Head-to-Head Model Comparison

DeepSeek V4 vs DeepSeek Reasoner

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Verificato dal team di ricerca di GetInfoUsUltima verifica: October 2026Politica editoriale

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DeepSeek V4Profile

DeepSeek ReasonerProfile

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DeepSeek

DeepSeek V4

Modello Base a Pesi Aperti

DeepSeek V4 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 1M, velocità di 78 token/s, indice di intelligenza di 56/100 e ta

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

Ragionamento a Pesi Aperti

DeepSeek Reasoner di DeepSeek è un modello di IA progettato per il ragionamento Chain-of-Thought a pesi aperti ad alta efficienza algoritmica, con finestra di contesto da 1M, velocità di 52 token/s, indice di intelligenza di 54/100 e tariff

Intelligence Index54 / 100

Input Token:

$0.28 / 1M

Output Token:

$0.42 / 1M

Context Window:

1M tokens

SWE-bench Verified:

56.5%

Differenze Chiave a Colpo d’Occhio

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

Chi vince in ogni categoria?

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

Punteggi Benchmark Verificati

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

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 V40.60s

DeepSeek Reasoner0.82s

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

DeepSeek V47.01s

DeepSeek Reasoner10.44s

Simulatore Interattivo dei Costi Token

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

Finestra di contesto da 1M con capacità massima di output fino a 66K token (SWE-bench Verified 62.8% · GPQA Diamond 78.4%)
Throughput elevato di 78 token/secondo con latenza al primo token (TTFT) di soli 600 ms
Pesi aperti (MoE (Colossal)) 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 Reasoner · Strengths & Key Advantages

Finestra di contesto da 1M con capacità massima di output fino a 33K token (SWE-bench Verified 56.5% · GPQA Diamond 76.5%)
Throughput elevato di 52 token/secondo con latenza al primo token (TTFT) di soli 820 ms
Pesi aperti (671B Total (37B )) 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.

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

Specifiche Tecniche

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: Risposte Rapide (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.

Verdetto

Sia DeepSeek V4 (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 Reasoner (Ideale per il ragionamento Chain-of-Thought a pesi aperti ad alta efficienza algoritmica, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: DeepSeek V4 guida nei benchmark di ragionamento e programmazione, DeepSeek V4 garantisce una generazione più rapida e DeepSeek Reasoner offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, DeepSeek V4 o DeepSeek Reasoner?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale DeepSeek V4. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra DeepSeek V4 e DeepSeek Reasoner?+

DeepSeek V4 costa $0.30 / $1.20 per milione di token di input/output, mentre DeepSeek Reasoner costa $0.28 / $0.42. Nel costo combinato, DeepSeek Reasoner risulta più economico.

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

DeepSeek V4 supporta 1M token di contesto contro 1M token di DeepSeek Reasoner, mentre DeepSeek V4 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 & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

DeepSeek V4 vs DeepSeek Reasoner

Cambia e Confronta Qualsiasi Modello

DeepSeek V4

DeepSeek Reasoner

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

DeepSeek V4 · Strengths & Key Advantages

Considerations & Trade-offs

DeepSeek Reasoner · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

DeepSeek V4 vs DeepSeek Reasoner: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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