DeepSeek V4 Pro / 채팅
Large-scale MoE model with 1.6T total parameters and 49B activated, supporting a 1M-token context window . Designed for advanced reasoning, coding, and long-horizon agent workflows . Top performance on GPQA Diamond (88.8%) and Terminal-Bench Hard (46.2%)
Playground Chat
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DeepSeek V4 ProDeep Reasoning with Logprobs
DeepSeek V4 Pro offers thinking toggles, high or max reasoning effort, logprobs evaluation signals, and production sampling for demanding analytical workloads.
At a Glance
V4 Pro Reasoning Stack
Analytical depth with evaluation hooks.
High or Max Effort
Dial reasoning between high and max for multi-step proofs and hard analysis.
Thinking Toggle
Enable or disable deep thinking per request depending on task complexity.
Logprob Evaluation
logprobs and top_logprobs expose token probability signals for confidence scoring.
Production Sampling
Temperature, top-p, max tokens, and stop sequences for controlled outputs.
How It Works
Deep analysis in four steps.
Set Thinking & Effort
Enable thinking and choose high or max depth.
Stream or Wait
Stream for long analyses; single-shot for batch evals.
Collect Logprobs
Use probability signals for confidence scoring.
Sample Responsibly
Tune temperature and top-p for tone control.
V4 Pro Domains
Where analytical depth is the product.
Formal Reasoning
Max effort for proofs and multi-hop analysis.
Eval Pipelines
Logprobs for confidence and regression tests.
Research Synthesis
High effort for literature and market synthesis.
Agent Decisions
Reasoned tool choice with probability signals.
Prompt Tips
Get more from V4 Pro reasoning.
High and Max effort spend tokens more usefully when you request proof paths.
Place documents before instructions for more reliable following.
Everyday Q&A is faster at high effort or with thinking off.
DeepSeek V4 Pro Quickstart
High / max reasoning with logprobs.
curl -X POST "https://api.powertokens.ai/v1/chat/completions" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "deepseek-v4-pro",
"messages": [
{"role": "user", "content": "Walk through a careful proof of the AM-GM inequality for two variables."}
],
"stream": true,
"temperature": 0.7
}'Technical Specifications
Confirmed parameters and runtime execution protocols.
가격 상세
이 모델의 실제 요금은 API 요청에서 전달된 특정 매개변수를 기반으로 동적으로 계산됩니다. 아래는 구체적인 조합과 해당 가격입니다:
- 주중(월~금) 09:00 - 12:00, 14:00 - 18:00 (UTC+8) 사이에 요청된 경우 2배의 가격 배율이 적용됩니다。
| 모달리티 | 입력 크레딧 | 출력 크레딧 | 입력 가격 | 출력 가격 | 암시적 캐시 적중 | 명시적 캐시 적중 | 캐시 생성 |
|---|---|---|---|---|---|---|---|
| Standard | 660/ 1M Tokens | 1,980/ 1M Tokens | $0.660 | $1.980 | $0.022 22/ 1M Tokens | $0.022 22/ 1M Tokens | -- |
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Frequently Asked Questions
Everything you need to know before integrating this model.
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