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o3-deep-research vs Gemini Deep Research

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-deep-research vs Gemini Deep Research: Benchmarks, Pricing & Speed Comparison

o3-deep-research von OpenAI und Gemini Deep Research von Google gehören zu den führenden KI-Modellen. o3-deep-research bietet ein Kontextfenster von 200K Tokens bei API-Preisen von $10.00/$40.00 pro 1M Tokens, während Gemini Deep Research 1.05M Tokens Kontext zu $10.00/$40.00 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

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OpenAI

o3-deep-research

Deep-Reasoning-Engine

o3-deep-research von OpenAI ist ein KI-Modell für tiefgehendes logisches Denken, komplexe Mathematik und wissenschaftliche Analysen mit 200K Kontextfenster, 45 Token/s Durchsatz, einem Intelligenz-Index von 55/100 und $10/$40 pro 1M Token.

Intelligence Index55 / 100
Input Token:

$10.00 / 1M

Output Token:

$40.00 / 1M

Context Window:

200K tokens

SWE-bench Verified:

55%

Google

Gemini Deep Research

Autonomes Agenten-Modell

Gemini Deep Research von Google ist ein KI-Modell für autonome Agenten-Workflows, zuverlässiges Tool-Calling und mehrstufige Automatisierung mit 1.0M Kontextfenster, 60 Token/s Durchsatz, einem Intelligenz-Index von 91/100 und $10/$40 pro 1

Intelligence Index91 / 100
Input Token:

$10.00 / 1M

Output Token:

$40.00 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

N/A

Wichtigste Unterschiede auf einen Blick

  • Both models cost the same under this workload — choose on capability, not price.
  • Gemini Deep Research has a larger context window (1M vs 200K tokens).
  • Gemini Deep Research is faster at 60 tok/s vs 45 tok/s.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
Featureo3-deep-researchGemini Deep Research
Intelligence Index
Composite reasoning & knowledge score (0-100)
5591
Provider
OpenAIGoogle
Input Price
Cost per million input tokens
$10.00 / 1M$10.00 / 1M
Output Price
Cost per million output tokens
$40.00 / 1M$40.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$7.75 / 1M$7.75 / 1M
Context Window
Maximum input + output tokens
200K tokens~300 A4 pages of text1.05M tokens~1,573 A4 pages of text
Max Output Tokens
Per single request
100K66K
Throughput Speed
Tokens generated per second
45 tok/s60 tok/s
SWE-bench Verified
Real-world software engineering
55%N/A
GPQA Diamond
Graduate-level reasoning
74.5%85%
Cached Input Price
Prompt caching discount per 1M tokens
$2.50 / 1M$2.50 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework
Coding

Evaluations ongoing

Cost
Tied
Identical monthly runtime cost
Speed
Gemini Deep Research
+33% faster generation
Context
Gemini Deep Research
1.05M tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

o3-deep-researchGemini Deep Research
SWE-bench Verified (Autonomous Coding)

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

55%vsN/A
GPQA Diamond (Doctorate-Level Science & Math)

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

74.5%vs85%
MMLU-Pro (Multi-task Language Understanding)

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

85.5%vs89.5%
LMSYS Chatbot Arena Elo

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

1336ptsvs1495pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

13.2%vs17.5%
Terminal-Bench (Agentic Coding & Terminal Use)

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

41%vsN/A
SciCode (Scientific Computing)

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

43%vsN/A
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

68%vsN/A
AIME (Advanced Math Competition)

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

87.5%vsN/A
Long-Context Reasoning

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

82%vsN/A
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

45 tok/s vs60 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-deep-research3.40s
Gemini Deep Research1.50s
Est. End-to-End Response (500 tokens)TTFT + generation time
o3-deep-research14.51s
Gemini Deep Research9.83s

