Head-to-Head Model Comparison

GPT-5.6 Terra vs GPT-5.6 Luna

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Revisado pela equipe de pesquisa do GetInfoUsÚltima revisão: October 2026Política editorial

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

GPT-5.6 TerraProfile

GPT-5.6 LunaProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

OpenAI

GPT-5.6 Terra

Modelo Principal de Fronteira

O GPT-5.6 Terra da OpenAI é um modelo de IA projetado para raciocínio corporativo de fronteira, programação avançada e cargas de produção, oferecendo janela de contexto de 1.1M, velocidade de 124 tokens/s, índice de inteligência de 85/100 e

Intelligence Index85 / 100

Input Token:

$2.00 / 1M

Output Token:

$10.00 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

81.4%

OpenAI

GPT-5.6 Luna

Modelo Leve e Econômico

O GPT-5.6 Luna da OpenAI é um modelo de IA projetado para máxima economia por token e alto desempenho em escala de produção, oferecendo janela de contexto de 1.1M, velocidade de 230 tokens/s, índice de inteligência de 57/100 e tarifa de $0.

Intelligence Index57 / 100

Input Token:

$0.20 / 1M

Output Token:

$1.20 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

63.5%

Principais Diferenças em Resumo

GPT-5.6 Luna is 8.7x cheaper for API usage (input + output tokens combined).
GPT-5.6 Luna is faster at 230 tok/s vs 124 tok/s.
GPT-5.6 Terra leads on SWE-bench coding (81.4% vs 63.5%).

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

Feature	GPT-5.6 Terra	GPT-5.6 Luna
Intelligence Index Composite reasoning & knowledge score (0-100)	85	57
Provider	OpenAI	OpenAI
Input Price Cost per million input tokens	$2.00 / 1M	$0.20 / 1M
Output Price Cost per million output tokens	$10.00 / 1M	$1.20 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$1.54 / 1M	$0.17 / 1M
Context Window Maximum input + output tokens	1.05M tokens~1,575 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	124 tok/s	230 tok/s
SWE-bench Verified Real-world software engineering	81.4%	63.5%
GPQA Diamond Graduate-level reasoning	80.4%	70.5%
Cached Input Price Prompt caching discount per 1M tokens	$0.20 / 1M	$0.02 / 1M

Quem vence em cada categoria?

At-a-glance decision framework

Coding

GPT-5.6 Terra

+17.9% SWE-bench lead

Cost

GPT-5.6 Luna

8.7x cheaper monthly runtime

Speed

GPT-5.6 Luna

+85% faster generation

Context

Tied

1.05M tokens window

Pontuações de Benchmarks Verificadas

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

GPT-5.6 TerraGPT-5.6 Luna

SWE-bench Verified (Autonomous Coding)

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

81.4%vs63.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

80.4%vs70.5%

MMLU-Pro (Multi-task Language Understanding)

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

89.8%vs83.5%

LMSYS Chatbot Arena Elo

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

1470ptsvs1368pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

16.4%vs11.4%

Terminal-Bench (Agentic Coding & Terminal Use)

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

57%vs38.5%

SciCode (Scientific Computing)

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

52.5%vs36%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

78.8%vs62.5%

AIME (Advanced Math Competition)

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

94.6%vs87%

Long-Context Reasoning

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

87.2%vs76.5%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

124 tok/s vs230 tok/s

Latência e Tempo de Resposta

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-5.6 Terra1.09s

GPT-5.6 Luna0.42s

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

GPT-5.6 Terra5.12s

GPT-5.6 Luna2.59s

Simulador Interativo de Custo de Tokens

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-5.6 Terra Monthly API Bill

$12.30 / month

$2/1M in · $10/1M out

GPT-5.6 Luna Monthly API Bill

$1.41 / month

$0.2/1M in · $1.2/1M out

GPT-5.6 Luna saves approximately $10.89 / month (89%) under this workload.8.7x Savings

GPT-5.6 Terra · Strengths & Key Advantages

Janela de contexto de 1.1M com limite máximo de saída de 128K tokens (SWE-bench Verified 81.4% · GPQA Diamond 80.4%)
Taxa de transferência rápida de 124 tokens/segundo com latência inicial (TTFT) de 1090 ms
Economia de API competitiva ($2/$10 por 1M de tokens) com suporte a cache de prompts e saídas JSON estruturadas

Considerations & Trade-offs

Modelo proprietário em nuvem; não permite download dos pesos para execução local offline
Para alto volume de requisições, recomenda-se ativar o cache de prompts e a Batch API para reduzir custos

