Head-to-Head Model Comparison

GPT-5.1 Codex Max vs Claude Opus 5.5

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
GPT-5.1 Codex Max vs Claude Opus 5.5: Benchmarks, Pricing & Speed Comparison

GPT-5.1 Codex Max by OpenAI and Claude Opus 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, GPT-5.1 Codex Max holds a notable pricing advantage for token ingestion. On output generation, GPT-5.1 Codex Max is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Claude Opus 5.5 provides an expanded memory horizon with 1,000K tokens. In latency-sensitive production environments, GPT-5.1 Codex Max 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

GPT-5.1 Codex Max

심층 추론 모델

OpenAI의 GPT-5.1 Codex Max 모델은 400K 토큰 컨텍스트 윈도우와 입력 $1.75/1M 토큰, 출력 $14/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.

Intelligence Index62 / 100
Input Token:

$1.75 / 1M

Output Token:

$14.00 / 1M

Context Window:

400K tokens

SWE-bench Verified:

78.2%

Anthropic

Claude Opus 5.5

심층 추론 모델

Anthropic의 Claude Opus 5.5 모델은 1M 토큰 컨텍스트 윈도우와 입력 $4.0/1M 토큰, 출력 $20.0/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.

Intelligence Index94 / 100
Input Token:

$4.00 / 1M

Output Token:

$20.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

84.6%

한눈에 보는 핵심 차이점

  • GPT-5.1 Codex Max is 1.6x cheaper for API usage (input + output tokens combined).
  • Claude Opus 5.5 has a larger context window (1M vs 400K tokens).
  • GPT-5.1 Codex Max is faster at 95 tok/s vs 78 tok/s.
  • Claude Opus 5.5 leads on SWE-bench coding (84.6% vs 78.2%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGPT-5.1 Codex MaxClaude Opus 5.5
Intelligence Index
Composite reasoning & knowledge score (0-100)
6294
Provider
OpenAIAnthropic
Input Price
Cost per million input tokens
$1.75 / 1M$4.00 / 1M
Output Price
Cost per million output tokens
$14.00 / 1M$20.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$1.87 / 1M$3.08 / 1M
Context Window
Maximum input + output tokens
400K tokens~600 A4 pages of text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
128K128K
Throughput Speed
Tokens generated per second
95 tok/s78 tok/s
SWE-bench Verified
Real-world software engineering
78.2%84.6%
GPQA Diamond
Graduate-level reasoning
80%82.3%
Cached Input Price
Prompt caching discount per 1M tokens
$0.17 / 1M$0.40 / 1M

부문별 우위 모델

At-a-glance decision framework
Coding
Claude Opus 5.5
+6.4% SWE-bench lead
Cost
GPT-5.1 Codex Max
1.6x cheaper monthly runtime
Speed
GPT-5.1 Codex Max
+22% faster generation
Context
Claude Opus 5.5
1M tokens window

검증된 벤치마크 점수 비교

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

GPT-5.1 Codex MaxClaude Opus 5.5
SWE-bench Verified (Autonomous Coding)

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

78.2%vs84.6%
GPQA Diamond (Doctorate-Level Science & Math)

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

80%vs82.3%
MMLU-Pro (Multi-task Language Understanding)

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

87.2%vs91.5%
LMSYS Chatbot Arena Elo

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

1378ptsvs1507pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

13.2%vs19.8%
Terminal-Bench (Agentic Coding & Terminal Use)

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

54.5%vs66.4%
SciCode (Scientific Computing)

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

48.5%vs58.7%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

75.5%vs83.1%
AIME (Advanced Math Competition)

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

92.6%vs95.4%
Long-Context Reasoning

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

80%vs92.6%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

95 tok/s vs78 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
GPT-5.1 Codex Max1.70s
Claude Opus 5.51.40s
Est. End-to-End Response (500 tokens)TTFT + generation time
GPT-5.1 Codex Max6.96s
Claude Opus 5.57.81s

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)
GPT-5.1 Codex Max Monthly API Bill
$15.49 / month
$1.75/1M in · $14/1M out
Claude Opus 5.5 Monthly API Bill
$24.60 / month
$4/1M in · $20/1M out
GPT-5.1 Codex Max saves approximately $9.11 / month (37%) under this workload.1.6x Savings

GPT-5.1 Codex Max · Strengths & Key Advantages

  • OpenAI의 첨단 Maximum-capability code-specialised reasoning model 아키텍처와 최대 400K 토큰 컨텍스트 지원
  • 경쟁력 있는 API 가격(입력 $1.75/1M, 출력 $14/1M)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 Opus 5.5 · Strengths & Key Advantages

  • Anthropic의 첨단 Extended Autonomous Hybrid Reasoning Transformer 아키텍처와 최대 1M 토큰 컨텍스트 지원
  • 경쟁력 있는 API 가격(입력 $4.0/1M, 출력 $20.0/1M)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.

