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DeepSeek-V3-0324 vs DeepSeek V3

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Revisado pela equipe de pesquisa do GetInfoUsÚltima revisão: October 2026Política 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 da DeepSeek e DeepSeek V3 da DeepSeek são dois modelos de IA de destaque. O DeepSeek-V3-0324 oferece uma janela de contexto de 128K tokens a $0.27/$1.10 por milhão de tokens, enquanto o DeepSeek V3 oferece 128K tokens de contexto a $0.27/$1.10 por milhão de tokens. Veja abaixo a comparação completa de benchmarks, latência e custos.

Comparação Direta (VS)

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DeepSeek

DeepSeek-V3-0324

Modelo Base de Pesos Abertos

O DeepSeek-V3-0324 da DeepSeek é um modelo de IA projetado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, oferecendo janela de contexto de 128K, velocidade de 65 tokens/s, índice de inteligência de 48/100

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

Modelo Base de Pesos Abertos

O DeepSeek V3 da DeepSeek é um modelo de IA projetado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, oferecendo janela de contexto de 128K, velocidade de 60 tokens/s, índice de inteligência de 44/100 e tar

Intelligence Index44 / 100
Input Token:

$0.27 / 1M

Output Token:

$1.10 / 1M

Context Window:

128K tokens

SWE-bench Verified:

49.2%

Principais Diferenças em Resumo

  • 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

Quem vence em cada categoria?

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

Pontuações de Benchmarks Verificadas

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

Latência e Tempo de Resposta

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

Simulador Interativo de Custo de Tokens

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

  • Janela de contexto de 128K com limite máximo de saída de 8K tokens (SWE-bench Verified 52.4% · GPQA Diamond 63.5%)
  • Taxa de transferência rápida de 65 tokens/segundo com latência inicial (TTFT) de 650 ms
  • Pesos abertos (671B (37B MoE)) para auto-hospedagem local ou em nuvem privada via vLLM, SGLang ou Ollama
Considerations & Trade-offs
  • Requer servidores GPU com memória VRAM adequada para execução local em precisão total
  • Em derivações científicas extremas, modelos pesados dedicados exclusivamente ao raciocínio oferecem maior profundidade

DeepSeek V3 · Strengths & Key Advantages

  • Janela de contexto de 128K com limite máximo de saída de 8K tokens (SWE-bench Verified 49.2% · GPQA Diamond 59.1%)
  • Taxa de transferência rápida de 60 tokens/segundo com latência inicial (TTFT) de 680 ms
  • Pesos abertos (671B) para auto-hospedagem local ou em nuvem privada via vLLM, SGLang ou Ollama
Considerations & Trade-offs
  • Requer servidores GPU com memória VRAM adequada para execução local em precisão total
  • Em derivações científicas extremas, modelos pesados dedicados exclusivamente ao raciocínio oferecem maior profundidade

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

Especificações Técnicas

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: Respostas Rápidas (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.

Veredito

Tanto DeepSeek-V3-0324 (Indicado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, arquiteturas RAG corporativas e integração escalável via API.) quanto DeepSeek V3 (Indicado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, arquiteturas RAG corporativas e integração escalável via API.) entregam excelente desempenho: DeepSeek-V3-0324 lidera em inteligência geral e programação, DeepSeek-V3-0324 oferece geração mais rápida e DeepSeek-V3-0324 apresenta o melhor custo-benefício em produção.

Perguntas Frequentes (FAQ)

Qual é melhor para programação e raciocínio, DeepSeek-V3-0324 ou DeepSeek V3?+

Nas avaliações de inteligência e código, DeepSeek-V3-0324 leva vantagem. Confira os gráficos SWE-bench e GPQA acima para detalhes.

Qual modelo tem a API mais barata, DeepSeek-V3-0324 ou DeepSeek V3?+

O DeepSeek-V3-0324 custa $0.27 / $1.10 por 1M de tokens (entrada/saída) e o DeepSeek V3 custa $0.27 / $1.10. No custo combinado, DeepSeek-V3-0324 é mais econômico.

Qual modelo possui maior janela de contexto e velocidade?+

O DeepSeek-V3-0324 suporta 128K tokens de contexto contra 128K tokens do DeepSeek V3, enquanto DeepSeek-V3-0324 entrega maior velocidade de geração.

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.

Fontes oficiais: Documentação oficial de API da DeepSeek e DeepSeek e benchmarks independentes (2026).