Head-to-Head Model Comparison

DeepSeek R1 vs o3-mini

Benchmarks, API Token Pricing, Context Window & Speed Comparison

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

Cambia e Confronta Qualsiasi Modello

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

DeepSeek R1Profile

o3-miniProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:DeepSeek R1 vs o3-mini

DeepSeek

DeepSeek R1

Ragionamento a Pesi Aperti

DeepSeek R1 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 128K, velocità di 45 token/s, indice di intelligenza di 49/100 e tariffa di

Intelligence Index49 / 100

Input Token:

$0.55 / 1M

Output Token:

$2.19 / 1M

Context Window:

128K tokens

SWE-bench Verified:

49.2%

OpenAI

o3-mini

Motore di Ragionamento Profondo

o3-mini di OpenAI è un modello di IA progettato per il ragionamento logico profondo, la matematica avanzata e l’analisi scientifica, con finestra di contesto da 200K, velocità di 85 token/s, indice di intelligenza di 51/100 e tariffa di $1.

Intelligence Index51 / 100

Input Token:

$1.10 / 1M

Output Token:

$4.40 / 1M

Context Window:

200K tokens

SWE-bench Verified:

49%

Differenze Chiave a Colpo d’Occhio

DeepSeek R1 is 2.1x cheaper for API usage (input + output tokens combined).
o3-mini has a larger context window (200K vs 128K tokens).
o3-mini is faster at 85 tok/s vs 45 tok/s.
DeepSeek R1 leads on SWE-bench coding (49.2% vs 49.0%).

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

Feature	DeepSeek R1	o3-mini
Intelligence Index Composite reasoning & knowledge score (0-100)	49	51
Provider	DeepSeek	OpenAI
Input Price Cost per million input tokens	$0.55 / 1M	$1.10 / 1M
Output Price Cost per million output tokens	$2.19 / 1M	$4.40 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.43 / 1M	$1.05 / 1M
Context Window Maximum input + output tokens	128K tokens~192 A4 pages of text	200K tokens~300 A4 pages of text
Max Output Tokens Per single request	8K	100K
Throughput Speed Tokens generated per second	45 tok/s	85 tok/s
SWE-bench Verified Real-world software engineering	49.2%	49%
GPQA Diamond Graduate-level reasoning	71.5%	79.7%
Cached Input Price Prompt caching discount per 1M tokens	$0.14 / 1M	$0.55 / 1M

Chi vince in ogni categoria?

At-a-glance decision framework

Coding

DeepSeek R1

+0.2% SWE-bench lead

Cost

DeepSeek R1

2.1x cheaper monthly runtime

Speed

o3-mini

+89% faster generation

Context

o3-mini

200K tokens window

Punteggi Benchmark Verificati

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

DeepSeek R1o3-mini

SWE-bench Verified (Autonomous Coding)

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

49.2%vs49%

GPQA Diamond (Doctorate-Level Science & Math)

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

71.5%vs79.7%

MMLU-Pro (Multi-task Language Understanding)

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

84%vs82.4%

LMSYS Chatbot Arena Elo

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

1340ptsvs1310pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

11.4%vs12.8%

Terminal-Bench (Agentic Coding & Terminal Use)

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

32.5%vs32.3%

SciCode (Scientific Computing)

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

29.5%vs29.4%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

51.7%vs51.6%

AIME (Advanced Math Competition)

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

82.2%vs91.7%

Long-Context Reasoning

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

75.6%vs74.2%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

45 tok/s vs85 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 R10.95s

o3-mini1.40s

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

DeepSeek R112.06s

o3-mini7.28s

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 R1 Monthly API Bill

$3.01 / month

$0.55/1M in · $2.19/1M out

o3-mini Monthly API Bill

$6.44 / month

$1.1/1M in · $4.4/1M out

DeepSeek R1 saves approximately $3.43 / month (53%) under this workload.2.1x Savings

