GPT-5.6 Luna vs GPT-5.5 Instant
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
GPT-5.6 Luna di OpenAI e GPT-5.5 Instant di OpenAI sono due modelli di IA di riferimento. GPT-5.6 Luna offre una finestra di contesto di 1.05M token a $0.20/$1.20 per milione di token, mentre GPT-5.5 Instant mette a disposizione 400K token di contesto a $0.75/$6.00 per milione di token. Di seguito analizziamo benchmark, latenza e costi.
Cambia e Confronta Qualsiasi Modello
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GPT-5.6 Luna
Modello Leggero ed EconomicoGPT-5.6 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 1.1M, velocità di 230 token/s, indice di intelligenza di 57/100 e tariffa di
$0.20 / 1M
$1.20 / 1M
1.05M tokens
63.5%
GPT-5.5 Instant
Modello Leggero ed EconomicoGPT-5.5 Instant 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 400K, velocità di 168 token/s, indice di intelligenza di 65/100 e tariffa
$0.75 / 1M
$6.00 / 1M
400K tokens
74.5%
Differenze Chiave a Colpo d’Occhio
- GPT-5.6 Luna is 4.7x cheaper for API usage (input + output tokens combined).
- GPT-5.6 Luna has a larger context window (1M vs 400K tokens).
- GPT-5.6 Luna is faster at 230 tok/s vs 168 tok/s.
- GPT-5.5 Instant leads on SWE-bench coding (74.5% vs 63.5%).
| Feature | GPT-5.6 Luna | GPT-5.5 Instant |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 57 | 65 |
Provider | OpenAI | OpenAI |
Input Price Cost per million input tokens | $0.20 / 1M | $0.75 / 1M |
Output Price Cost per million output tokens | $1.20 / 1M | $6.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.17 / 1M | $0.80 / 1M |
Context Window Maximum input + output tokens | 1.05M tokens~1,575 A4 pages of text | 400K tokens~600 A4 pages of text |
Max Output Tokens Per single request | 128K | 128K |
Throughput Speed Tokens generated per second | 230 tok/s | 168 tok/s |
SWE-bench Verified Real-world software engineering | 63.5% | 74.5% |
GPQA Diamond Graduate-level reasoning | 70.5% | 75% |
Cached Input Price Prompt caching discount per 1M tokens | $0.02 / 1M | $0.07 / 1M |
Chi vince in ogni categoria?
At-a-glance decision frameworkPunteggi Benchmark Verificati
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
Latenza e Tempo di Risposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulatore Interattivo dei Costi Token
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-5.6 Luna · Strengths & Key Advantages
- Finestra di contesto da 1.1M con capacità massima di output fino a 128K token (SWE-bench Verified 63.5% · GPQA Diamond 70.5%)
- Throughput elevato di 230 token/secondo con latenza al primo token (TTFT) di soli 420 ms
- Economia API competitiva ($0.2/$1.2 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-5.5 Instant · Strengths & Key Advantages
- Finestra di contesto da 400K con capacità massima di output fino a 128K token (SWE-bench Verified 74.5% · GPQA Diamond 75%)
- Throughput elevato di 168 token/secondo con latenza al primo token (TTFT) di soli 780 ms
- Economia API competitiva ($0.75/$6 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
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-5.6 Luna | GPT-5.5 Instant |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.19 | $0.95 |
Output Tokens / Task Tokens generated per task | 46K | 52K |
Reasoning Tokens / Task Thinking tokens burned per task | 20K | 26K |
Specifiche Tecniche
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-5.6 Luna | GPT-5.5 Instant |
|---|---|---|
| Official API Model ID (SDK) | gpt-5-6-luna | gpt-5-5-instant |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-07 | 2026-05 |
| Knowledge Cutoff | February 2026 | December 2025 |
| Architecture | Efficient compact reasoning model | Low-latency 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) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.2/1M in) across full 1050K window | Flat rate ($0.75/1M in) across full 400K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.02/1M (no extra cache-write fee) | Automatic prefix caching at $0.075/1M (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 Luna vs GPT-5.5 Instant: Risposte Rapide (FAQ)
Which is more intelligent, GPT-5.6 Luna or GPT-5.5 Instant?
GPT-5.5 Instant is more intelligent. It scores 65 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 168 tokens per second.
Which model is cheaper to run?
GPT-5.6 Luna is cheaper at $0.17 per 1M tokens blended, compared with $0.80 (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 0.78s.
Which model has a larger context window?
GPT-5.6 Luna has a larger context window, supporting 1.05M tokens compared with 400K tokens.
Verdetto
Sia GPT-5.6 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-5.5 Instant (Ideale per la massima efficienza dei costi per token e l’inferenza rapida in produzione, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: GPT-5.5 Instant guida nei benchmark di ragionamento e programmazione, GPT-5.6 Luna garantisce una generazione più rapida e GPT-5.6 Luna offre il miglior rapporto qualità-prezzo per carichi API elevati.
Domande Frequenti (FAQ)
Quale modello è migliore per coding e ragionamento, GPT-5.6 Luna o GPT-5.5 Instant?+
Nell’indice di intelligenza composito e nei benchmark di programmazione prevale GPT-5.5 Instant. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.
Qual è la differenza di prezzo API tra GPT-5.6 Luna e GPT-5.5 Instant?+
GPT-5.6 Luna costa $0.20 / $1.20 per milione di token di input/output, mentre GPT-5.5 Instant costa $0.75 / $6.00. Nel costo combinato, GPT-5.6 Luna risulta più economico.
Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+
GPT-5.6 Luna supporta 1.05M token di contesto contro 400K token di GPT-5.5 Instant, mentre GPT-5.6 Luna offre una velocità di generazione superiore.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
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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.
Fonti ufficiali: Documentazione API ufficiale di OpenAI e OpenAI, schede di sistema e benchmark indipendenti (2026).