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Head-to-Head Model Comparison

DeepSeek-V3-0324 vs DeepSeek V3

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
DeepSeek-V3-0324 vs DeepSeek V3: Benchmarks, Pricing & Speed Comparison

DeepSeek-V3-0324 dari DeepSeek dan DeepSeek V3 dari DeepSeek adalah dua model AI unggulan. DeepSeek-V3-0324 menawarkan jendela konteks 128K token dengan harga $0.27/$1.10 per 1 juta token, sedangkan DeepSeek V3 menyediakan konteks 128K token seharga $0.27/$1.10 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.

Perbandingan Langsung (VS)

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DeepSeek

DeepSeek-V3-0324

Model Dasar Bobot Terbuka

DeepSeek-V3-0324 dari DeepSeek adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 128K, kecepatan 65 token/detik, indeks kecerdasan 48/100, serta tarif $

Intelligence Index48 / 100
Input Token:

$0.27 / 1M

Output Token:

$1.10 / 1M

Context Window:

128K tokens

SWE-bench Verified:

52.4%

DeepSeek

DeepSeek V3

Model Dasar Bobot Terbuka

DeepSeek V3 dari DeepSeek adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 128K, kecepatan 60 token/detik, indeks kecerdasan 44/100, serta tarif $0.27/

Intelligence Index44 / 100
Input Token:

$0.27 / 1M

Output Token:

$1.10 / 1M

Context Window:

128K tokens

SWE-bench Verified:

49.2%

Perbedaan Utama Sekilas

  • Both models cost the same under this workload — choose on capability, not price.
  • DeepSeek-V3-0324 is faster at 65 tok/s vs 60 tok/s.
  • DeepSeek-V3-0324 leads on SWE-bench coding (52.4% vs 49.2%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureDeepSeek-V3-0324DeepSeek V3
Intelligence Index
Composite reasoning & knowledge score (0-100)
4844
Provider
DeepSeekDeepSeek
Input Price
Cost per million input tokens
$0.27 / 1M$0.27 / 1M
Output Price
Cost per million output tokens
$1.10 / 1M$1.10 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.21 / 1M$0.21 / 1M
Context Window
Maximum input + output tokens
128K tokens~192 A4 pages of text128K tokens~192 A4 pages of text
Max Output Tokens
Per single request
8K8K
Throughput Speed
Tokens generated per second
65 tok/s60 tok/s
SWE-bench Verified
Real-world software engineering
52.4%49.2%
GPQA Diamond
Graduate-level reasoning
63.5%59.1%
Cached Input Price
Prompt caching discount per 1M tokens
$0.07 / 1M$0.07 / 1M

Siapa Unggul di Mana?

At-a-glance decision framework
Coding
DeepSeek-V3-0324
+3.2% SWE-bench lead
Cost
Tied
Identical monthly runtime cost
Speed
DeepSeek-V3-0324
+8% faster generation
Context
Tied
128K tokens window

Skor Benchmark Terverifikasi

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

DeepSeek-V3-0324DeepSeek V3
SWE-bench Verified (Autonomous Coding)

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

52.4%vs49.2%
GPQA Diamond (Doctorate-Level Science & Math)

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

63.5%vs59.1%
MMLU-Pro (Multi-task Language Understanding)

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

78.4%vs75.9%
LMSYS Chatbot Arena Elo

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

1320ptsvs1300pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

10.2%vs9.5%
Terminal-Bench (Agentic Coding & Terminal Use)

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

34.8%vs32.5%
SciCode (Scientific Computing)

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

32.1%vs29.5%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

54.8%vs51.7%
AIME (Advanced Math Competition)

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

72%vs68%
Long-Context Reasoning

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

71.5%vs68.3%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

65 tok/s vs60 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-V3-03240.65s
DeepSeek V30.68s
Est. End-to-End Response (500 tokens)TTFT + generation time
DeepSeek-V3-03248.34s
DeepSeek V39.01s

