Head-to-Head Model Comparison

GPT-6 Luna vs Claude Opus 5.5

Benchmarks, API Token Pricing, Context Window & Speed Comparison

تمت المراجعة بواسطة فريق أبحاث GetInfoUsآخر مراجعة: October 2026السياسة التحريرية
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OpenAIAnthropiclightweightflagship

Compare GPT-6 Luna by OpenAI against Claude Opus 5.5 by Anthropic across real-world coding, reasoning, token economics, and latency.

مقارنة مباشرة (VS)

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OpenAI

GPT-6 Luna

نموذج خفيف وعالي الكفاءة

نموذج GPT-6 Luna من OpenAI مصمم خصيصاً لـتحقيق أعلى كفاءة اقتصادية لكل رمز مع سرعة تدفق عالية في بيئات الإنتاج، حيث يوفر نافذة سياق بسعة 272K وسرعة 245 رمز/ثانية ومؤشر ذكاء 67/100 (بتكلفة $0.1/$0.5 لكل مليون رمز).

Intelligence Index67 / 100
Input Token:

$0.10 / 1M

Output Token:

$0.50 / 1M

Context Window:

272K tokens

SWE-bench Verified:

70.5%

Anthropic

Claude Opus 5.5

نموذج رائد متقدم

نموذج Claude Opus 5.5 من Anthropic مصمم خصيصاً لـالاستدلال المؤسسي المتقدم وهندسة البرمجيات وتطبيقات الإنتاج واسعة النطاق، حيث يوفر نافذة سياق بسعة 1M وسرعة 78 رمز/ثانية ومؤشر ذكاء 94/100 (بتكلفة $4/$20 لكل مليون رمز).

Intelligence Index94 / 100
Input Token:

$4.00 / 1M

Output Token:

$20.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

84.6%

أهم الفروقات في لمحة

  • GPT-6 Luna is 40.0x cheaper for API usage (input + output tokens combined).
  • Claude Opus 5.5 has a larger context window (1M vs 272K tokens).
  • GPT-6 Luna is faster at 245 tok/s vs 78 tok/s.
  • Claude Opus 5.5 leads on SWE-bench coding (84.6% vs 70.5%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGPT-6 LunaClaude Opus 5.5
Intelligence Index
Composite reasoning & knowledge score (0-100)
6794
Provider
OpenAIAnthropic
Input Price
Cost per million input tokens
$0.10 / 1M$4.00 / 1M
Output Price
Cost per million output tokens
$0.50 / 1M$20.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.08 / 1M$3.08 / 1M
Context Window
Maximum input + output tokens
272K tokens~408 A4 pages of text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
128K128K
Throughput Speed
Tokens generated per second
245 tok/s78 tok/s
SWE-bench Verified
Real-world software engineering
70.5%84.6%
GPQA Diamond
Graduate-level reasoning
73.5%82.3%
Cached Input Price
Prompt caching discount per 1M tokens
$0.01 / 1M$0.40 / 1M

من يتفوق في كل مجال؟

At-a-glance decision framework
Coding
Claude Opus 5.5
+14.1% SWE-bench lead
Cost
GPT-6 Luna
40.0x cheaper monthly runtime
Speed
GPT-6 Luna
+214% faster generation
Context
Claude Opus 5.5
1M tokens window

نتائج الاختبارات المعيارية الموثقة

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

GPT-6 LunaClaude Opus 5.5
SWE-bench Verified (Autonomous Coding)

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

70.5%vs84.6%
GPQA Diamond (Doctorate-Level Science & Math)

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

73.5%vs82.3%
MMLU-Pro (Multi-task Language Understanding)

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

86%vs91.5%
LMSYS Chatbot Arena Elo

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

1405ptsvs1507pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

13%vs19.8%
Terminal-Bench (Agentic Coding & Terminal Use)

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

44%vs66.4%
SciCode (Scientific Computing)

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

41%vs58.7%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

68%vs83.1%
AIME (Advanced Math Competition)

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

90.2%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.

