Head-to-Head Model Comparison

GPT-6 Luna vs GPT-5.6 Sol

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Verificato dal team di ricerca di GetInfoUsUltima verifica: October 2026Politica editoriale

Cambia e Confronta Qualsiasi Modello

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

GPT-6 LunaProfile

GPT-5.6 SolProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

OpenAI

GPT-6 Luna

Modello Leggero ed Economico

GPT-6 Luna di OpenAI è un modello di IA progettato per la massima efficienza dei costi per token e l’inferenza rapida in produzione, con finestra di contesto da 272K, velocità di 245 token/s, indice di intelligenza di 67/100 e tariffa di $0

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

Modello Ammiraglia di Frontiera

GPT-5.6 Sol di OpenAI è un modello di IA progettato per il ragionamento aziendale di frontiera, la programmazione avanzata e i carichi di produzione, con finestra di contesto da 1.1M, velocità di 108 token/s, indice di intelligenza di 89/10

Intelligence Index89 / 100

Input Token:

$4.00 / 1M

Output Token:

$20.00 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

83.8%

Differenze Chiave a Colpo d’Occhio

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

Chi vince in ogni categoria?

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

Punteggi Benchmark Verificati

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

Latenza e Tempo di Risposta

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

Simulatore Interattivo dei Costi Token

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

Finestra di contesto da 272K con capacità massima di output fino a 128K token (SWE-bench Verified 70.5% · GPQA Diamond 73.5%)
Throughput elevato di 245 token/secondo con latenza al primo token (TTFT) di soli 380 ms
Economia API competitiva ($0.1/$0.5 per 1M di token) con supporto per prompt caching, tool calling e output JSON strutturati

Considerations & Trade-offs

Modello cloud proprietario chiuso; non consente il download locale dei pesi per l’esecuzione offline
Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

GPT-5.6 Sol · Strengths & Key Advantages

Finestra di contesto da 1.1M con capacità massima di output fino a 128K token (SWE-bench Verified 83.8% · GPQA Diamond 82%)
Throughput elevato di 108 token/secondo con latenza al primo token (TTFT) di soli 1210 ms
Economia API competitiva ($4/$20 per 1M di token) con supporto per prompt caching, tool calling e output JSON strutturati

Considerations & Trade-offs

Modello cloud proprietario chiuso; non consente il download locale dei pesi per l’esecuzione offline
Per volumi di traffico elevati in produzione si consiglia di attivare il prompt caching e la Batch API

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

Specifiche Tecniche

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: Risposte Rapide (FAQ)

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.

Verdetto

Sia GPT-6 Luna (Ideale per la massima efficienza dei costi per token e l’inferenza rapida in produzione, sistemi RAG aziendali e integrazioni API scalabili.) sia GPT-5.6 Sol (Ideale per il ragionamento aziendale di frontiera, la programmazione avanzata e i carichi di produzione, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: GPT-5.6 Sol guida nei benchmark di ragionamento e programmazione, GPT-6 Luna garantisce una generazione più rapida e GPT-6 Luna offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, GPT-6 Luna o GPT-5.6 Sol?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale GPT-5.6 Sol. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra GPT-6 Luna e GPT-5.6 Sol?+

GPT-6 Luna costa $0.10 / $0.50 per milione di token di input/output, mentre GPT-5.6 Sol costa $4.00 / $20.00. Nel costo combinato, GPT-6 Luna risulta più economico.

Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+

GPT-6 Luna supporta 272K token di contesto contro 1.05M token di GPT-5.6 Sol, mentre GPT-6 Luna offre una velocità di generazione superiore.

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

Cambia e Confronta Qualsiasi Modello

GPT-6 Luna

GPT-5.6 Sol

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

GPT-6 Luna · Strengths & Key Advantages

Considerations & Trade-offs

GPT-5.6 Sol · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

GPT-6 Luna vs GPT-5.6 Sol: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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