DeepSeek R1 · Strengths & Key Advantages

Finestra di contesto da 128K con capacità massima di output fino a 8K token (SWE-bench Verified 49.2% · GPQA Diamond 71.5%)
Throughput elevato di 45 token/secondo con latenza al primo token (TTFT) di soli 950 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

o3-mini · Strengths & Key Advantages

Finestra di contesto da 200K con capacità massima di output fino a 100K token (SWE-bench Verified 49% · GPQA Diamond 79.7%)
Throughput elevato di 85 token/secondo con latenza al primo token (TTFT) di soli 1400 ms
Economia API competitiva ($1.1/$4.4 per 1M di token) con supporto per prompt caching, tool calling e output JSON strutturati

Considerations & Trade-offs

Modello cloud proprietario chiuso; non consente il download locale dei pesi per l’esecuzione offline
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 R1	o3-mini
Cost per Task Estimated USD to finish one task	$0.65	$1.30
Output Tokens / Task Tokens generated per task	45K	45K
Reasoning Tokens / Task Thinking tokens burned per task	25K	25K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

Feature	DeepSeek R1	o3-mini
Official API Model ID (SDK)	deepseek-reasoner	o3-mini
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI Responses API & Chat Completions v1
Release Date	2025-01	2025-01
Knowledge Cutoff	July 2024	October 2023
Architecture	Open-Weights Reasoning MoE	Chain-of-Thought Reasoning Dense
Parameters	671B Total (37B Active)	Confidential
Enterprise Rate Limits (RPM / TPM)	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud	10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)	Flat rate ($0.55/1M in) across full 128K window	Flat rate ($1.1/1M in) across full 200K window
Prompt Cache Policy & TTL	Automatic prefix caching at $0.14/1M (no extra cache-write fee)	Automatic prefix caching at $0.55/1M (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	Native Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning Support	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)	Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
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) · 416 GB VRAM (4-bit Quantized) · 1443 GB VRAM (Native BF16)	Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier Classification	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)	OpenAI Preparedness Framework Audited & System Card Published
Enterprise Compliance (ZDR/HIPAA/SOC2)	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalities	text	text
Output Modalities	text	text

DeepSeek R1 vs o3-mini: Risposte Rapide (FAQ)

Which is more intelligent, DeepSeek R1 or o3-mini?

o3-mini is more intelligent. It scores 51 compared with 49 on the composite Intelligence Index.

Which model is faster?

o3-mini is faster, generating 85 tokens per second compared with 45 tokens per second.

Which model is cheaper to run?

DeepSeek R1 is cheaper at $0.43 per 1M tokens blended, compared with $1.05 (7:2:1 cache:input:output ratio).

Which model has lower latency?

DeepSeek R1 has lower latency, with a time to first token of 0.95s compared with 1.40s.

Which model has a larger context window?

o3-mini has a larger context window, supporting 200K tokens compared with 128K tokens.

Verdetto

Sia DeepSeek R1 (Ideale per il ragionamento Chain-of-Thought a pesi aperti ad alta efficienza algoritmica, sistemi RAG aziendali e integrazioni API scalabili.) sia o3-mini (Ideale per il ragionamento logico profondo, la matematica avanzata e l’analisi scientifica, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: o3-mini guida nei benchmark di ragionamento e programmazione, o3-mini garantisce una generazione più rapida e DeepSeek R1 offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, DeepSeek R1 o o3-mini?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale o3-mini. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra DeepSeek R1 e o3-mini?+

DeepSeek R1 costa $0.55 / $2.19 per milione di token di input/output, mentre o3-mini costa $1.10 / $4.40. Nel costo combinato, DeepSeek R1 risulta più economico.

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

DeepSeek R1 supporta 128K token di contesto contro 200K token di o3-mini, mentre o3-mini 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 R1 vs o3-mini

Cambia e Confronta Qualsiasi Modello

DeepSeek R1

o3-mini

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

DeepSeek R1 · Strengths & Key Advantages

Considerations & Trade-offs

o3-mini · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

DeepSeek R1 vs o3-mini: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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