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Head-to-Head Model Comparison

GPT-6 Luna vs GPT-5.6 Sol

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
GPT-6 Luna vs GPT-5.6 Sol: Benchmarks, Pricing & Speed Comparison

GPT-6 Luna von OpenAI und GPT-5.6 Sol von OpenAI gehören zu den führenden KI-Modellen. GPT-6 Luna bietet ein Kontextfenster von 272K Tokens bei API-Preisen von $0.10/$0.50 pro 1M Tokens, während GPT-5.6 Sol 1.05M Tokens Kontext zu $4.00/$20.00 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

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OpenAI

GPT-6 Luna

Kompaktes Effizienz-Modell

GPT-6 Luna von OpenAI ist ein KI-Modell für maximale Kosteneffizienz pro Token und ressourcenschonende Produktions-Workloads mit 272K Kontextfenster, 245 Token/s Durchsatz, einem Intelligenz-Index von 67/100 und $0.1/$0.5 pro 1M Token.

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

Frontier-Flaggschiff-Modell

GPT-5.6 Sol von OpenAI ist ein KI-Modell für anspruchsvolles Enterprise-Reasoning, Softwareentwicklung und produktive KI-Workflows mit 1.1M Kontextfenster, 108 Token/s Durchsatz, einem Intelligenz-Index von 89/100 und $4/$20 pro 1M Token.

Intelligence Index89 / 100
Input Token:

$4.00 / 1M

Output Token:

$20.00 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

83.8%

Wichtigste Unterschiede auf einen Blick

  • 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 →
FeatureGPT-6 LunaGPT-5.6 Sol
Intelligence Index
Composite reasoning & knowledge score (0-100)
6789
Provider
OpenAIOpenAI
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 text1.05M tokens~1,575 A4 pages of text
Max Output Tokens
Per single request
128K128K
Throughput Speed
Tokens generated per second
245 tok/s108 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

Wer gewinnt in welcher Disziplin?

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

Geprüfte Benchmark-Ergebnisse im Vergleich

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

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-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

Interaktiver Token-Kosten-Simulator

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

100K / day
10K500K1M
30K / day
5K150K300K
50% 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

  • 272K Kontextfenster und bis zu 128K maximale Output-Token (SWE-bench Verified 70.5% · GPQA Diamond 73.5%)
  • Hohe Generierungsgeschwindigkeit von 245 Token/Sekunde bei nur 380 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($0.1/$0.5 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

GPT-5.6 Sol · Strengths & Key Advantages

  • 1.1M Kontextfenster und bis zu 128K maximale Output-Token (SWE-bench Verified 83.8% · GPQA Diamond 82%)
  • Hohe Generierungsgeschwindigkeit von 108 Token/Sekunde bei nur 1210 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($4/$20 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 hohem Anfragevolumen empfiehlt sich der Einsatz von Prompt-Caching und Batch-API zur Kostenoptimierung

Token Use & Task Economics

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

MetricGPT-6 LunaGPT-5.6 Sol
Cost per Task
Estimated USD to finish one task
$0.13$3.30
Output Tokens / Task
Tokens generated per task
46K64K
Reasoning Tokens / Task
Thinking tokens burned per task
21K38K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

FeatureGPT-6 LunaGPT-5.6 Sol
Official API Model ID (SDK)gpt-6-lunagpt-5-6-sol
API ProtocolOpenAI Responses API & Chat Completions v1OpenAI Responses API & Chat Completions v1
Release Date2026-092026-07
Knowledge CutoffApril 2026February 2026
ArchitectureUltra-budget compact reasoning modelFrontier unified reasoning model
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM10,000 – 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 windowFlat rate ($4/1M in) across full 1050K window
Prompt Cache Policy & TTLAutomatic 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 AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)Supported (Screen coordinate grounding, browser & desktop GUI action loops)
MCP & Parallel Tool CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxNative Web Search tool + Hosted Code Interpreter & File Search RAGNative Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning SupportSupervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
Multimodal Token BillingText 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 VRAMCloud-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 ClassificationOpenAI Preparedness Framework Audited & System Card PublishedOpenAI Preparedness Framework Audited & System Card Published
Enterprise Compliance (ZDR/HIPAA/SOC2)SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise TierSOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalitiestext, visiontext, vision
Output Modalitiestexttext

GPT-6 Luna vs GPT-5.6 Sol: Schnelle Antworten (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.

Fazit

Sowohl GPT-6 Luna (Optimal für maximale Kosteneffizienz pro Token und ressourcenschonende Produktions-Workloads, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch GPT-5.6 Sol (Optimal für anspruchsvolles Enterprise-Reasoning, Softwareentwicklung und produktive KI-Workflows, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: GPT-5.6 Sol führt bei komplexem Reasoning und Coding, GPT-6 Luna liefert den höheren Token-Durchsatz und GPT-6 Luna ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – GPT-6 Luna oder GPT-5.6 Sol?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt GPT-5.6 Sol vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von GPT-6 Luna und GPT-5.6 Sol?+

GPT-6 Luna kostet $0.10 / $0.50 pro 1M Input-/Output-Tokens, GPT-5.6 Sol liegt bei $4.00 / $20.00. Beim gewichteten Gesamtpreis ist GPT-6 Luna günstiger.

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

GPT-6 Luna bietet 272K Tokens Kontext gegenüber 1.05M Tokens bei GPT-5.6 Sol. Bei der Generierungsgeschwindigkeit liegt GPT-6 Luna 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 & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

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.

Offizielle Quellen: Offizielle API-Dokumentation von OpenAI und OpenAI, Systemkarten und unabhängige Benchmark-Auswertungen (2026).