Head-to-Head Model Comparison

GPT-5.6 Luna vs Gemini 3.5 Flash-Lite

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien

Beliebige Modelle wechseln & vergleichen

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

GPT-5.6 LunaProfile

Gemini 3.5 Flash-LiteProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

OpenAI

GPT-5.6 Luna

Kompaktes Effizienz-Modell

GPT-5.6 Luna von OpenAI ist ein KI-Modell für maximale Kosteneffizienz pro Token und ressourcenschonende Produktions-Workloads mit 1.1M Kontextfenster, 230 Token/s Durchsatz, einem Intelligenz-Index von 57/100 und $0.2/$1.2 pro 1M Token.

Intelligence Index57 / 100

Input Token:

$0.20 / 1M

Output Token:

$1.20 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

63.5%

Google

Gemini 3.5 Flash-Lite

Ultraschnelle Echtzeit-Inferenz

Gemini 3.5 Flash-Lite von Google ist ein KI-Modell für ultraschnelle Echtzeit-Antworten, niedrige Latenz und Hochdurchsatz-Routing mit 1.0M Kontextfenster, 240 Token/s Durchsatz, einem Intelligenz-Index von 69/100 und $0.4/$2 pro 1M Token.

Intelligence Index69 / 100

Input Token:

$0.40 / 1M

Output Token:

$2.00 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

N/A

Wichtigste Unterschiede auf einen Blick

GPT-5.6 Luna is 1.8x cheaper for API usage (input + output tokens combined).
GPT-5.6 Luna has a larger context window (1M vs 1M tokens).
Gemini 3.5 Flash-Lite is faster at 240 tok/s vs 230 tok/s.

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

Feature	GPT-5.6 Luna	Gemini 3.5 Flash-Lite
Intelligence Index Composite reasoning & knowledge score (0-100)	57	69
Provider	OpenAI	Google
Input Price Cost per million input tokens	$0.20 / 1M	$0.40 / 1M
Output Price Cost per million output tokens	$1.20 / 1M	$2.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.17 / 1M	$0.35 / 1M
Context Window Maximum input + output tokens	1.05M tokens~1,575 A4 pages of text	1.05M tokens~1,573 A4 pages of text
Max Output Tokens Per single request	128K	33K
Throughput Speed Tokens generated per second	230 tok/s	240 tok/s
SWE-bench Verified Real-world software engineering	63.5%	N/A
GPQA Diamond Graduate-level reasoning	70.5%	N/A
Cached Input Price Prompt caching discount per 1M tokens	$0.02 / 1M	$0.10 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework

Coding

Evaluations ongoing

Cost

GPT-5.6 Luna

1.8x cheaper monthly runtime

Speed

Gemini 3.5 Flash-Lite

+4% faster generation

Context

GPT-5.6 Luna

1.05M tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

GPT-5.6 LunaGemini 3.5 Flash-Lite

SWE-bench Verified (Autonomous Coding)

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

63.5%vsN/A

GPQA Diamond (Doctorate-Level Science & Math)

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

70.5%vsN/A

MMLU-Pro (Multi-task Language Understanding)

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

83.5%vs81.2%

LMSYS Chatbot Arena Elo

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

1368ptsvs1375pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

11.4%vsN/A

Terminal-Bench (Agentic Coding & Terminal Use)

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

38.5%vsN/A

SciCode (Scientific Computing)

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

36%vsN/A

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

62.5%vsN/A

AIME (Advanced Math Competition)

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

87%vsN/A

Long-Context Reasoning

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

76.5%vsN/A

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

230 tok/s vs240 tok/s

Latenz & Antwortzeit

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 Luna0.42s

Gemini 3.5 Flash-Lite0.29s

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

GPT-5.6 Luna2.59s

Gemini 3.5 Flash-Lite2.37s

Interaktiver Token-Kosten-Simulator

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 Luna Monthly API Bill

$1.41 / month

$0.2/1M in · $1.2/1M out

Gemini 3.5 Flash-Lite Monthly API Bill

$2.55 / month

$0.4/1M in · $2/1M out

GPT-5.6 Luna saves approximately $1.14 / month (45%) under this workload.1.8x Savings

GPT-5.6 Luna · Strengths & Key Advantages

1.1M Kontextfenster und bis zu 128K maximale Output-Token (SWE-bench Verified 63.5% · GPQA Diamond 70.5%)
Hohe Generierungsgeschwindigkeit von 230 Token/Sekunde bei nur 420 ms Erstlatenz (TTFT)
Effiziente API-Struktur ($0.2/$1.2 pro 1M Token) inklusive Prompt-Caching, Tool-Calling und strikter JSON-Ausgabe

