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o4-mini vs o1-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
o4-mini vs o1-mini: Benchmarks, Pricing & Speed Comparison

o4-mini von OpenAI und o1-mini von OpenAI gehören zu den führenden KI-Modellen. o4-mini bietet ein Kontextfenster von 200K Tokens bei API-Preisen von $1.10/$4.40 pro 1M Tokens, während o1-mini 128K 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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OpenAI

o4-mini

Deep-Reasoning-Engine

o4-mini von OpenAI ist ein KI-Modell für tiefgehendes logisches Denken, komplexe Mathematik und wissenschaftliche Analysen mit 200K Kontextfenster, 105 Token/s Durchsatz, einem Intelligenz-Index von 50/100 und $1.1/$4.4 pro 1M Token.

Intelligence Index50 / 100
Input Token:

$1.10 / 1M

Output Token:

$4.40 / 1M

Context Window:

200K tokens

SWE-bench Verified:

57.5%

OpenAI

o1-mini

Deep-Reasoning-Engine

o1-mini von OpenAI ist ein KI-Modell für tiefgehendes logisches Denken, komplexe Mathematik und wissenschaftliche Analysen mit 128K Kontextfenster, 90 Token/s Durchsatz, einem Intelligenz-Index von 44/100 und $1.1/$4.4 pro 1M Token.

Intelligence Index44 / 100
Input Token:

$1.10 / 1M

Output Token:

$4.40 / 1M

Context Window:

128K tokens

SWE-bench Verified:

44%

Wichtigste Unterschiede auf einen Blick

  • o4-mini is 1.1x cheaper for API usage (input + output tokens combined).
  • o4-mini has a larger context window (200K vs 128K tokens).
  • o4-mini is faster at 105 tok/s vs 90 tok/s.
  • o4-mini leads on SWE-bench coding (57.5% vs 44.0%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
Featureo4-minio1-mini
Intelligence Index
Composite reasoning & knowledge score (0-100)
5044
Provider
OpenAIOpenAI
Input Price
Cost per million input tokens
$1.10 / 1M$1.10 / 1M
Output Price
Cost per million output tokens
$4.40 / 1M$4.40 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.85 / 1M$1.05 / 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
100K66K
Throughput Speed
Tokens generated per second
105 tok/s90 tok/s
SWE-bench Verified
Real-world software engineering
57.5%44%
GPQA Diamond
Graduate-level reasoning
73%68.5%
Cached Input Price
Prompt caching discount per 1M tokens
$0.28 / 1M$0.55 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework
Coding
o4-mini
+13.5% SWE-bench lead
Cost
o4-mini
1.1x cheaper monthly runtime
Speed
o4-mini
+17% faster generation
Context
o4-mini
200K tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

o4-minio1-mini
SWE-bench Verified (Autonomous Coding)

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

57.5%vs44%
GPQA Diamond (Doctorate-Level Science & Math)

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

73%vs68.5%
MMLU-Pro (Multi-task Language Understanding)

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

83%vs79%
LMSYS Chatbot Arena Elo

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

1325ptsvs1330pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

11.2%vs8.6%
Terminal-Bench (Agentic Coding & Terminal Use)

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

36%vs30.5%
SciCode (Scientific Computing)

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

38.5%vs36%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

65%vs60.5%
AIME (Advanced Math Competition)

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

89.5%vs88%
Long-Context Reasoning

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

74.5%vs70.5%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

105 tok/s vs90 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-mini1.80s
o1-mini2.20s
Est. End-to-End Response (500 tokens)TTFT + generation time
o4-mini6.56s
o1-mini7.76s

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 Monthly API Bill
$6.02 / month
$1.1/1M in · $4.4/1M out
o1-mini Monthly API Bill
$6.44 / month
$1.1/1M in · $4.4/1M out
o4-mini saves approximately $0.41 / month (6%) under this workload.1.1x Savings

o4-mini · Strengths & Key Advantages

  • 200K Kontextfenster und bis zu 100K maximale Output-Token (SWE-bench Verified 57.5% · GPQA Diamond 73%)
  • Hohe Generierungsgeschwindigkeit von 105 Token/Sekunde bei nur 1800 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

o1-mini · Strengths & Key Advantages

  • 128K Kontextfenster und bis zu 66K maximale Output-Token (SWE-bench Verified 44% · GPQA Diamond 68.5%)
  • Hohe Generierungsgeschwindigkeit von 90 Token/Sekunde bei nur 2200 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.

Metrico4-minio1-mini
Cost per Task
Estimated USD to finish one task
$0.72$0.85
Output Tokens / Task
Tokens generated per task
45K45K
Reasoning Tokens / Task
Thinking tokens burned per task
25K26K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

Featureo4-minio1-mini
Official API Model ID (SDK)o4-minio1-mini
API ProtocolOpenAI Responses API & Chat Completions v1OpenAI Responses API & Chat Completions v1
Release Date2025-042024-09
Knowledge CutoffJune 2024October 2023
ArchitectureCompact reasoning transformerReasoning transformer with hidden chain-of-thought
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 ($1.1/1M in) across full 200K windowFlat rate ($1.1/1M in) across full 128K window
Prompt Cache Policy & TTLAutomatic prefix caching at $0.275/1M (no extra cache-write fee)Automatic prefix caching at $0.55/1M (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 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)
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
Output Modalitiestexttext

o4-mini vs o1-mini: Schnelle Antworten (FAQ)

Which is more intelligent, o4-mini or o1-mini?

o4-mini is more intelligent. It scores 50 compared with 44 on the composite Intelligence Index.

Which model is faster?

o4-mini is faster, generating 105 tokens per second compared with 90 tokens per second.

Which model is cheaper to run?

o4-mini is cheaper at $0.85 per 1M tokens blended, compared with $1.05 (7:2:1 cache:input:output ratio).

Which model has lower latency?

o4-mini has lower latency, with a time to first token of 1.80s compared with 2.20s.

Which model has a larger context window?

o4-mini has a larger context window, supporting 200K tokens compared with 128K tokens.

Fazit

Sowohl o4-mini (Optimal für tiefgehendes logisches Denken, komplexe Mathematik und wissenschaftliche Analysen, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch o1-mini (Optimal für tiefgehendes logisches Denken, komplexe Mathematik und wissenschaftliche Analysen, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: o4-mini führt bei komplexem Reasoning und Coding, o4-mini liefert den höheren Token-Durchsatz und o4-mini ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – o4-mini oder o1-mini?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt o4-mini vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von o4-mini und o1-mini?+

o4-mini kostet $1.10 / $4.40 pro 1M Input-/Output-Tokens, o1-mini liegt bei $1.10 / $4.40. Beim gewichteten Gesamtpreis ist o4-mini günstiger.

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

o4-mini bietet 200K Tokens Kontext gegenüber 128K Tokens bei o1-mini. Bei der Generierungsgeschwindigkeit liegt o4-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 OpenAI und OpenAI, Systemkarten und unabhängige Benchmark-Auswertungen (2026).