o4-mini vs o3-mini
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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o4-mini by OpenAI and o3-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, both models offer identical input token rates. On output generation, costs are matched. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 200K tokens. In latency-sensitive production environments, o4-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
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o4-mini
심층 추론 모델OpenAI의 o4-mini 모델은 200K 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
200K tokens
57.5%
o3-mini
심층 추론 모델OpenAI의 o3-mini 모델은 200K 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
200K tokens
49%
한눈에 보는 핵심 차이점
- o4-mini is 1.1x cheaper for API usage (input + output tokens combined).
- o4-mini is faster at 105 tok/s vs 85 tok/s.
- o4-mini leads on SWE-bench coding (57.5% vs 49.0%).
| Feature | o4-mini | o3-mini |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 50 | 51 |
Provider | OpenAI | OpenAI |
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 text | 200K tokens~300 A4 pages of text |
Max Output Tokens Per single request | 100K | 100K |
Throughput Speed Tokens generated per second | 105 tok/s | 85 tok/s |
SWE-bench Verified Real-world software engineering | 57.5% | 49% |
GPQA Diamond Graduate-level reasoning | 73% | 79.7% |
Cached Input Price Prompt caching discount per 1M tokens | $0.28 / 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.
o4-mini · Strengths & Key Advantages
- OpenAI의 첨단 Compact reasoning transformer 아키텍처와 최대 200K 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
o3-mini · Strengths & Key Advantages
- OpenAI의 첨단 Chain-of-Thought Reasoning Dense 아키텍처와 최대 200K 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 | o4-mini | o3-mini |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.72 | $1.30 |
Output Tokens / Task Tokens generated per task | 45K | 45K |
Reasoning Tokens / Task Thinking tokens burned per task | 25K | 25K |
기술 사양 (Specifications)
Architecture, release timeline, capabilities, and modalities.
| Feature | o4-mini | o3-mini |
|---|---|---|
| Official API Model ID (SDK) | o4-mini | o3-mini |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-04 | 2025-01 |
| Knowledge Cutoff | June 2024 | October 2023 |
| Architecture | Compact reasoning transformer | Chain-of-Thought Reasoning Dense |
| 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 ($1.1/1M in) across full 200K window | Flat rate ($1.1/1M in) across full 200K window |
| Prompt Cache Policy & TTL | Automatic 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 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 |
o4-mini vs o3-mini: 핵심 요약 문답 (FAQ)
Which is more intelligent, o4-mini or o3-mini?
o3-mini is more intelligent. It scores 51 compared with 50 on the composite Intelligence Index.
Which model is faster?
o4-mini is faster, generating 105 tokens per second compared with 85 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?
o3-mini has lower latency, with a time to first token of 1.40s compared with 1.80s.
종합 평가
Verdict & Model Selection Recommendation: Choose o4-mini if your priority is Budget reasoning inside agent pipelines and coding assistants. Its performance in demanding tasks justifies its operational token cost. Choose o3-mini if your workflow prioritizes Competitive coding, mathematical research, algorithmic optimization, and backend logic. 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 o4-mini compare to o3-mini in API token pricing and cost per token?+
Both o4-mini and o3-mini share matched API token pricing at $1.10 per 1M input tokens and $4.40 per 1M output tokens. Because their token economics are identical, your selection should be guided by benchmark latency, memory horizon, and reasoning capabilities rather than operational token fees. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: o4-mini or o3-mini?+
Both o4-mini and o3-mini offer an identical context window of 200K tokens (~150k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: o4-mini or o3-mini?+
o4-mini scores 57.5% on SWE-bench Verified while o3-mini scores 49%. For autonomous software engineering and multi-file debugging, o4-mini holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between o4-mini and o3-mini?+
o4-mini features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, o3-mini provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between o4-mini and o3-mini?+
o4-mini generates approximately 105 tokens per second, while o3-mini delivers 85 tokens per second. In latency-sensitive production environments, o4-mini provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining o4-mini and o3-mini to optimize costs?+
Yes! Tiered multi-model routing can optimize for latency and accuracy: you can route high-frequency conversational turns and latency-sensitive queries to o4-mini, and route deep architectural or complex reasoning queries to o4-mini. This balanced routing strategy maximizes system throughput and answer quality.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
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- o4-mini vs DeepSeek V4.1-Flash: Benchmarks, Pricing & Speed Comparison비교
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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