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DeepSeek-V3-0324 vs DeepSeek V3

Benchmarks, API Token Pricing, Context Window & Speed Comparison

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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 von DeepSeek und DeepSeek V3 von DeepSeek gehören zu den führenden KI-Modellen. DeepSeek-V3-0324 bietet ein Kontextfenster von 128K Tokens bei API-Preisen von $0.27/$1.10 pro 1M Tokens, während DeepSeek V3 128K Tokens Kontext zu $0.27/$1.10 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

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DeepSeek

DeepSeek-V3-0324

Open-Weights-Basismodell

DeepSeek-V3-0324 von DeepSeek ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 128K Kontextfenster, 65 Token/s Durchsatz, einem Intelligenz-Index von 48/100 und $0.27/$1.1 pro 1M Token

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

Open-Weights-Basismodell

DeepSeek V3 von DeepSeek ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 128K Kontextfenster, 60 Token/s Durchsatz, einem Intelligenz-Index von 44/100 und $0.27/$1.1 pro 1M Token.

Intelligence Index44 / 100
Input Token:

$0.27 / 1M

Output Token:

$1.10 / 1M

Context Window:

128K tokens

SWE-bench Verified:

49.2%

Wichtigste Unterschiede auf einen Blick

  • 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

Wer gewinnt in welcher Disziplin?

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

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Latenz & Antwortzeit

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

Interaktiver Token-Kosten-Simulator

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

  • 128K Kontextfenster und bis zu 8K maximale Output-Token (SWE-bench Verified 52.4% · GPQA Diamond 63.5%)
  • Hohe Generierungsgeschwindigkeit von 65 Token/Sekunde bei nur 650 ms Erstlatenz (TTFT)
  • Offene Modellgewichte (671B (37B MoE)) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama
Considerations & Trade-offs
  • Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
  • Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

DeepSeek V3 · Strengths & Key Advantages

  • 128K Kontextfenster und bis zu 8K maximale Output-Token (SWE-bench Verified 49.2% · GPQA Diamond 59.1%)
  • Hohe Generierungsgeschwindigkeit von 60 Token/Sekunde bei nur 680 ms Erstlatenz (TTFT)
  • Offene Modellgewichte (671B) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama
Considerations & Trade-offs
  • Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
  • Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

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

Technische Spezifikationen

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: Schnelle Antworten (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.

Fazit

Sowohl DeepSeek-V3-0324 (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch DeepSeek V3 (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: DeepSeek-V3-0324 führt bei komplexem Reasoning und Coding, DeepSeek-V3-0324 liefert den höheren Token-Durchsatz und DeepSeek-V3-0324 ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – DeepSeek-V3-0324 oder DeepSeek V3?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt DeepSeek-V3-0324 vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von DeepSeek-V3-0324 und DeepSeek V3?+

DeepSeek-V3-0324 kostet $0.27 / $1.10 pro 1M Input-/Output-Tokens, DeepSeek V3 liegt bei $0.27 / $1.10. Beim gewichteten Gesamtpreis ist DeepSeek-V3-0324 günstiger.

Welches Modell hat das größere Kontextfenster und die höhere Geschwindigkeit?+

DeepSeek-V3-0324 bietet 128K Tokens Kontext gegenüber 128K Tokens bei DeepSeek V3. Bei der Generierungsgeschwindigkeit liegt DeepSeek-V3-0324 vorn.

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.

Offizielle Quellen: Offizielle API-Dokumentation von DeepSeek und DeepSeek, Systemkarten und unabhängige Benchmark-Auswertungen (2026).