Head-to-Head Model Comparison

DeepSeek V4 vs DeepSeek Reasoner

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial

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DeepSeek V4Profile

DeepSeek ReasonerProfile

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DeepSeek

DeepSeek V4

Model Dasar Bobot Terbuka

DeepSeek V4 dari DeepSeek adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 1M, kecepatan 78 token/detik, indeks kecerdasan 56/100, serta tarif $0.3/$1.

Intelligence Index56 / 100

Input Token:

$0.30 / 1M

Output Token:

$1.20 / 1M

Context Window:

1M tokens

SWE-bench Verified:

62.8%

DeepSeek

DeepSeek Reasoner

Penalaran Bobot Terbuka

DeepSeek Reasoner dari DeepSeek adalah model AI yang dirancang untuk penalaran rantai pemikiran (Chain-of-Thought) berbobot terbuka yang sangat efisien, menghadirkan jendela konteks 1M, kecepatan 52 token/detik, indeks kecerdasan 54/100, se

Intelligence Index54 / 100

Input Token:

$0.28 / 1M

Output Token:

$0.42 / 1M

Context Window:

1M tokens

SWE-bench Verified:

56.5%

Perbedaan Utama Sekilas

DeepSeek Reasoner is 1.9x cheaper for API usage (input + output tokens combined).
DeepSeek V4 is faster at 78 tok/s vs 52 tok/s.
DeepSeek V4 leads on SWE-bench coding (62.8% vs 56.5%).

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

Feature	DeepSeek V4	DeepSeek Reasoner
Intelligence Index Composite reasoning & knowledge score (0-100)	56	54
Provider	DeepSeek	DeepSeek
Input Price Cost per million input tokens	$0.30 / 1M	$0.28 / 1M
Output Price Cost per million output tokens	$1.20 / 1M	$0.42 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.20 / 1M	$0.12 / 1M
Context Window Maximum input + output tokens	1M tokens~1,500 A4 pages of text	1M tokens~1,500 A4 pages of text
Max Output Tokens Per single request	66K	33K
Throughput Speed Tokens generated per second	78 tok/s	52 tok/s
SWE-bench Verified Real-world software engineering	62.8%	56.5%
GPQA Diamond Graduate-level reasoning	78.4%	76.5%
Cached Input Price Prompt caching discount per 1M tokens	$0.03 / 1M	$0.03 / 1M

Siapa Unggul di Mana?

At-a-glance decision framework

Coding

DeepSeek V4

+6.3% SWE-bench lead

Cost

DeepSeek Reasoner

1.9x cheaper monthly runtime

Speed

DeepSeek V4

+50% faster generation

Context

Tied

1M tokens window

Skor Benchmark Terverifikasi

Official evaluation metrics verified by independent third-party research labs.

DeepSeek V4DeepSeek Reasoner

SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

62.8%vs56.5%

GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

78.4%vs76.5%

MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

87.5%vs86.2%

LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1415ptsvs1368pts

Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

14.5%vs13.5%

Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

42%vs37.5%

SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

39.5%vs35%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

65%vs58.5%

AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

89%vs87.5%

Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

88%vs85%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

78 tok/s vs52 tok/s

Latensi & Waktu Respons

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better

DeepSeek V40.60s

DeepSeek Reasoner0.82s

Est. End-to-End Response (500 tokens)TTFT + generation time

DeepSeek V47.01s

DeepSeek Reasoner10.44s

Simulator Biaya Token Interaktif

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

DeepSeek V4 Monthly API Bill

$1.57 / month

$0.3/1M in · $1.2/1M out

DeepSeek Reasoner Monthly API Bill

$0.84 / month

$0.28/1M in · $0.42/1M out

DeepSeek Reasoner saves approximately $0.73 / month (47%) under this workload.1.9x Savings

DeepSeek V4 · Strengths & Key Advantages

Jendela konteks 1M dengan kapasitas output maksimum hingga 66K token (SWE-bench Verified 62.8% · GPQA Diamond 78.4%)
Kecepatan generasi tinggi 78 token/detik dengan latensi token pertama (TTFT) hanya 600 ms
Bobot terbuka (MoE (Colossal)) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama