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-deep-research Monthly API Bill
$54.75 / month
$10/1M in · $40/1M out
Gemini Deep Research Monthly API Bill
$54.75 / month
$10/1M in · $40/1M out
These models cost the same under this workload — no savings either way.Break-even

o3-deep-research · Strengths & Key Advantages

  • 200K Kontextfenster und bis zu 100K maximale Output-Token (SWE-bench Verified 55% · GPQA Diamond 74.5%)
  • Hohe Generierungsgeschwindigkeit von 45 Token/Sekunde bei nur 3400 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($10/$40 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

Gemini Deep Research · Strengths & Key Advantages

  • 1.0M Kontextfenster und bis zu 66K maximale Output-Token (GPQA Diamond 85% · MMLU-Pro 89.5%)
  • Hohe Generierungsgeschwindigkeit von 60 Token/Sekunde bei nur 1500 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($10/$40 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

Token Use & Task Economics

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

Metrico3-deep-researchGemini Deep Research
Cost per Task
Estimated USD to finish one task
$7.28$8.50
Output Tokens / Task
Tokens generated per task
90K50K
Reasoning Tokens / Task
Thinking tokens burned per task
42K35K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

Featureo3-deep-researchGemini Deep Research
Official API Model ID (SDK)o3-deep-researchmodels/gemini-deep-research
API ProtocolOpenAI Responses API & Chat Completions v1Google Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date2025-062026-04
Knowledge CutoffJune 2024Real-time Google Search Integration
ArchitectureAgentic reasoning transformer with web and file toolsAgentic recursive research and synthesis engine
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)Flat rate ($10/1M in) across full 200K windowStandard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTLAutomatic prefix caching at $2.5/1M (no extra cache-write fee)Implicit + Explicit Context Caching at $2.5/1M with configurable TTL
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)Supported (Screen coordinate grounding, browser & desktop GUI action loops)
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 RAGBuilt-in Google Search Grounding + Managed Python Code Execution Sandbox
Official Fine-Tuning SupportSupervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)Vertex AI Supervised Adapter Tuning & Context Distillation
Multimodal Token BillingText BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAMCloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier ClassificationOpenAI Preparedness Framework Audited & System Card PublishedGoogle DeepMind Frontier Safety Framework Verified
Enterprise Compliance (ZDR/HIPAA/SOC2)SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise TierSOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalitiestext, visiontext, vision
Output Modalitiestexttext

o3-deep-research vs Gemini Deep Research: Schnelle Antworten (FAQ)

Which is more intelligent, o3-deep-research or Gemini Deep Research?

Gemini Deep Research is more intelligent. It scores 91 compared with 55 on the composite Intelligence Index.

Which model is faster?

Gemini Deep Research is faster, generating 60 tokens per second compared with 45 tokens per second.

Which model has lower latency?

Gemini Deep Research has lower latency, with a time to first token of 1.50s compared with 3.40s.

Which model has a larger context window?

Gemini Deep Research has a larger context window, supporting 1.05M tokens compared with 200K tokens.

Fazit

Sowohl o3-deep-research (Optimal für tiefgehendes logisches Denken, komplexe Mathematik und wissenschaftliche Analysen, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Gemini Deep Research (Optimal für autonome Agenten-Workflows, zuverlässiges Tool-Calling und mehrstufige Automatisierung, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Gemini Deep Research führt bei komplexem Reasoning und Coding, Gemini Deep Research liefert den höheren Token-Durchsatz und o3-deep-research ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – o3-deep-research oder Gemini Deep Research?+

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

Wie unterscheiden sich die API-Preise von o3-deep-research und Gemini Deep Research?+

o3-deep-research kostet $10.00 / $40.00 pro 1M Input-/Output-Tokens, Gemini Deep Research liegt bei $10.00 / $40.00. Beim gewichteten Gesamtpreis ist o3-deep-research günstiger.

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

o3-deep-research bietet 200K Tokens Kontext gegenüber 1.05M Tokens bei Gemini Deep Research. Bei der Generierungsgeschwindigkeit liegt Gemini Deep Research 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 Google, Systemkarten und unabhängige Benchmark-Auswertungen (2026).