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Head-to-Head Model Comparison

o4-mini vs o1-mini

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial
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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 dari OpenAI dan o1-mini dari OpenAI adalah dua model AI unggulan. o4-mini menawarkan jendela konteks 200K token dengan harga $1.10/$4.40 per 1 juta token, sedangkan o1-mini menyediakan konteks 128K token seharga $1.10/$4.40 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.

Perbandingan Langsung (VS)

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OpenAI

o4-mini

Mesin Penalaran Mendalam

o4-mini dari OpenAI adalah model AI yang dirancang untuk penalaran logis mendalam, matematika tingkat lanjut, dan analisis ilmiah, menghadirkan jendela konteks 200K, kecepatan 105 token/detik, indeks kecerdasan 50/100, serta tarif $1.1/$4.4

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

Mesin Penalaran Mendalam

o1-mini dari OpenAI adalah model AI yang dirancang untuk penalaran logis mendalam, matematika tingkat lanjut, dan analisis ilmiah, menghadirkan jendela konteks 128K, kecepatan 90 token/detik, indeks kecerdasan 44/100, serta tarif $1.1/$4.4

Intelligence Index44 / 100
Input Token:

$1.10 / 1M

Output Token:

$4.40 / 1M

Context Window:

128K tokens

SWE-bench Verified:

44%

Perbedaan Utama Sekilas

  • 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

Siapa Unggul di Mana?

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

Skor Benchmark Terverifikasi

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

Latensi & Waktu Respons

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

Simulator Biaya Token Interaktif

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

  • Jendela konteks 200K dengan kapasitas output maksimum hingga 100K token (SWE-bench Verified 57.5% · GPQA Diamond 73%)
  • Kecepatan generasi tinggi 105 token/detik dengan latensi token pertama (TTFT) hanya 1800 ms
  • Skema harga API kompetitif (tarif $1.1/$4.4 per 1M token) dengan dukungan cache prompt dan output JSON terstruktur
Considerations & Trade-offs
  • Model cloud proprietari tertutup; bobot model tidak dapat diunduh untuk penggunaan lokal offline
  • Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

o1-mini · Strengths & Key Advantages

  • Jendela konteks 128K dengan kapasitas output maksimum hingga 66K token (SWE-bench Verified 44% · GPQA Diamond 68.5%)
  • Kecepatan generasi tinggi 90 token/detik dengan latensi token pertama (TTFT) hanya 2200 ms
  • Skema harga API kompetitif (tarif $1.1/$4.4 per 1M token) dengan dukungan cache prompt dan output JSON terstruktur
Considerations & Trade-offs
  • Model cloud proprietari tertutup; bobot model tidak dapat diunduh untuk penggunaan lokal offline
  • Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

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

Spesifikasi Teknis

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: Jawaban Cepat (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.

Kesimpulan

Baik o4-mini (Sangat cocok untuk penalaran logis mendalam, matematika tingkat lanjut, dan analisis ilmiah, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun o1-mini (Sangat cocok untuk penalaran logis mendalam, matematika tingkat lanjut, dan analisis ilmiah, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: o4-mini unggul dalam penalaran dan pemrograman, o4-mini memberikan kecepatan generasi token lebih tinggi, dan o4-mini lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, o4-mini atau o1-mini?+

Dalam indeks kecerdasan gabungan dan benchmark coding, o4-mini memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API o4-mini vs o1-mini?+

o4-mini mengenakan biaya $1.10 / $4.40 per 1M token input/output, sedangkan o1-mini sebesar $1.10 / $4.40. Secara keseluruhan, o4-mini lebih hemat biaya.

Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+

o4-mini mendukung konteks 200K token dibandingkan 128K token pada o1-mini, sementara o4-mini unggul dalam kecepatan output token per detik.

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.

Sumber resmi: Dokumentasi API resmi OpenAI & OpenAI serta evaluasi benchmark independen (2026).