GPT-5.6 Luna · Strengths & Key Advantages

Janela de contexto de 1.1M com limite máximo de saída de 128K tokens (SWE-bench Verified 63.5% · GPQA Diamond 70.5%)
Taxa de transferência rápida de 230 tokens/segundo com latência inicial (TTFT) de 420 ms
Economia de API competitiva ($0.2/$1.2 por 1M de tokens) com suporte a cache de prompts e saídas JSON estruturadas

Considerations & Trade-offs

Modelo proprietário em nuvem; não permite download dos pesos para execução local offline
Em derivações científicas extremas, modelos pesados dedicados exclusivamente ao raciocínio oferecem maior profundidade

Token Use & Task Economics

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

Metric	GPT-5.6 Terra	GPT-5.6 Luna
Cost per Task Estimated USD to finish one task	$1.95	$0.19
Output Tokens / Task Tokens generated per task	58K	46K
Reasoning Tokens / Task Thinking tokens burned per task	32K	20K

Especificações Técnicas

Architecture, release timeline, capabilities, and modalities.

Feature	GPT-5.6 Terra	GPT-5.6 Luna
Official API Model ID (SDK)	gpt-5-6-terra	gpt-5-6-luna
API Protocol	OpenAI Responses API & Chat Completions v1	OpenAI Responses API & Chat Completions v1
Release Date	2026-07	2026-07
Knowledge Cutoff	February 2026	February 2026
Architecture	Balanced unified reasoning model	Efficient compact reasoning model
Parameters	Confidential	Confidential
Enterprise Rate Limits (RPM / TPM)	10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM	10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)	Flat rate ($2/1M in) across full 1050K window	Flat rate ($0.2/1M in) across full 1050K window
Prompt Cache Policy & TTL	Automatic prefix caching at $0.2/1M (no extra cache-write fee)	Automatic prefix caching at $0.02/1M (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-5.6 Terra vs GPT-5.6 Luna: Respostas Rápidas (FAQ)

Which is more intelligent, GPT-5.6 Terra or GPT-5.6 Luna?

GPT-5.6 Terra is more intelligent. It scores 85 compared with 57 on the composite Intelligence Index.

Which model is faster?

GPT-5.6 Luna is faster, generating 230 tokens per second compared with 124 tokens per second.

Which model is cheaper to run?

GPT-5.6 Luna is cheaper at $0.17 per 1M tokens blended, compared with $1.54 (7:2:1 cache:input:output ratio).

Which model has lower latency?

GPT-5.6 Luna has lower latency, with a time to first token of 0.42s compared with 1.09s.

Veredito

Tanto GPT-5.6 Terra (Indicado para raciocínio corporativo de fronteira, programação avançada e cargas de produção, arquiteturas RAG corporativas e integração escalável via API.) quanto GPT-5.6 Luna (Indicado para máxima economia por token e alto desempenho em escala de produção, arquiteturas RAG corporativas e integração escalável via API.) entregam excelente desempenho: GPT-5.6 Terra lidera em inteligência geral e programação, GPT-5.6 Luna oferece geração mais rápida e GPT-5.6 Luna apresenta o melhor custo-benefício em produção.

Perguntas Frequentes (FAQ)

Qual é melhor para programação e raciocínio, GPT-5.6 Terra ou GPT-5.6 Luna?+

Nas avaliações de inteligência e código, GPT-5.6 Terra leva vantagem. Confira os gráficos SWE-bench e GPQA acima para detalhes.

Qual modelo tem a API mais barata, GPT-5.6 Terra ou GPT-5.6 Luna?+

O GPT-5.6 Terra custa $2.00 / $10.00 por 1M de tokens (entrada/saída) e o GPT-5.6 Luna custa $0.20 / $1.20. No custo combinado, GPT-5.6 Luna é mais econômico.

Qual modelo possui maior janela de contexto e velocidade?+

O GPT-5.6 Terra suporta 1.05M tokens de contexto contra 1.05M tokens do GPT-5.6 Luna, enquanto GPT-5.6 Luna entrega maior velocidade de geração.

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-5.6 Terra vs GPT-5.6 Luna

Troque e Compare Qualquer Modelo

GPT-5.6 Terra

GPT-5.6 Luna

Quem vence em cada categoria?

Pontuações de Benchmarks Verificadas

Latência e Tempo de Resposta

Simulador Interativo de Custo de Tokens

GPT-5.6 Terra · Strengths & Key Advantages

Considerations & Trade-offs

GPT-5.6 Luna · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificações Técnicas

GPT-5.6 Terra vs GPT-5.6 Luna: Respostas Rápidas (FAQ)

Veredito

Perguntas Frequentes (FAQ)

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