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DeepSeek-V3-0324 vs DeepSeek V3

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Verificato dal team di ricerca di GetInfoUsUltima verifica: October 2026Politica editoriale
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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 di DeepSeek e DeepSeek V3 di DeepSeek sono due modelli di IA di riferimento. DeepSeek-V3-0324 offre una finestra di contesto di 128K token a $0.27/$1.10 per milione di token, mentre DeepSeek V3 mette a disposizione 128K token di contesto a $0.27/$1.10 per milione di token. Di seguito analizziamo benchmark, latenza e costi.

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DeepSeek

DeepSeek-V3-0324

Modello Base a Pesi Aperti

DeepSeek-V3-0324 di DeepSeek è un modello di IA progettato per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, con finestra di contesto da 128K, velocità di 65 token/s, indice di intelligenza di 48/1

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

Modello Base a Pesi Aperti

DeepSeek V3 di DeepSeek è un modello di IA progettato per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, con finestra di contesto da 128K, velocità di 60 token/s, indice di intelligenza di 44/100 e

Intelligence Index44 / 100
Input Token:

$0.27 / 1M

Output Token:

$1.10 / 1M

Context Window:

128K tokens

SWE-bench Verified:

49.2%

Differenze Chiave a Colpo d’Occhio

  • 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

Chi vince in ogni 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

Punteggi Benchmark Verificati

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

Latenza e Tempo di Risposta

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

Simulatore Interattivo dei Costi Token

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

  • Finestra di contesto da 128K con capacità massima di output fino a 8K token (SWE-bench Verified 52.4% · GPQA Diamond 63.5%)
  • Throughput elevato di 65 token/secondo con latenza al primo token (TTFT) di soli 650 ms
  • Pesi aperti (671B (37B MoE)) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama
Considerations & Trade-offs
  • Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
  • Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

DeepSeek V3 · Strengths & Key Advantages

  • Finestra di contesto da 128K con capacità massima di output fino a 8K token (SWE-bench Verified 49.2% · GPQA Diamond 59.1%)
  • Throughput elevato di 60 token/secondo con latenza al primo token (TTFT) di soli 680 ms
  • Pesi aperti (671B) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama
Considerations & Trade-offs
  • Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
  • Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

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

Specifiche Tecniche

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: Risposte Rapide (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.

Verdetto

Sia DeepSeek-V3-0324 (Ideale per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, sistemi RAG aziendali e integrazioni API scalabili.) sia DeepSeek V3 (Ideale per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: DeepSeek-V3-0324 guida nei benchmark di ragionamento e programmazione, DeepSeek-V3-0324 garantisce una generazione più rapida e DeepSeek-V3-0324 offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, DeepSeek-V3-0324 o DeepSeek V3?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale DeepSeek-V3-0324. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra DeepSeek-V3-0324 e DeepSeek V3?+

DeepSeek-V3-0324 costa $0.27 / $1.10 per milione di token di input/output, mentre DeepSeek V3 costa $0.27 / $1.10. Nel costo combinato, DeepSeek-V3-0324 risulta più economico.

Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+

DeepSeek-V3-0324 supporta 128K token di contesto contro 128K token di DeepSeek V3, mentre DeepSeek-V3-0324 offre una velocità di generazione superiore.

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.

Fonti ufficiali: Documentazione API ufficiale di DeepSeek e DeepSeek, schede di sistema e benchmark indipendenti (2026).