OpenAI o1 vs o1-mini
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
OpenAI o1 by OpenAI and o1-mini by OpenAI represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, o1-mini holds a notable pricing advantage for token ingestion. On output generation, o1-mini is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, OpenAI o1 provides an expanded memory horizon with 200K tokens. In latency-sensitive production environments, o1-mini leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
원하는 AI 모델 선택 및 실시간 비교
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
OpenAI o1
심층 추론 모델OpenAI의 OpenAI o1 모델은 200K 토큰 컨텍스트 윈도우와 입력 $15/1M 토큰, 출력 $60/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$15.00 / 1M
$60.00 / 1M
200K tokens
48.9%
o1-mini
심층 추론 모델OpenAI의 o1-mini 모델은 128K 토큰 컨텍스트 윈도우와 입력 $1.1/1M 토큰, 출력 $4.4/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$1.10 / 1M
$4.40 / 1M
128K tokens
44%
한눈에 보는 핵심 차이점
- o1-mini is 13.6x cheaper for API usage (input + output tokens combined).
- OpenAI o1 has a larger context window (200K vs 128K tokens).
- o1-mini is faster at 90 tok/s vs 60 tok/s.
- OpenAI o1 leads on SWE-bench coding (48.9% vs 44.0%).
| Feature | OpenAI o1 | o1-mini |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 55 | 44 |
Provider | OpenAI | OpenAI |
Input Price Cost per million input tokens | $15.00 / 1M | $1.10 / 1M |
Output Price Cost per million output tokens | $60.00 / 1M | $4.40 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $14.25 / 1M | $1.05 / 1M |
Context Window Maximum input + output tokens | 200K tokens~300 A4 pages of text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 100K | 66K |
Throughput Speed Tokens generated per second | 60 tok/s | 90 tok/s |
SWE-bench Verified Real-world software engineering | 48.9% | 44% |
GPQA Diamond Graduate-level reasoning | 77.3% | 68.5% |
Cached Input Price Prompt caching discount per 1M tokens | $7.50 / 1M | $0.55 / 1M |
부문별 우위 모델
At-a-glance decision framework검증된 벤치마크 점수 비교
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
지연 시간(Latency) 및 응답 속도
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
실시간 API 토큰 비용 시뮬레이터
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
OpenAI o1 · Strengths & Key Advantages
- OpenAI의 첨단 Chain-of-Thought Reasoning Foundation 아키텍처와 최대 200K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $15/1M, 출력 $60/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
o1-mini · Strengths & Key Advantages
- OpenAI의 첨단 Reasoning transformer with hidden chain-of-thought 아키텍처와 최대 128K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $1.1/1M, 출력 $4.4/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | OpenAI o1 | o1-mini |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.20 | $0.85 |
Output Tokens / Task Tokens generated per task | 24K | 45K |
Reasoning Tokens / Task Thinking tokens burned per task | 15K | 26K |
기술 사양 (Specifications)
Architecture, release timeline, capabilities, and modalities.
| Feature | OpenAI o1 | o1-mini |
|---|---|---|
| Official API Model ID (SDK) | o1 | o1-mini |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2024-12 | 2024-09 |
| Knowledge Cutoff | October 2023 | October 2023 |
| Architecture | Chain-of-Thought Reasoning Foundation | Reasoning transformer with hidden chain-of-thought |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($15/1M in) across full 200K window | Flat rate ($1.1/1M in) across full 128K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $7.5/1M (no extra cache-write fee) | Automatic prefix caching at $0.55/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Native Web Search tool + Hosted Code Interpreter & File Search RAG | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | Cloud-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 Classification | OpenAI Preparedness Framework Audited & System Card Published | OpenAI Preparedness Framework Audited & System Card Published |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text, vision | text |
| Output Modalities | text | text |
OpenAI o1 vs o1-mini: 핵심 요약 문답 (FAQ)
Which is more intelligent, OpenAI o1 or o1-mini?
OpenAI o1 is more intelligent. It scores 55 compared with 44 on the composite Intelligence Index.
Which model is faster?
o1-mini is faster, generating 90 tokens per second compared with 60 tokens per second.
Which model is cheaper to run?
o1-mini is cheaper at $1.05 per 1M tokens blended, compared with $14.25 (7:2:1 cache:input:output ratio).
Which model has lower latency?
o1-mini has lower latency, with a time to first token of 2.20s compared with 3.20s.
Which model has a larger context window?
OpenAI o1 has a larger context window, supporting 200K tokens compared with 128K tokens.
종합 평가
Verdict & Model Selection Recommendation: Choose OpenAI o1 if your priority is Doctorate-level scientific research, competitive algorithmic coding, and high-stakes mathematical proofs. Its performance in demanding tasks justifies its operational token cost. Choose o1-mini if your workflow prioritizes Budget STEM and coding reasoning where cost matters. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
자주 묻는 질문 (FAQ)
How does OpenAI o1 compare to o1-mini in API token pricing and cost per token?+
OpenAI o1 costs $15.00 per 1M input tokens and $60.00 per 1M output tokens, whereas o1-mini costs $1.10 input and $4.40 output. Overall, o1-mini is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: OpenAI o1 or o1-mini?+
OpenAI o1 supports a context window of 200K tokens (~150k words), while o1-mini supports 128K tokens (~96k words). Therefore, OpenAI o1 can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: OpenAI o1 or o1-mini?+
OpenAI o1 scores 48.9% on SWE-bench Verified while o1-mini scores 44%. For autonomous software engineering and multi-file debugging, OpenAI o1 holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between OpenAI o1 and o1-mini?+
OpenAI o1 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, o1-mini provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between OpenAI o1 and o1-mini?+
OpenAI o1 generates approximately 60 tokens per second, while o1-mini delivers 90 tokens per second. In latency-sensitive production environments, o1-mini provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining OpenAI o1 and o1-mini to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to o1-mini to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to OpenAI o1. This tiered routing pattern cuts overall API bills by up to 70%.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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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.
공식 출처: GetInfoUs Independent AI Benchmarks & Official API Specs