Simulator Biaya Token Interaktif

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

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
DeepSeek-V3-0324 Monthly API Bill
$1.50 / month
$0.27/1M in · $1.1/1M out
DeepSeek V3 Monthly API Bill
$1.50 / month
$0.27/1M in · $1.1/1M out
These models cost the same under this workload — no savings either way.Break-even

DeepSeek-V3-0324 · Strengths & Key Advantages

  • Jendela konteks 128K dengan kapasitas output maksimum hingga 8K token (SWE-bench Verified 52.4% · GPQA Diamond 63.5%)
  • Kecepatan generasi tinggi 65 token/detik dengan latensi token pertama (TTFT) hanya 650 ms
  • Bobot terbuka (671B (37B MoE)) 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 V3 · Strengths & Key Advantages

  • Jendela konteks 128K dengan kapasitas output maksimum hingga 8K token (SWE-bench Verified 49.2% · GPQA Diamond 59.1%)
  • Kecepatan generasi tinggi 60 token/detik dengan latensi token pertama (TTFT) hanya 680 ms
  • Bobot terbuka (671B) 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.

MetricDeepSeek-V3-0324DeepSeek V3
Cost per Task
Estimated USD to finish one task
$0.18$0.14
Output Tokens / Task
Tokens generated per task
45K45K
Reasoning Tokens / Task
Thinking tokens burned per task
0K0K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

FeatureDeepSeek-V3-0324DeepSeek V3
Official API Model ID (SDK)deepseek-chatdeepseek-chat
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2025-032024-12
Knowledge CutoffJanuary 2025July 2024
Architecture671B Mixture-of-Experts (37B active)671B Mixture-of-Experts (37B active)
Parameters671B (37B active MoE)671B
Enterprise Rate Limits (RPM / TPM)Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed CloudUnlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)Flat rate ($0.27/1M in) across full 128K windowFlat rate ($0.27/1M in) across full 128K window
Prompt Cache Policy & TTLAutomatic prefix caching at $0.07/1M (no extra cache-write fee)Automatic prefix caching at $0.07/1M (no extra cache-write fee)
Computer Use / GUI AgentAPI / 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 CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportFull 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 BillingText BPE (~4 chars/token)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMBF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 416 GB VRAM (4-bit Quantized) · 1443 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 ClassificationOpen-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 Control100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestexttext
Output Modalitiestexttext

DeepSeek-V3-0324 vs DeepSeek V3: Jawaban Cepat (FAQ)

Which is more intelligent, DeepSeek-V3-0324 or DeepSeek V3?

DeepSeek-V3-0324 is more intelligent. It scores 48 compared with 44 on the composite Intelligence Index.

Which model is faster?

DeepSeek-V3-0324 is faster, generating 65 tokens per second compared with 60 tokens per second.

Which model has lower latency?

DeepSeek-V3-0324 has lower latency, with a time to first token of 0.65s compared with 0.68s.

Kesimpulan

Baik DeepSeek-V3-0324 (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun DeepSeek V3 (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: DeepSeek-V3-0324 unggul dalam penalaran dan pemrograman, DeepSeek-V3-0324 memberikan kecepatan generasi token lebih tinggi, dan DeepSeek-V3-0324 lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, DeepSeek-V3-0324 atau DeepSeek V3?+

Dalam indeks kecerdasan gabungan dan benchmark coding, DeepSeek-V3-0324 memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API DeepSeek-V3-0324 vs DeepSeek V3?+

DeepSeek-V3-0324 mengenakan biaya $0.27 / $1.10 per 1M token input/output, sedangkan DeepSeek V3 sebesar $0.27 / $1.10. Secara keseluruhan, DeepSeek-V3-0324 lebih hemat biaya.

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

DeepSeek-V3-0324 mendukung konteks 128K token dibandingkan 128K token pada DeepSeek V3, sementara DeepSeek-V3-0324 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 & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.

Sumber resmi: Dokumentasi API resmi DeepSeek & DeepSeek serta evaluasi benchmark independen (2026).