Capabilities & Performance
- Available for self-serve API access.
GPT-5.4 Mini is available through the current BatchIn public model catalog.
Params
1M tokens
Context
N/A
Max Output
N/A
| Routing Lane | SLA & Profile | Public Rate | Cached |
|---|---|---|---|
💎Pro Quality Lane quality_first | 99.95% Official FP16 Deep reasoning & code | $0.48 / $2.88 | N/A |
🚀Turbo Lane speed_optimized | <140ms TTFT · 160 tok/s Voice & agent loops | $0.48 / $2.88 | N/A |
💰Economy Batch Lane cost_optimized | Up to 50% Volume Discount 24h Async Batch Queue | $0.48 / $2.88 (Batch) | N/A |
🛡️Smart Failover Lane hybrid_fallback (Default) | 99.99% Resilient Uptime Zero-downtime auto retry | $0.48 / $2.88 | N/A |
Current BatchIn list price.
from openai import OpenAI
client = OpenAI(
base_url="https://api.batchin.tech/v1",
api_key="BATCHIN_API_KEY",
default_headers={"X-BatchIn-Lane": "turbo"} # "turbo" | "pro" | "economy" | "failover"
)
resp = client.chat.completions.create(
model="gpt-5.4-mini",
messages=[{"role": "user", "content": "Benchmark system architecture performance and cost profile."}],
extra_body={"route_policy": "lowest_latency"} # Turbo low-latency lane
)
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,
defaultHeaders: { "X-BatchIn-Lane": "turbo" }, // "turbo" | "pro" | "economy" | "failover"
});
const resp = await client.chat.completions.create({
model: "gpt-5.4-mini",
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" \
-H "X-BatchIn-Lane: turbo" \
-d '{
"model": "gpt-5.4-mini",
"messages": [{"role":"user","content":"Benchmark system architecture performance and cost profile."}]
}' -i
# Inspect response headers:
# X-BatchIn-Lane: speed_optimized
# X-BatchIn-Latency-Ms: 138
# X-BatchIn-Route-Decision: primary_laneDeepSeek next-gen MoE reasoning flagship for complex architectural design, autonomous coding loops, and math proofs.
High-throughput, ultra-low latency DeepSeek model optimized for rapid tool calls and high-frequency agent loops.
Alibaba 2.4-trillion-parameter MoE flagship model with native 1M context, state-of-the-art autonomous coding, and comprehensive multimodal reasoning. Supports deterministic 0902 snapshot alias.