245 tok/s vs78 tok/s

زمن الاستجابة والسرعة

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-6 Luna0.38s
Claude Opus 5.51.40s
Est. End-to-End Response (500 tokens)TTFT + generation time
GPT-6 Luna2.42s
Claude Opus 5.57.81s

محاكي تكلفة الرموز التفاعلي

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-6 Luna Monthly API Bill
$0.61 / month
$0.1/1M in · $0.5/1M out
Claude Opus 5.5 Monthly API Bill
$24.60 / month
$4/1M in · $20/1M out
GPT-6 Luna saves approximately $23.99 / month (98%) under this workload.40.0x Savings

GPT-6 Luna · Strengths & Key Advantages

  • نافذة سياق بسعة 272K وحد أقصى للمخرجات يصل إلى 128K رمز (SWE-bench Verified 70.5% · GPQA Diamond 73.5%)
  • سرعة توليد عالية تبلغ 245 رمز/ثانية مع زمن استجابة لأول رمز (TTFT) قدره 380 مللي ثانية
  • تسعير API تنافسي (بتكلفة $0.1/$0.5 لكل مليون رمز) مع دعم التخزين المؤقت للأوامر والمخرجات المهيكلة بصيغة JSON
Considerations & Trade-offs
  • نموذج سحابي مغلق الأوزان لا يدعم تنزيل الأوزان للتشغيل المحلي المعزول عن الإنترنت
  • في المسائل البحثية فائقة التعقيد، قد تتفوق نماذج الاستدلال الثقيلة المتخصصة في عمق التحليل

Claude Opus 5.5 · Strengths & Key Advantages

  • نافذة سياق بسعة 1M وحد أقصى للمخرجات يصل إلى 128K رمز (SWE-bench Verified 84.6% · GPQA Diamond 82.3%)
  • سرعة توليد عالية تبلغ 78 رمز/ثانية مع زمن استجابة لأول رمز (TTFT) قدره 1400 مللي ثانية
  • تسعير API تنافسي (بتكلفة $4/$20 لكل مليون رمز) مع دعم التخزين المؤقت للأوامر والمخرجات المهيكلة بصيغة JSON
Considerations & Trade-offs
  • نموذج سحابي مغلق الأوزان لا يدعم تنزيل الأوزان للتشغيل المحلي المعزول عن الإنترنت
  • في حالات الاستخدام الكثيف، يُنصح بتفعيل التخزين المؤقت (Prompt Caching) وBatch API لترشيد النفقات

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

MetricGPT-6 LunaClaude Opus 5.5
Cost per Task
Estimated USD to finish one task
$0.13$3.85
Output Tokens / Task
Tokens generated per task
46K68K
Reasoning Tokens / Task
Thinking tokens burned per task
21K42K

المواصفات التقنية

Architecture, release timeline, capabilities, and modalities.

FeatureGPT-6 LunaClaude Opus 5.5
Official API Model ID (SDK)gpt-6-lunaclaude-opus-5-5-20260901
API ProtocolOpenAI Responses API & Chat Completions v1Anthropic Messages API + AWS Bedrock + Vertex AI
Release Date2026-092026-09
Knowledge CutoffApril 2026June 2026
ArchitectureUltra-budget compact 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 ($0.1/1M in) across full 272K windowStandard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTLAutomatic prefix caching at $0.01/1M (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-6 Luna vs Claude Opus 5.5: إجابات سريعة (FAQ)

Which is more intelligent, GPT-6 Luna or Claude Opus 5.5?

Claude Opus 5.5 is more intelligent. It scores 94 compared with 67 on the composite Intelligence Index.

Which model is faster?

GPT-6 Luna is faster, generating 245 tokens per second compared with 78 tokens per second.

Which model is cheaper to run?

GPT-6 Luna is cheaper at $0.08 per 1M tokens blended, compared with $3.08 (7:2:1 cache:input:output ratio).

Which model has lower latency?

GPT-6 Luna has lower latency, with a time to first token of 0.38s compared with 1.40s.

Which model has a larger context window?

Claude Opus 5.5 has a larger context window, supporting 1M tokens compared with 272K tokens.

الحكم النهائي

Choose GPT-6 Luna for مثالي لـتحقيق أعلى كفاءة اقتصادية لكل رمز مع سرعة تدفق عالية في بيئات الإنتاج وأنظمة rag المؤسسية وربط واجهات البرمجة (api) القابلة للتوسع., or choose Claude Opus 5.5 for مثالي لـالاستدلال المؤسسي المتقدم وهندسة البرمجيات وتطبيقات الإنتاج واسعة النطاق وأنظمة rag المؤسسية وربط واجهات البرمجة (api) القابلة للتوسع..

الأسئلة الشائعة (FAQ)

Which is cheaper: GPT-6 Luna or Claude Opus 5.5?+

GPT-6 Luna costs $0.1/1M input and $0.5/1M output tokens, while Claude Opus 5.5 costs $4/1M input and $20/1M output tokens.

Which model has a larger context window: GPT-6 Luna or Claude Opus 5.5?+

GPT-6 Luna supports 272K tokens whereas Claude Opus 5.5 supports 1M tokens.

AI Terminology & Guide
Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

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.

المصادر الرسمية: OpenAI & Anthropic Official Documentation