Considerations & Trade-offs

Proprietäres Cloud-Modell ohne Möglichkeit zum lokalen Download der Modellgewichte
Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

Gemini 3.5 Flash-Lite · Strengths & Key Advantages

1.0M Kontextfenster und bis zu 33K maximale Output-Token (MMLU-Pro 81.2% · Arena Elo 1375)
Hohe Generierungsgeschwindigkeit von 240 Token/Sekunde bei nur 290 ms Erstlatenz (TTFT)
Effiziente API-Struktur ($0.4/$2 pro 1M Token) inklusive Prompt-Caching, Tool-Calling und strikter JSON-Ausgabe

Considerations & Trade-offs

Proprietäres Cloud-Modell ohne Möglichkeit zum lokalen Download der Modellgewichte
Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

Token Use & Task Economics

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

Metric	GPT-5.6 Luna	Gemini 3.5 Flash-Lite
Cost per Task Estimated USD to finish one task	$0.19	$0.55
Output Tokens / Task Tokens generated per task	46K	18K
Reasoning Tokens / Task Thinking tokens burned per task	20K	7K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

Feature	GPT-5.6 Luna	Gemini 3.5 Flash-Lite
Official API Model ID (SDK)	gpt-5-6-luna	models/gemini-3-5-flash-lite
API Protocol	OpenAI Responses API & Chat Completions v1	Google Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date	2026-07	2026-07
Knowledge Cutoff	February 2026	March 2026
Architecture	Efficient compact reasoning model	Lightweight efficient multimodal transformer
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)
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
Output Modalities	text	text

GPT-5.6 Luna vs Gemini 3.5 Flash-Lite: Schnelle Antworten (FAQ)

Which is more intelligent, GPT-5.6 Luna or Gemini 3.5 Flash-Lite?

Gemini 3.5 Flash-Lite is more intelligent. It scores 69 compared with 57 on the composite Intelligence Index.

Which model is faster?

Gemini 3.5 Flash-Lite is faster, generating 240 tokens per second compared with 230 tokens per second.

Which model is cheaper to run?

GPT-5.6 Luna is cheaper at $0.17 per 1M tokens blended, compared with $0.35 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Gemini 3.5 Flash-Lite has lower latency, with a time to first token of 0.29s compared with 0.42s.

Which model has a larger context window?

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

Fazit

Sowohl GPT-5.6 Luna (Optimal für maximale Kosteneffizienz pro Token und ressourcenschonende Produktions-Workloads, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Gemini 3.5 Flash-Lite (Optimal für ultraschnelle Echtzeit-Antworten, niedrige Latenz und Hochdurchsatz-Routing, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Gemini 3.5 Flash-Lite führt bei komplexem Reasoning und Coding, Gemini 3.5 Flash-Lite liefert den höheren Token-Durchsatz und GPT-5.6 Luna ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – GPT-5.6 Luna oder Gemini 3.5 Flash-Lite?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Gemini 3.5 Flash-Lite vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von GPT-5.6 Luna und Gemini 3.5 Flash-Lite?+

GPT-5.6 Luna kostet $0.20 / $1.20 pro 1M Input-/Output-Tokens, Gemini 3.5 Flash-Lite liegt bei $0.40 / $2.00. Beim gewichteten Gesamtpreis ist GPT-5.6 Luna günstiger.

Welches Modell hat das größere Kontextfenster und die höhere Geschwindigkeit?+

GPT-5.6 Luna bietet 1.05M Tokens Kontext gegenüber 1.05M Tokens bei Gemini 3.5 Flash-Lite. Bei der Generierungsgeschwindigkeit liegt Gemini 3.5 Flash-Lite vorn.

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 Luna vs Gemini 3.5 Flash-Lite

Beliebige Modelle wechseln & vergleichen

GPT-5.6 Luna

Gemini 3.5 Flash-Lite

Wer gewinnt in welcher Disziplin?

Geprüfte Benchmark-Ergebnisse im Vergleich

Latenz & Antwortzeit

Interaktiver Token-Kosten-Simulator

GPT-5.6 Luna · Strengths & Key Advantages

Considerations & Trade-offs

Gemini 3.5 Flash-Lite · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Technische Spezifikationen

GPT-5.6 Luna vs Gemini 3.5 Flash-Lite: Schnelle Antworten (FAQ)

Fazit

Häufig gestellte Fragen (FAQ)

Related reading