Head-to-Head Model Comparison

GPT-6 Luna vs GPT-5.6 Sol

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs araştırma ekibi tarafından incelendiSon inceleme: October 2026Yayın ilkeleri

Paylaş

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GPT-6 LunaProfile

GPT-5.6 SolProfile

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Popular Matchups:

OpenAI

GPT-6 Luna

Hafif ve Ekonomik Model

OpenAI tarafından geliştirilen GPT-6 Luna; token başına maksimum maliyet verimliliği ve hızlı üretim çıkarımı için tasarlanmış olup 272K bağlam penceresi, 245 token/sn hız, 67/100 zeka indeksi ve 1M token başına $0.1/$0.5 fiyatlandırma suna

Intelligence Index67 / 100

Input Token:

$0.10 / 1M

Output Token:

$0.50 / 1M

Context Window:

272K tokens

SWE-bench Verified:

70.5%

OpenAI

GPT-5.6 Sol

Amiral Gemisi Öncü Model

OpenAI tarafından geliştirilen GPT-5.6 Sol; öncü kurumsal akıl yürütme, yazılım geliştirme ve üretim ölçekli AI iş akışları için tasarlanmış olup 1.1M bağlam penceresi, 108 token/sn hız, 89/100 zeka indeksi ve 1M token başına $4/$20 fiyatla

Intelligence Index89 / 100

Input Token:

$4.00 / 1M

Output Token:

$20.00 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

83.8%

Bir Bakışta Temel Farklar

GPT-6 Luna is 40.0x cheaper for API usage (input + output tokens combined).
GPT-5.6 Sol has a larger context window (1M vs 272K tokens).
GPT-6 Luna is faster at 245 tok/s vs 108 tok/s.
GPT-5.6 Sol leads on SWE-bench coding (83.8% vs 70.5%).

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

Feature	GPT-6 Luna	GPT-5.6 Sol
Intelligence Index Composite reasoning & knowledge score (0-100)	67	89
Provider	OpenAI	OpenAI
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 text	1.05M tokens~1,575 A4 pages of text
Max Output Tokens Per single request	128K	128K
Throughput Speed Tokens generated per second	245 tok/s	108 tok/s
SWE-bench Verified Real-world software engineering	70.5%	83.8%
GPQA Diamond Graduate-level reasoning	73.5%	82%
Cached Input Price Prompt caching discount per 1M tokens	$0.01 / 1M	$0.40 / 1M

Hangi Alanda Kim Kazanıyor?

At-a-glance decision framework

Coding

GPT-5.6 Sol

+13.3% SWE-bench lead

Cost

GPT-6 Luna

40.0x cheaper monthly runtime

Speed

GPT-6 Luna

+127% faster generation

Context

GPT-5.6 Sol

1.05M tokens window

Doğrulanmış Benchmark Puanları

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

GPT-6 LunaGPT-5.6 Sol

SWE-bench Verified (Autonomous Coding)

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

70.5%vs83.8%

GPQA Diamond (Doctorate-Level Science & Math)

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

73.5%vs82%

MMLU-Pro (Multi-task Language Understanding)

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

86%vs90.8%

LMSYS Chatbot Arena Elo

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

1405ptsvs1496pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

13%vs17.8%

Terminal-Bench (Agentic Coding & Terminal Use)

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

44%vs61%

SciCode (Scientific Computing)

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

41%vs55%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

68%vs80.6%

AIME (Advanced Math Competition)

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

90.2%vs95.8%

Long-Context Reasoning

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

80%vs89.6%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

245 tok/s vs108 tok/s

Gecikme Süresi ve Yanıt Hızı

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

GPT-5.6 Sol1.21s

Est. End-to-End Response (500 tokens)TTFT + generation time

GPT-6 Luna2.42s

GPT-5.6 Sol5.84s

Gerçek Zamanlı Token Maliyet Simülatörü

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

GPT-6 Luna Monthly API Bill

$0.61 / month

$0.1/1M in · $0.5/1M out

GPT-5.6 Sol 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

272K bağlam penceresi ve yanıt başına maksimum 128K çıktı tokenı kapasitesi (SWE-bench Verified 70.5% · GPQA Diamond 73.5%)
Sadece 380 ms ilk token gecikmesi (TTFT) ile saniyede 245 token yüksek üretim hızı
İstem önbellekleme (Prompt Caching), araç çağırma ve yapılandırılmış JSON çıktısını destekleyen rekabetçi API ekonomisi (1M token başına $0.1/$0.5 fiyatlandırma)

Considerations & Trade-offs

Kapalı ağırlıklı bulut modelidir; çevrimdışı yerel sunuculara indirilerek çalıştırılamaz
Çok karmaşık akademik ve bilimsel çıkarımlarda üst düzey ağır akıl yürütme modelleri daha derin sonuçlar verebilir

GPT-5.6 Sol · Strengths & Key Advantages

1.1M bağlam penceresi ve yanıt başına maksimum 128K çıktı tokenı kapasitesi (SWE-bench Verified 83.8% · GPQA Diamond 82%)
Sadece 1210 ms ilk token gecikmesi (TTFT) ile saniyede 108 token yüksek üretim hızı
İstem önbellekleme (Prompt Caching), araç çağırma ve yapılandırılmış JSON çıktısını destekleyen rekabetçi API ekonomisi (1M token başına $4/$20 fiyatlandırma)

