Capabilities & Performance
- 1,000,000 token seamless document comprehension
- 85% cheaper than official list price on BatchIn Enterprise
Alibaba flagship reasoning model with native 1M context window and state-of-the-art benchmark capabilities.
Params
1M Context
Context
1M
Max Output
32K
| Lane | Public Rate | Cached |
|---|---|---|
| Realtime API | $0.50 / $1.50 | $0.100 |
| Batch Queue | Batch rates available on rollout | $0.100 |
Prices per 1M tokens. Cached prompt rate applies on prefix hits.
from openai import OpenAI
client = OpenAI(
base_url="https://api.batchin.tech/v1",
api_key="BATCHIN_API_KEY"
)
resp = client.chat.completions.create(
model="qwen3.7-max",
messages=[{"role": "user", "content": "Benchmark system architecture performance and cost profile."}]
)
print(resp.choices[0].message.content)import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://api.batchin.tech/v1",
apiKey: process.env.BATCHIN_API_KEY,
});
const resp = await client.chat.completions.create({
model: "qwen3.7-max",
messages: [{ role: "user", content: "Benchmark system architecture performance and cost profile." }],
});
console.log(resp.choices[0]?.message?.content);curl https://api.batchin.tech/v1/chat/completions \
-H "Authorization: Bearer $BATCHIN_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "qwen3.7-max",
"messages": [{"role":"user","content":"Benchmark system architecture performance and cost profile."}]
}'Balanced workhorse model with 1M context, robust tool use, and enterprise-grade reliability.
Large-scale 480B MoE code generation model tailored for whole-repo refactoring and complex bug fixing.
DeepSeek next-gen MoE reasoning flagship for complex architectural design, autonomous coding loops, and math proofs.