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o3-pro vs DeepSeek R1

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
o3-pro vs DeepSeek R1: Benchmarks, Pricing & Speed Comparison

o3-pro von OpenAI und DeepSeek R1 von DeepSeek gehören zu den führenden KI-Modellen. o3-pro bietet ein Kontextfenster von 200K Tokens bei API-Preisen von $20.00/$80.00 pro 1M Tokens, während DeepSeek R1 128K Tokens Kontext zu $0.55/$2.19 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

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OpenAI

o3-pro

Deep-Reasoning-Engine

o3-pro von OpenAI ist ein KI-Modell für tiefgehendes logisches Denken, komplexe Mathematik und wissenschaftliche Analysen mit 200K Kontextfenster, 30 Token/s Durchsatz, einem Intelligenz-Index von 58/100 und $20/$80 pro 1M Token.

Intelligence Index58 / 100
Input Token:

$20.00 / 1M

Output Token:

$80.00 / 1M

Context Window:

200K tokens

SWE-bench Verified:

71.5%

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%

Wichtigste Unterschiede auf einen Blick

  • DeepSeek R1 is 36.4x cheaper for API usage (input + output tokens combined).
  • o3-pro has a larger context window (200K vs 128K tokens).
  • DeepSeek R1 is faster at 45 tok/s vs 30 tok/s.
  • o3-pro leads on SWE-bench coding (71.5% vs 49.2%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
Featureo3-proDeepSeek R1
Intelligence Index
Composite reasoning & knowledge score (0-100)
5849
Provider
OpenAIDeepSeek
Input Price
Cost per million input tokens
$20.00 / 1M$0.55 / 1M
Output Price
Cost per million output tokens
$80.00 / 1M$2.19 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$15.50 / 1M$0.43 / 1M
Context Window
Maximum input + output tokens
200K tokens~300 A4 pages of text128K tokens~192 A4 pages of text
Max Output Tokens
Per single request
100K8K
Throughput Speed
Tokens generated per second
30 tok/s45 tok/s
SWE-bench Verified
Real-world software engineering
71.5%49.2%
GPQA Diamond
Graduate-level reasoning
79.5%71.5%
Cached Input Price
Prompt caching discount per 1M tokens
$5.00 / 1M$0.14 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework
Coding
o3-pro
+22.3% SWE-bench lead
Cost
DeepSeek R1
36.4x cheaper monthly runtime
Speed
DeepSeek R1
+50% faster generation
Context
o3-pro
200K tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

o3-proDeepSeek R1
SWE-bench Verified (Autonomous Coding)

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

71.5%vs49.2%
GPQA Diamond (Doctorate-Level Science & Math)

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

79.5%vs71.5%
MMLU-Pro (Multi-task Language Understanding)

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

88.5%vs84%
LMSYS Chatbot Arena Elo

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

1352ptsvs1340pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

13.6%vs11.4%
Terminal-Bench (Agentic Coding & Terminal Use)

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

47.5%vs32.5%
SciCode (Scientific Computing)

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

45.5%vs29.5%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

73%vs51.7%
AIME (Advanced Math Competition)

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

93%vs82.2%
Long-Context Reasoning

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

80.5%vs75.6%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

30 tok/s vs45 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
o3-pro5.20s
DeepSeek R10.95s
Est. End-to-End Response (500 tokens)TTFT + generation time
o3-pro21.87s
DeepSeek R112.06s

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)
o3-pro Monthly API Bill
$109.50 / month
$20/1M in · $80/1M out
DeepSeek R1 Monthly API Bill
$3.01 / month
$0.55/1M in · $2.19/1M out
DeepSeek R1 saves approximately $106.49 / month (97%) under this workload.36.4x Savings

o3-pro · Strengths & Key Advantages

  • 200K Kontextfenster und bis zu 100K maximale Output-Token (SWE-bench Verified 71.5% · GPQA Diamond 79.5%)
  • Hohe Generierungsgeschwindigkeit von 30 Token/Sekunde bei nur 5200 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($20/$80 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 hohem Anfragevolumen empfiehlt sich der Einsatz von Prompt-Caching und Batch-API zur Kostenoptimierung

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

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

Metrico3-proDeepSeek R1
Cost per Task
Estimated USD to finish one task
$11.68$0.65
Output Tokens / Task
Tokens generated per task
58K45K
Reasoning Tokens / Task
Thinking tokens burned per task
38K25K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

Featureo3-proDeepSeek R1
Official API Model ID (SDK)o3-prodeepseek-reasoner
API ProtocolOpenAI Responses API & Chat Completions v1OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2025-062025-01
Knowledge CutoffJune 2024July 2024
ArchitectureHigh-effort reasoning transformerOpen-Weights Reasoning MoE
ParametersConfidential671B Total (37B Active)
Enterprise Rate Limits (RPM / TPM)10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPMUnlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)Flat rate ($20/1M in) across full 200K windowFlat rate ($0.55/1M in) across full 128K window
Prompt Cache Policy & TTLAutomatic prefix caching at $5/1M (no extra cache-write fee)Automatic prefix caching at $0.14/1M (no extra cache-write fee)
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)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 & SandboxNative Web Search tool + Hosted Code Interpreter & File Search RAGSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportSupervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
Multimodal Token BillingText BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMCloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)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 ClassificationOpenAI Preparedness Framework Audited & System Card PublishedOpen-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
Enterprise Compliance (ZDR/HIPAA/SOC2)SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestext, visiontext
Output Modalitiestexttext

o3-pro vs DeepSeek R1: Schnelle Antworten (FAQ)

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

o3-pro is more intelligent. It scores 58 compared with 49 on the composite Intelligence Index.

Which model is faster?

DeepSeek R1 is faster, generating 45 tokens per second compared with 30 tokens per second.

Which model is cheaper to run?

DeepSeek R1 is cheaper at $0.43 per 1M tokens blended, compared with $15.50 (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 5.20s.

Which model has a larger context window?

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

Fazit

Sowohl o3-pro (Optimal für tiefgehendes logisches Denken, komplexe Mathematik und wissenschaftliche Analysen, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch DeepSeek R1 (Optimal für Open-Weights-Reasoning mit transparenter Gedankenketten-Verarbeitung (Chain-of-Thought), Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: o3-pro führt bei komplexem Reasoning und Coding, DeepSeek R1 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 – o3-pro oder DeepSeek R1?+

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

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

o3-pro kostet $20.00 / $80.00 pro 1M Input-/Output-Tokens, DeepSeek R1 liegt bei $0.55 / $2.19. Beim gewichteten Gesamtpreis ist DeepSeek R1 günstiger.

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

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