DeepSeek V4 Flash API
Released 2026 | 393,216 Tokens context | V4 family parameters
DeepSeek V4 Flash API enables low-latency chat applications, high-volume assistant workloads, and general-purpose reasoning and drafting. DeepSeek V4 Flash is a fast, cost-efficient variant in the DeepSeek V4 family, built for high-throughput chat and agent workflows. It is tuned for production traffic where cost per token and responsive turn-taking matter—customer-facing assistants, internal copilots, and agent flows that combine natural language with tools, APIs, or multi-step planning. The model keeps a strong quality-to-cost balance for sustained workloads when you do not need the heaviest tier. Call it through the Qubrid serverless chat API on NVIDIA GPU infrastructure with streaming, long-context support, and OpenAI-compatible parameters. Standout strengths include Lower token pricing for scale and Good quality-to-cost balance. It is optimized for production agent and assistant workloads where response quality, latency, and predictable operating cost all matter.
from openai import OpenAI # Initialize the OpenAI client with Qubrid base URL client = OpenAI( base_url="https://platform.qubrid.com/v1", api_key="QUBRID_API_KEY", ) stream = client.chat.completions.create( model="deepseek-ai/DeepSeek-V4-Flash", messages=[ { "role": "user", "content": "Explain quantum computing in simple terms" } ], max_tokens=393216, temperature=1, top_p=1, stream=True ) for chunk in stream: if chunk.choices and chunk.choices[0].delta.content: print(chunk.choices[0].delta.content, end="", flush=True) print("\n")from openai import OpenAI # Initialize the OpenAI client with Qubrid base URL client = OpenAI( base_url="https://platform.qubrid.com/v1", api_key="QUBRID_API_KEY", ) stream = client.chat.completions.create( model="deepseek-ai/DeepSeek-V4-Flash", messages=[ { "role": "user", "content": "Explain quantum computing in simple terms" } ], max_tokens=393216, temperature=1, top_p=1, stream=True ) for chunk in stream: if chunk.choices and chunk.choices[0].delta.content: print(chunk.choices[0].delta.content, end="", flush=True) print("\n") Enterprise
Platform Integration
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