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o4-mini-deep-research vs o3-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
o4-mini-deep-research vs o3-deep-research: Benchmarks, Pricing & Speed Comparison

o4-mini-deep-research von OpenAI und o3-deep-research von OpenAI gehören zu den führenden KI-Modellen. o4-mini-deep-research bietet ein Kontextfenster von 200K Tokens bei API-Preisen von $2.00/$8.00 pro 1M Tokens, während o3-deep-research 200K 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

o4-mini-deep-research

Deep-Reasoning-Engine

o4-mini-deep-research von OpenAI ist ein KI-Modell für tiefgehendes logisches Denken, komplexe Mathematik und wissenschaftliche Analysen mit 200K Kontextfenster, 70 Token/s Durchsatz, einem Intelligenz-Index von 48/100 und $2/$8 pro 1M Toke

Intelligence Index48 / 100
Input Token:

$2.00 / 1M

Output Token:

$8.00 / 1M

Context Window:

200K tokens

SWE-bench Verified:

48.5%

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%

Wichtigste Unterschiede auf einen Blick

  • o4-mini-deep-research is 5.0x cheaper for API usage (input + output tokens combined).
  • o4-mini-deep-research is faster at 70 tok/s vs 45 tok/s.
  • o3-deep-research leads on SWE-bench coding (55.0% vs 48.5%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
Featureo4-mini-deep-researcho3-deep-research
Intelligence Index
Composite reasoning & knowledge score (0-100)
4855
Provider
OpenAIOpenAI
Input Price
Cost per million input tokens
$2.00 / 1M$10.00 / 1M
Output Price
Cost per million output tokens
$8.00 / 1M$40.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$1.55 / 1M$7.75 / 1M
Context Window
Maximum input + output tokens
200K tokens~300 A4 pages of text200K tokens~300 A4 pages of text
Max Output Tokens
Per single request
100K100K
Throughput Speed
Tokens generated per second
70 tok/s45 tok/s
SWE-bench Verified
Real-world software engineering
48.5%55%
GPQA Diamond
Graduate-level reasoning
68%74.5%
Cached Input Price
Prompt caching discount per 1M tokens
$0.50 / 1M$2.50 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework
Coding
o3-deep-research
+6.5% SWE-bench lead
Cost
o4-mini-deep-research
5.0x cheaper monthly runtime
Speed
o4-mini-deep-research
+56% faster generation
Context
Tied
200K tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

o4-mini-deep-researcho3-deep-research
SWE-bench Verified (Autonomous Coding)

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

48.5%vs55%
GPQA Diamond (Doctorate-Level Science & Math)

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

68%vs74.5%
MMLU-Pro (Multi-task Language Understanding)

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

80.5%vs85.5%
LMSYS Chatbot Arena Elo

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

1318ptsvs1336pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

11.8%vs13.2%
Terminal-Bench (Agentic Coding & Terminal Use)

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

33.5%vs41%
SciCode (Scientific Computing)

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

35%vs43%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

60%vs68%
AIME (Advanced Math Competition)

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

85%vs87.5%
Long-Context Reasoning

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

76%vs82%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

70 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
o4-mini-deep-research2.40s
o3-deep-research3.40s
Est. End-to-End Response (500 tokens)TTFT + generation time
o4-mini-deep-research9.54s
o3-deep-research14.51s

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)
o4-mini-deep-research Monthly API Bill
$10.95 / month
$2/1M in · $8/1M out
o3-deep-research Monthly API Bill
$54.75 / month
$10/1M in · $40/1M out
o4-mini-deep-research saves approximately $43.80 / month (80%) under this workload.5.0x Savings

o4-mini-deep-research · Strengths & Key Advantages

  • 200K Kontextfenster und bis zu 100K maximale Output-Token (SWE-bench Verified 48.5% · GPQA Diamond 68%)
  • Hohe Generierungsgeschwindigkeit von 70 Token/Sekunde bei nur 2400 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($2/$8 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

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

Token Use & Task Economics

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

Metrico4-mini-deep-researcho3-deep-research
Cost per Task
Estimated USD to finish one task
$1.10$7.28
Output Tokens / Task
Tokens generated per task
75K90K
Reasoning Tokens / Task
Thinking tokens burned per task
30K42K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

Featureo4-mini-deep-researcho3-deep-research
Official API Model ID (SDK)o4-mini-deep-researcho3-deep-research
API ProtocolOpenAI Responses API & Chat Completions v1OpenAI Responses API & Chat Completions v1
Release Date2025-062025-06
Knowledge CutoffJune 2024June 2024
ArchitectureCompact agentic reasoning transformerAgentic reasoning transformer with web and file tools
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) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)Flat rate ($2/1M in) across full 200K windowFlat rate ($10/1M in) across full 200K window
Prompt Cache Policy & TTLAutomatic prefix caching at $0.5/1M (no extra cache-write fee)Automatic prefix caching at $2.5/1M (no extra cache-write fee)
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 RAGNative Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning SupportSupervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
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 PublishedOpenAI Preparedness Framework Audited & System Card Published
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

o4-mini-deep-research vs o3-deep-research: Schnelle Antworten (FAQ)

Which is more intelligent, o4-mini-deep-research or o3-deep-research?

o3-deep-research is more intelligent. It scores 55 compared with 48 on the composite Intelligence Index.

Which model is faster?

o4-mini-deep-research is faster, generating 70 tokens per second compared with 45 tokens per second.

Which model is cheaper to run?

o4-mini-deep-research is cheaper at $1.55 per 1M tokens blended, compared with $7.75 (7:2:1 cache:input:output ratio).

Which model has lower latency?

o4-mini-deep-research has lower latency, with a time to first token of 2.40s compared with 3.40s.

Fazit

Sowohl o4-mini-deep-research (Optimal für tiefgehendes logisches Denken, komplexe Mathematik und wissenschaftliche Analysen, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch o3-deep-research (Optimal für tiefgehendes logisches Denken, komplexe Mathematik und wissenschaftliche Analysen, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: o3-deep-research führt bei komplexem Reasoning und Coding, o4-mini-deep-research liefert den höheren Token-Durchsatz und o4-mini-deep-research ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – o4-mini-deep-research oder o3-deep-research?+

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

Wie unterscheiden sich die API-Preise von o4-mini-deep-research und o3-deep-research?+

o4-mini-deep-research kostet $2.00 / $8.00 pro 1M Input-/Output-Tokens, o3-deep-research liegt bei $10.00 / $40.00. Beim gewichteten Gesamtpreis ist o4-mini-deep-research günstiger.

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

o4-mini-deep-research bietet 200K Tokens Kontext gegenüber 200K Tokens bei o3-deep-research. Bei der Generierungsgeschwindigkeit liegt o4-mini-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 OpenAI, Systemkarten und unabhängige Benchmark-Auswertungen (2026).