Considerations & Trade-offs

Kapalı ağırlıklı bulut modelidir; çevrimdışı yerel sunuculara indirilerek çalıştırılamaz
Yüksek hacimli üretim trafiğinde maliyetleri optimize etmek için Prompt Caching ve Batch API kullanımı önerilir

Token Use & Task Economics

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

Metric	GPT-6 Luna	GPT-5.6 Sol
Cost per Task Estimated USD to finish one task	$0.13	$3.30
Output Tokens / Task Tokens generated per task	46K	64K
Reasoning Tokens / Task Thinking tokens burned per task	21K	38K

Teknik Özellikler

Architecture, release timeline, capabilities, and modalities.

Feature	GPT-6 Luna	GPT-5.6 Sol
Official API Model ID (SDK)	gpt-6-luna	gpt-5-6-sol
API Protocol	OpenAI Responses API & Chat Completions v1	OpenAI Responses API & Chat Completions v1
Release Date	2026-09	2026-07
Knowledge Cutoff	April 2026	February 2026
Architecture	Ultra-budget compact reasoning model	Frontier unified reasoning model
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) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)	Flat rate ($0.1/1M in) across full 272K window	Flat rate ($4/1M in) across full 1050K window
Prompt Cache Policy & TTL	Automatic prefix caching at $0.01/1M (no extra cache-write fee)	Automatic prefix caching at $0.4/1M (no extra cache-write fee)
Computer Use / GUI Agent	Supported (Screen coordinate grounding, browser & desktop GUI action loops)	Supported (Screen coordinate grounding, browser & desktop GUI action loops)
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) · Vision (~258–765 tokens per image tile)
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, vision
Output Modalities	text	text

GPT-6 Luna vs GPT-5.6 Sol: Hızlı Yanıtlar (SSS)

Which is more intelligent, GPT-6 Luna or GPT-5.6 Sol?

GPT-5.6 Sol is more intelligent. It scores 89 compared with 67 on the composite Intelligence Index.

Which model is faster?

GPT-6 Luna is faster, generating 245 tokens per second compared with 108 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 1.21s.

Which model has a larger context window?

GPT-5.6 Sol has a larger context window, supporting 1.05M tokens compared with 272K tokens.

Sonuç

Hem GPT-6 Luna (token başına maksimum maliyet verimliliği ve hızlı üretim çıkarımı, kurumsal RAG mimarileri ve ölçeklenebilir üretim API entegrasyonları için idealdir.) hem de GPT-5.6 Sol (öncü kurumsal akıl yürütme, yazılım geliştirme ve üretim ölçekli AI iş akışları, kurumsal RAG mimarileri ve ölçeklenebilir üretim API entegrasyonları için idealdir.) güçlü üretim seçenekleridir: GPT-5.6 Sol karmaşık akıl yürütme ve kodlama testlerinde öne çıkarken, GPT-6 Luna daha yüksek üretim hızı sağlar; GPT-6 Luna ise yüksek hacimli API kullanımlarında daha ekonomiktir.

Sıkça Sorulan Sorular (SSS)

Kodlama ve akıl yürütme için GPT-6 Luna mi yoksa GPT-5.6 Sol mi daha iyi?+

Bileşik zeka endeksi ve yazılım testlerinde GPT-5.6 Sol önde yer alıyor. Detaylı karşılaştırma için yukarıdaki SWE-bench ve GPQA tablolarına göz atabilirsiniz.

GPT-6 Luna ve GPT-5.6 Sol API fiyatları nasıl karşılaştırılır?+

GPT-6 Luna 1M giriş/çıkış tokeni başına $0.10 / $0.50 ücretlendirilirken, GPT-5.6 Sol $4.00 / $20.00 seviyesindedir. Karma maliyette GPT-6 Luna daha avantajlıdır.

Hangi modelin bağlam penceresi daha geniş ve hızı daha yüksek?+

GPT-6 Luna 272K token bağlam penceresi sunarken GPT-5.6 Sol 1.05M token kapasiteye sahiptir. Saniyedeki token üretim hızında ise GPT-6 Luna öndedir.

AI Terminology & Guide

Explore Glossary →

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

Tokens & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

GPT-6 Luna vs GPT-5.6 Sol

İstediğiniz Modelleri Seçin ve Karşılaştırın

GPT-6 Luna

GPT-5.6 Sol

Hangi Alanda Kim Kazanıyor?

Doğrulanmış Benchmark Puanları

Gecikme Süresi ve Yanıt Hızı

Gerçek Zamanlı Token Maliyet Simülatörü

GPT-6 Luna · Strengths & Key Advantages

Considerations & Trade-offs

GPT-5.6 Sol · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Teknik Özellikler

GPT-6 Luna vs GPT-5.6 Sol: Hızlı Yanıtlar (SSS)

Sonuç

Sıkça Sorulan Sorular (SSS)

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