MetricGPT-5.1 Codex MaxClaude Opus 5.5
Cost per Task
Estimated USD to finish one task
$2.40$3.85
Output Tokens / Task
Tokens generated per task
62K68K
Reasoning Tokens / Task
Thinking tokens burned per task
36K42K

기술 사양 (Specifications)

Architecture, release timeline, capabilities, and modalities.

FeatureGPT-5.1 Codex MaxClaude Opus 5.5
Official API Model ID (SDK)gpt-5-1-codex-maxclaude-opus-5-5-20260901
API ProtocolOpenAI Responses API & Chat Completions v1Anthropic Messages API + AWS Bedrock + Vertex AI
Release Date2025-112026-09
Knowledge CutoffOctober 2024June 2026
ArchitectureMaximum-capability code-specialised reasoning modelExtended Autonomous Hybrid 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) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)Flat rate ($1.75/1M in) across full 400K windowStandard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTLAutomatic prefix caching at $0.175/1M (no extra cache-write fee)$0.4/1M 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

GPT-5.1 Codex Max vs Claude Opus 5.5: 핵심 요약 문답 (FAQ)

Which is more intelligent, GPT-5.1 Codex Max or Claude Opus 5.5?

Claude Opus 5.5 is more intelligent. It scores 94 compared with 62 on the composite Intelligence Index.

Which model is faster?

GPT-5.1 Codex Max is faster, generating 95 tokens per second compared with 78 tokens per second.

Which model is cheaper to run?

GPT-5.1 Codex Max is cheaper at $1.87 per 1M tokens blended, compared with $3.08 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Claude Opus 5.5 has lower latency, with a time to first token of 1.40s compared with 1.70s.

Which model has a larger context window?

Claude Opus 5.5 has a larger context window, supporting 1M tokens compared with 400K tokens.

종합 평가

Verdict & Model Selection Recommendation: Choose GPT-5.1 Codex Max if your priority is Enterprise-scale code agents and monorepo work. Its performance in demanding tasks justifies its operational token cost. Choose Claude Opus 5.5 if your workflow prioritizes Large-scale software development agents, enterprise codebases, and mission-critical scientific synthesis. 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 GPT-5.1 Codex Max compare to Claude Opus 5.5 in API token pricing and cost per token?+

GPT-5.1 Codex Max costs $1.75 per 1M input tokens and $14.00 per 1M output tokens, whereas Claude Opus 5.5 costs $4.00 input and $20.00 output. Overall, GPT-5.1 Codex Max 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: GPT-5.1 Codex Max or Claude Opus 5.5?+

GPT-5.1 Codex Max supports a context window of 400K tokens (~300k words), while Claude Opus 5.5 supports 1,000K tokens (~750k words). Therefore, Claude Opus 5.5 can process more textual data and document context in a single prompt.

Which AI model is better for software engineering and coding: GPT-5.1 Codex Max or Claude Opus 5.5?+

GPT-5.1 Codex Max scores 78.2% on SWE-bench Verified while Claude Opus 5.5 scores 84.6%. For autonomous software engineering and multi-file debugging, Claude Opus 5.5 holds the verified benchmark lead.

What is the difference in reasoning capability and extended thinking between GPT-5.1 Codex Max and Claude Opus 5.5?+

GPT-5.1 Codex Max features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Claude Opus 5.5 provides chain-of-thought reasoning depth for complex math and science.

How does inference speed (tokens per second) compare between GPT-5.1 Codex Max and Claude Opus 5.5?+

GPT-5.1 Codex Max generates approximately 95 tokens per second, while Claude Opus 5.5 delivers 78 tokens per second. In latency-sensitive production environments, GPT-5.1 Codex Max provides faster time-to-first-token (TTFT) and higher streaming response velocity.

Can you implement a tiered LLM routing setup combining GPT-5.1 Codex Max and Claude Opus 5.5 to optimize costs?+

Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to GPT-5.1 Codex Max to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Claude Opus 5.5. This tiered routing pattern cuts overall API bills by up to 70%.

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