.1k context window, multi-turn tool calling, vision inputs, and structured outputs for agentic workloads.
# 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 Kimi-K2.7-Code — same client, same key
client.chat.completions.create(
model="moonshot/kimi-k2.7-code",
messages=[{"role":"user","content":"hello"}],
)