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DeepSeek R1 vs o3-mini

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 R1 vs o3-mini: Benchmarks, Pricing & Speed Comparison

DeepSeek R1 von DeepSeek und o3-mini von OpenAI gehören zu den führenden KI-Modellen. DeepSeek R1 bietet ein Kontextfenster von 128K Tokens bei API-Preisen von $0.55/$2.19 pro 1M Tokens, während o3-mini 200K Tokens Kontext zu $1.10/$4.40 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

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Popular Matchups:DeepSeek R1 vs o3-mini
DeepSeek

DeepSeek R1

Open-Weights-Reasoning-Modell

DeepSeek R1 von DeepSeek ist ein KI-Modell für Open-Weights-Reasoning mit transparenter Gedankenketten-Verarbeitung (Chain-of-Thought) mit 128K Kontextfenster, 45 Token/s Durchsatz, einem Intelligenz-Index von 49/100 und $0.55/$2.19 pro 1M

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

Deep-Reasoning-Engine

o3-mini von OpenAI ist ein KI-Modell für tiefgehendes logisches Denken, komplexe Mathematik und wissenschaftliche Analysen mit 200K Kontextfenster, 85 Token/s Durchsatz, einem Intelligenz-Index von 51/100 und $1.1/$4.4 pro 1M Token.

Intelligence Index51 / 100
Input Token:

$1.10 / 1M

Output Token:

$4.40 / 1M

Context Window:

200K tokens

SWE-bench Verified:

49%

Wichtigste Unterschiede auf einen Blick

  • 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 →
FeatureDeepSeek R1o3-mini
Intelligence Index
Composite reasoning & knowledge score (0-100)
4951
Provider
DeepSeekOpenAI
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 text200K tokens~300 A4 pages of text
Max Output Tokens
Per single request
8K100K
Throughput Speed
Tokens generated per second
45 tok/s85 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

Wer gewinnt in welcher Disziplin?

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

Geprüfte Benchmark-Ergebnisse im Vergleich

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

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 R10.95s
o3-mini1.40s
Est. End-to-End Response (500 tokens)TTFT + generation time
DeepSeek R112.06s
o3-mini7.28s

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 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

  • 128K Kontextfenster und bis zu 8K maximale Output-Token (SWE-bench Verified 49.2% · GPQA Diamond 71.5%)
  • Hohe Generierungsgeschwindigkeit von 45 Token/Sekunde bei nur 950 ms Erstlatenz (TTFT)
  • Offene Modellgewichte (671B Total (37B )) 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

o3-mini · Strengths & Key Advantages

  • 200K Kontextfenster und bis zu 100K maximale Output-Token (SWE-bench Verified 49% · GPQA Diamond 79.7%)
  • Hohe Generierungsgeschwindigkeit von 85 Token/Sekunde bei nur 1400 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($1.1/$4.4 pro 1M Token) inklusive Prompt-Caching, Tool-Calling und strikter JSON-Ausgabe
Considerations & Trade-offs
  • Proprietäres Cloud-Modell ohne Möglichkeit zum lokalen Download der Modellgewichte
  • 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 R1o3-mini
Cost per Task
Estimated USD to finish one task
$0.65$1.30
Output Tokens / Task
Tokens generated per task
45K45K
Reasoning Tokens / Task
Thinking tokens burned per task
25K25K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

FeatureDeepSeek R1o3-mini
Official API Model ID (SDK)deepseek-reasonero3-mini
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI Responses API & Chat Completions v1
Release Date2025-012025-01
Knowledge CutoffJuly 2024October 2023
ArchitectureOpen-Weights Reasoning MoEChain-of-Thought Reasoning Dense
Parameters671B 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 Cloud10,000 – 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 windowFlat rate ($1.1/1M in) across full 200K window
Prompt Cache Policy & TTLAutomatic 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 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 toolsNative Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning SupportFull Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
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)Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier ClassificationOpen-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 ControlSOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalitiestexttext
Output Modalitiestexttext

DeepSeek R1 vs o3-mini: Schnelle Antworten (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.

Fazit

Sowohl DeepSeek R1 (Optimal für Open-Weights-Reasoning mit transparenter Gedankenketten-Verarbeitung (Chain-of-Thought), Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch o3-mini (Optimal für tiefgehendes logisches Denken, komplexe Mathematik und wissenschaftliche Analysen, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: o3-mini führt bei komplexem Reasoning und Coding, o3-mini liefert den höheren Token-Durchsatz und DeepSeek R1 ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – DeepSeek R1 oder o3-mini?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt o3-mini vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von DeepSeek R1 und o3-mini?+

DeepSeek R1 kostet $0.55 / $2.19 pro 1M Input-/Output-Tokens, o3-mini liegt bei $1.10 / $4.40. Beim gewichteten Gesamtpreis ist DeepSeek R1 günstiger.

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

DeepSeek R1 bietet 128K Tokens Kontext gegenüber 200K Tokens bei o3-mini. Bei der Generierungsgeschwindigkeit liegt o3-mini 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 OpenAI, Systemkarten und unabhängige Benchmark-Auswertungen (2026).