Considerations & Trade-offs

Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

DeepSeek Reasoner · Strengths & Key Advantages

Jendela konteks 1M dengan kapasitas output maksimum hingga 33K token (SWE-bench Verified 56.5% · GPQA Diamond 76.5%)
Kecepatan generasi tinggi 52 token/detik dengan latensi token pertama (TTFT) hanya 820 ms
Bobot terbuka (671B Total (37B )) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama

Considerations & Trade-offs

Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

Metric	DeepSeek V4	DeepSeek Reasoner
Cost per Task Estimated USD to finish one task	$0.38	$0.22
Output Tokens / Task Tokens generated per task	48K	45K
Reasoning Tokens / Task Thinking tokens burned per task	22K	26K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

Feature	DeepSeek V4	DeepSeek Reasoner
Official API Model ID (SDK)	deepseek-reasoner	deepseek-reasoner
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2026-07	2025-12
Knowledge Cutoff	April 2026	September 2025
Architecture	Next-Gen Sparse MoE with MLA	Reinforcement-Learned Reasoning MoE
Parameters	MoE (Colossal)	671B Total (37B Active)
Enterprise Rate Limits (RPM / TPM)	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)	Flat rate ($0.3/1M in) across full 1000K window	Flat rate ($0.28/1M in) across full 1000K window
Prompt Cache Policy & TTL	Automatic prefix caching at $0.03/1M (no extra cache-write fee)	Automatic prefix caching at $0.028/1M (no extra cache-write fee)
Computer Use / GUI Agent	API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)	API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
MCP & Parallel Tool Calling	Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema	Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & Sandbox	Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools	Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning Support	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
Multimodal Token Billing	Text BPE (~4 chars/token)	Text BPE (~4 chars/token)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 43 GB VRAM (4-bit Quantized) · 151 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 416 GB VRAM (4-bit Quantized) · 1443 GB VRAM (Native BF16)
Safety & Frontier Classification	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
Enterprise Compliance (ZDR/HIPAA/SOC2)	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalities	text	text
Output Modalities	text	text

DeepSeek V4 vs DeepSeek Reasoner: Jawaban Cepat (FAQ)

Which is more intelligent, DeepSeek V4 or DeepSeek Reasoner?

DeepSeek V4 is more intelligent. It scores 56 compared with 54 on the composite Intelligence Index.

Which model is faster?

DeepSeek V4 is faster, generating 78 tokens per second compared with 52 tokens per second.

Which model is cheaper to run?

DeepSeek Reasoner is cheaper at $0.12 per 1M tokens blended, compared with $0.20 (7:2:1 cache:input:output ratio).

Which model has lower latency?

DeepSeek V4 has lower latency, with a time to first token of 0.60s compared with 0.82s.

Kesimpulan

Baik DeepSeek V4 (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun DeepSeek Reasoner (Sangat cocok untuk penalaran rantai pemikiran (Chain-of-Thought) berbobot terbuka yang sangat efisien, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: DeepSeek V4 unggul dalam penalaran dan pemrograman, DeepSeek V4 memberikan kecepatan generasi token lebih tinggi, dan DeepSeek Reasoner lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, DeepSeek V4 atau DeepSeek Reasoner?+

Dalam indeks kecerdasan gabungan dan benchmark coding, DeepSeek V4 memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API DeepSeek V4 vs DeepSeek Reasoner?+

DeepSeek V4 mengenakan biaya $0.30 / $1.20 per 1M token input/output, sedangkan DeepSeek Reasoner sebesar $0.28 / $0.42. Secara keseluruhan, DeepSeek Reasoner lebih hemat biaya.

Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+

DeepSeek V4 mendukung konteks 1M token dibandingkan 1M token pada DeepSeek Reasoner, sementara DeepSeek V4 unggul dalam kecepatan output token per detik.

AI Terminology & Guide

Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

DeepSeek V4 vs DeepSeek Reasoner

Ganti & Bandingkan Model Apa Saja

DeepSeek V4

DeepSeek Reasoner

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

DeepSeek V4 · Strengths & Key Advantages

Considerations & Trade-offs

DeepSeek Reasoner · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

DeepSeek V4 vs DeepSeek Reasoner: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

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