GPT-5.6 Luna vs Gemini 4 Argon
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare GPT-5.6 Luna by OpenAI against Gemini 4 Argon by Google across real-world coding, reasoning, token economics, and latency.
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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%
Gemini 4 Argon
IA Multimodale NativaGemini 4 Argon di Google è un modello di IA progettato per la comprensione multimodale nativa di testo e immagini e l’analisi visiva dei documenti, con finestra di contesto da 1M, velocità di 115 token/s, indice di intelligenza di 97/100 e
$2.00 / 1M
$10.00 / 1M
1M tokens
81.9%
Differenze Chiave a Colpo d’Occhio
- GPT-5.6 Luna is 8.6x cheaper for API usage (input + output tokens combined).
- GPT-5.6 Luna has a larger context window (1M vs 1M tokens).
- GPT-5.6 Luna is faster at 230 tok/s vs 115 tok/s.
- Gemini 4 Argon leads on SWE-bench coding (81.9% vs 63.5%).
| Feature | GPT-5.6 Luna | Gemini 4 Argon |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 57 | 97 |
Provider | OpenAI | |
Input Price Cost per million input tokens | $0.20 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $1.20 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.17 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 1.05M tokens~1,575 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 128K | 1000K |
Throughput Speed Tokens generated per second | 230 tok/s | 115 tok/s |
SWE-bench Verified Real-world software engineering | 63.5% | 81.9% |
GPQA Diamond Graduate-level reasoning | 70.5% | 81.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.02 / 1M | $0.10 / 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
Gemini 4 Argon · Strengths & Key Advantages
- Finestra di contesto da 1M con capacità massima di output fino a 1M token (SWE-bench Verified 81.9% · GPQA Diamond 81.5%)
- Throughput elevato di 115 token/secondo con latenza al primo token (TTFT) di soli 850 ms
- Economia API competitiva ($2/$10 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-5.6 Luna | Gemini 4 Argon |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.19 | $2.45 |
Output Tokens / Task Tokens generated per task | 46K | 75K |
Reasoning Tokens / Task Thinking tokens burned per task | 20K | 36K |
Specifiche Tecniche
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-5.6 Luna | Gemini 4 Argon |
|---|---|---|
| Official API Model ID (SDK) | gpt-5-6-luna | models/gemini-4-argon |
| API Protocol | OpenAI Responses API & Chat Completions v1 | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2026-07 | 2026-09 |
| Knowledge Cutoff | February 2026 | June 2026 |
| Architecture | Efficient compact reasoning model | Pathways Multimodal Sparse Mixture-of-Experts |
| 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 ($0.2/1M in) across full 1050K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.02/1M (no extra cache-write fee) | Implicit + Explicit Context Caching at $0.1/1M with configurable TTL |
| 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 | Built-in Google Search Grounding + Managed Python Code Execution Sandbox |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | Vertex AI Supervised Adapter Tuning & Context Distillation |
| 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) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) |
| 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 | Google DeepMind Frontier Safety Framework Verified |
| 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, audio, video |
| Output Modalities | text | text |
GPT-5.6 Luna vs Gemini 4 Argon: Risposte Rapide (FAQ)
Which is more intelligent, GPT-5.6 Luna or Gemini 4 Argon?
Gemini 4 Argon is more intelligent. It scores 97 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 115 tokens per second.
Which model is cheaper to run?
GPT-5.6 Luna is cheaper at $0.17 per 1M tokens blended, compared with $1.47 (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 0.85s.
Which model has a larger context window?
GPT-5.6 Luna has a larger context window, supporting 1.05M tokens compared with 1M tokens.
Verdetto
Choose GPT-5.6 Luna for ideale per la massima efficienza dei costi per token e l’inferenza rapida in produzione, sistemi rag aziendali e integrazioni api scalabili., or choose Gemini 4 Argon for ideale per la comprensione multimodale nativa di testo e immagini e l’analisi visiva dei documenti, sistemi rag aziendali e integrazioni api scalabili..
Domande Frequenti (FAQ)
Which is cheaper: GPT-5.6 Luna or Gemini 4 Argon?+
GPT-5.6 Luna costs $0.2/1M input and $1.2/1M output tokens, while Gemini 4 Argon costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: GPT-5.6 Luna or Gemini 4 Argon?+
GPT-5.6 Luna supports 1.1M tokens whereas Gemini 4 Argon supports 1M tokens.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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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: OpenAI & Google Official Documentation