DeepSeek V4 Pro

Vendor: DeepSeek Model ID: deepseek-v4-pro Capability: 64K context · tool use · streaming · structured output · reasoning Pricing: per-token, pro tier (live rate) DeepSeek V4 Pro is the model to beat in the open-weight tier on code generation tasks. Reach for it when the problem is structured — implementations of well-defined algorithms, refactors with clear constraints, codebase-style migrations — and the answer either compiles or it doesn’t. For freer-form tasks (architecture design, prose, multi-step planning), GPT-5.5 and Claude Opus 4.8 still have an edge.

Request

curl https://llm.bytespike.ai/v1/chat/completions \
  -H "Authorization: Bearer $BYTESPIKE_API_KEY" \
  -H "content-type: application/json" \
  -d '{
    "model": "deepseek-v4-pro",
    "messages": [{"role": "user", "content": "Implement a thread-safe LRU cache in Rust."}]
  }'

Body parameters

Field	Type	Required	Default	Notes
`model`	string	yes	—	`deepseek-v4-pro`
`messages`	array	yes	—	—
`max_tokens`	integer	no	model max	Max: 16384.
`temperature`	number	no	1.0	—
`tools`	array	no	—	Function calling supported (parallel).
`response_format`	object	no	—	JSON / structured output.
`reasoning`	object	no	—	Optional reasoning chain — set `{"enabled": true}` to enable.
`stream`	boolean	no	false	SSE streaming.

Response

{
  "id": "chatcmpl-…",
  "object": "chat.completion",
  "model": "deepseek-v4-pro",
  "choices": [{"index": 0, "message": {"role": "assistant", "content": "use std::sync::..."}, "finish_reason": "stop"}],
  "usage": {"prompt_tokens": 32, "completion_tokens": 1248, "total_tokens": 1280}
}

Code examples

curl https://llm.bytespike.ai/v1/chat/completions \
  -H "Authorization: Bearer $BYTESPIKE_API_KEY" \
  -H "content-type: application/json" \
  -d '{"model": "deepseek-v4-pro", "messages": [{"role": "user", "content": "Implement a thread-safe LRU cache in Rust."}]}'

from openai import OpenAI

client = OpenAI(base_url="https://llm.bytespike.ai/v1", api_key="$BYTESPIKE_API_KEY")
resp = client.chat.completions.create(
    model="deepseek-v4-pro",
    messages=[{"role": "user", "content": "Implement a thread-safe LRU cache in Rust."}],
)
print(resp.choices[0].message.content)

import OpenAI from "openai"

const client = new OpenAI({
  baseURL: "https://llm.bytespike.ai/v1",
  apiKey: process.env.BYTESPIKE_API_KEY,
})
const resp = await client.chat.completions.create({
  model: "deepseek-v4-pro",
  messages: [
    { role: "user", content: "Implement a thread-safe LRU cache in Rust." },
  ],
})
console.log(resp.choices[0].message.content)

Streaming + caching

"stream": true for SSE. Automatic prompt caching on stable prefixes.

Errors

Code	Trigger	Billed?
400 / 401 / 402 / 422 / 429	Standard	No
5xx	Upstream	No (auto-retry)

When to use

Structured code generation in well-defined languages.
Algorithm implementations, refactors with hard constraints.
For lower latency, see DeepSeek V4 Flash.
For multi-step planning beyond code, see GPT-5.5 or Claude Opus 4.7 (now superseded by Opus 4.8).

Limits

Limit	Value
Context window	64K tokens
Max output	16384 tokens
Supports tool use	Yes (parallel)
Supports vision	No
Supports streaming	Yes
Supports prompt caching	Automatic
Supports reasoning chain	Yes

API Reference

Protocol endpoints

OpenAI

Anthropic

DeepSeek

Moonshot

Zhipu

MiniMax

Image

Video

Account & billing

Utility

Request

Body parameters

Response

Code examples

Streaming + caching

Errors

When to use

Limits

​Request

​Body parameters

​Response

​Code examples

​Streaming + caching

​Errors

​When to use

​Limits

Request

Body parameters

Response

Code examples

Streaming + caching

Errors

When to use

Limits