Head-to-Head Model Comparison

o4-mini vs Claude Haiku 5.5

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs 연구팀 검토 완료최종 검토일: October 2026편집 정책
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
o4-mini vs Claude Haiku 5.5: Benchmarks, Pricing & Speed Comparison

o4-mini by OpenAI and Claude Haiku 5.5 by Anthropic 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, Claude Haiku 5.5 holds a notable pricing advantage for token ingestion. On output generation, o4-mini is more economical per million generated tokens. 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, Claude Haiku 5.5 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.

1:1 모델 맞대결 비교 (VS)

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OpenAI

o4-mini

심층 추론 모델

OpenAI의 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.

Intelligence Index50 / 100
Input Token:

$1.10 / 1M

Output Token:

$4.40 / 1M

Context Window:

200K tokens

SWE-bench Verified:

57.5%

Anthropic

Claude Haiku 5.5

심층 추론 모델

Anthropic의 Claude Haiku 5.5 모델은 200K 토큰 컨텍스트 윈도우와 입력 $1/1M 토큰, 출력 $5/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.

Intelligence Index62 / 100
Input Token:

$1.00 / 1M

Output Token:

$5.00 / 1M

Context Window:

200K tokens

SWE-bench Verified:

68.4%

한눈에 보는 핵심 차이점

  • o4-mini is 1.0x cheaper for API usage (input + output tokens combined).
  • Claude Haiku 5.5 is faster at 160 tok/s vs 105 tok/s.
  • Claude Haiku 5.5 leads on SWE-bench coding (68.4% vs 57.5%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
Featureo4-miniClaude Haiku 5.5
Intelligence Index
Composite reasoning & knowledge score (0-100)
5062
Provider
OpenAIAnthropic
Input Price
Cost per million input tokens
$1.10 / 1M$1.00 / 1M
Output Price
Cost per million output tokens
$4.40 / 1M$5.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.85 / 1M$0.77 / 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
100K64K
Throughput Speed
Tokens generated per second
105 tok/s160 tok/s
SWE-bench Verified
Real-world software engineering
57.5%68.4%
GPQA Diamond
Graduate-level reasoning
73%69.8%
Cached Input Price
Prompt caching discount per 1M tokens
$0.28 / 1M$0.10 / 1M

부문별 우위 모델

At-a-glance decision framework
Coding
Claude Haiku 5.5
+10.9% SWE-bench lead
Cost
o4-mini
1.0x cheaper monthly runtime
Speed
Claude Haiku 5.5
+52% faster generation
Context
Tied
200K tokens window

검증된 벤치마크 점수 비교

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

o4-miniClaude Haiku 5.5
SWE-bench Verified (Autonomous Coding)

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

57.5%vs68.4%
GPQA Diamond (Doctorate-Level Science & Math)

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

73%vs69.8%
MMLU-Pro (Multi-task Language Understanding)

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

83%vs83.2%
LMSYS Chatbot Arena Elo

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

1325ptsvs1370pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

11.2%vsN/A
Terminal-Bench (Agentic Coding & Terminal Use)

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

36%vsN/A
SciCode (Scientific Computing)

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

38.5%vsN/A
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

65%vsN/A
AIME (Advanced Math Competition)

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

89.5%vsN/A
Long-Context Reasoning

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

74.5%vsN/A
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

105 tok/s vs160 tok/s

지연 시간(Latency) 및 응답 속도

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
Claude Haiku 5.50.22s
Est. End-to-End Response (500 tokens)TTFT + generation time
o4-mini6.56s
Claude Haiku 5.53.35s

실시간 API 토큰 비용 시뮬레이터

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
Claude Haiku 5.5 Monthly API Bill
$6.15 / month
$1/1M in · $5/1M out
o4-mini saves approximately $0.13 / month (2%) under this workload.1.0x Savings

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

Claude Haiku 5.5 · Strengths & Key Advantages

  • Anthropic의 첨단 Ultra-Fast Lightweight Reasoning Transformer 아키텍처와 최대 200K 토큰 컨텍스트 지원
  • 경쟁력 있는 API 가격(입력 $1/1M, 출력 $5/1M)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.

Metrico4-miniClaude Haiku 5.5
Cost per Task
Estimated USD to finish one task
$0.72$0.70
Output Tokens / Task
Tokens generated per task
45K20K
Reasoning Tokens / Task
Thinking tokens burned per task
25K10K

기술 사양 (Specifications)

Architecture, release timeline, capabilities, and modalities.

Featureo4-miniClaude Haiku 5.5
Official API Model ID (SDK)o4-miniclaude-haiku-5-5-20261001
API ProtocolOpenAI Responses API & Chat Completions v1Anthropic Messages API + AWS Bedrock + Vertex AI
Release Date2025-042026-10
Knowledge CutoffJune 2024August 2026
ArchitectureCompact reasoning transformerUltra-Fast Lightweight Reasoning Transformer
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)Flat rate ($1.1/1M in) across full 200K windowStandard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTLAutomatic prefix caching at $0.275/1M (no extra cache-write fee)$0.1/1M cached read (90% off); 5-min default & 1-hour extended TTL
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 Citations + Code Execution Tool + MCP Connectors
Official Fine-Tuning SupportSupervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)Custom Model Tuning via Amazon Bedrock & Prompt Distillation
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 PublishedAnthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated
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 vs Claude Haiku 5.5: 핵심 요약 문답 (FAQ)

Which is more intelligent, o4-mini or Claude Haiku 5.5?

Claude Haiku 5.5 is more intelligent. It scores 62 compared with 50 on the composite Intelligence Index.

Which model is faster?

Claude Haiku 5.5 is faster, generating 160 tokens per second compared with 105 tokens per second.

Which model is cheaper to run?

Claude Haiku 5.5 is cheaper at $0.77 per 1M tokens blended, compared with $0.85 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Claude Haiku 5.5 has lower latency, with a time to first token of 0.22s 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 Claude Haiku 5.5 if your workflow prioritizes Real-time user-facing chatbots, rapid text classification, code completion, and edge agent routing. 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 Claude Haiku 5.5 in API token pricing and cost per token?+

o4-mini costs $1.10 per 1M input tokens and $4.40 per 1M output tokens, whereas Claude Haiku 5.5 costs $1.00 input and $5.00 output. Overall, Claude Haiku 5.5 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: o4-mini or Claude Haiku 5.5?+

Both o4-mini and Claude Haiku 5.5 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 Claude Haiku 5.5?+

o4-mini scores 57.5% on SWE-bench Verified while Claude Haiku 5.5 scores 68.4%. For autonomous software engineering and multi-file debugging, Claude Haiku 5.5 holds the verified benchmark lead.

What is the difference in reasoning capability and extended thinking between o4-mini and Claude Haiku 5.5?+

o4-mini features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Claude Haiku 5.5 provides chain-of-thought reasoning depth for complex math and science.

How does inference speed (tokens per second) compare between o4-mini and Claude Haiku 5.5?+

o4-mini generates approximately 105 tokens per second, while Claude Haiku 5.5 delivers 160 tokens per second. In latency-sensitive production environments, Claude Haiku 5.5 provides faster time-to-first-token (TTFT) and higher streaming response velocity.

Can you implement a tiered LLM routing setup combining o4-mini and Claude Haiku 5.5 to optimize costs?+

Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Claude Haiku 5.5 to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to o4-mini. This tiered routing pattern cuts overall API bills by up to 70%.

AI Terminology & Guide
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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.

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