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Head-to-Head Model Comparison

GPT-6.1 Sol vs Claude Opus 5.5

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-6.1 Sol vs Claude Opus 5.5: Benchmarks, Pricing & Speed Comparison

GPT-6.1 Sol von OpenAI und Claude Opus 5.5 von Anthropic gehören zu den führenden KI-Modellen. GPT-6.1 Sol bietet ein Kontextfenster von 1M Tokens bei API-Preisen von $2.00/$10.00 pro 1M Tokens, während Claude Opus 5.5 1M 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-6.1 Sol

Frontier-Flaggschiff-Modell

GPT-6.1 Sol von OpenAI ist ein KI-Modell für anspruchsvolles Enterprise-Reasoning, Softwareentwicklung und produktive KI-Workflows mit 1M Kontextfenster, 120 Token/s Durchsatz, einem Intelligenz-Index von 94/100 und $2/$10 pro 1M Token.

Intelligence Index94 / 100
Input Token:

$2.00 / 1M

Output Token:

$10.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

82.3%

Anthropic

Claude Opus 5.5

Frontier-Flaggschiff-Modell

Claude Opus 5.5 von Anthropic ist ein KI-Modell für anspruchsvolles Enterprise-Reasoning, Softwareentwicklung und produktive KI-Workflows mit 1M Kontextfenster, 78 Token/s Durchsatz, einem Intelligenz-Index von 94/100 und $4/$20 pro 1M Toke

Intelligence Index94 / 100
Input Token:

$4.00 / 1M

Output Token:

$20.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

84.6%

Wichtigste Unterschiede auf einen Blick

  • GPT-6.1 Sol is 2.0x cheaper for API usage (input + output tokens combined).
  • GPT-6.1 Sol is faster at 120 tok/s vs 78 tok/s.
  • Claude Opus 5.5 leads on SWE-bench coding (84.6% vs 82.3%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGPT-6.1 SolClaude Opus 5.5
Intelligence Index
Composite reasoning & knowledge score (0-100)
9494
Provider
OpenAIAnthropic
Input Price
Cost per million input tokens
$2.00 / 1M$4.00 / 1M
Output Price
Cost per million output tokens
$10.00 / 1M$20.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$1.47 / 1M$3.08 / 1M
Context Window
Maximum input + output tokens
1M tokens~1,500 A4 pages of text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
128K128K
Throughput Speed
Tokens generated per second
120 tok/s78 tok/s
SWE-bench Verified
Real-world software engineering
82.3%84.6%
GPQA Diamond
Graduate-level reasoning
80.8%82.3%
Cached Input Price
Prompt caching discount per 1M tokens
$0.10 / 1M$0.40 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework
Coding
Claude Opus 5.5
+2.3% SWE-bench lead
Cost
GPT-6.1 Sol
2.0x cheaper monthly runtime
Speed
GPT-6.1 Sol
+54% faster generation
Context
Tied
1M tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

GPT-6.1 SolClaude Opus 5.5
SWE-bench Verified (Autonomous Coding)

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

82.3%vs84.6%
GPQA Diamond (Doctorate-Level Science & Math)

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

80.8%vs82.3%
MMLU-Pro (Multi-task Language Understanding)

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

89.9%vs91.5%
LMSYS Chatbot Arena Elo

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

1519ptsvs1507pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

18.1%vs19.8%
Terminal-Bench (Agentic Coding & Terminal Use)

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

63.6%vs66.4%
SciCode (Scientific Computing)

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

54.8%vs58.7%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

79.7%vs83.1%
AIME (Advanced Math Competition)

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

93.5%vs95.4%
Long-Context Reasoning

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

91%vs92.6%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

120 tok/s vs78 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-6.1 Sol1.12s
Claude Opus 5.51.40s
Est. End-to-End Response (500 tokens)TTFT + generation time
GPT-6.1 Sol5.29s
Claude Opus 5.57.81s

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-6.1 Sol Monthly API Bill
$12.15 / month
$2/1M in · $10/1M out
Claude Opus 5.5 Monthly API Bill
$24.60 / month
$4/1M in · $20/1M out
GPT-6.1 Sol saves approximately $12.45 / month (51%) under this workload.2.0x Savings

GPT-6.1 Sol · Strengths & Key Advantages

  • 1M Kontextfenster und bis zu 128K maximale Output-Token (SWE-bench Verified 82.3% · GPQA Diamond 80.8%)
  • Hohe Generierungsgeschwindigkeit von 120 Token/Sekunde bei nur 1120 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($2/$10 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

Claude Opus 5.5 · Strengths & Key Advantages

  • 1M Kontextfenster und bis zu 128K maximale Output-Token (SWE-bench Verified 84.6% · GPQA Diamond 82.3%)
  • Hohe Generierungsgeschwindigkeit von 78 Token/Sekunde bei nur 1400 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-6.1 SolClaude Opus 5.5
Cost per Task
Estimated USD to finish one task
$2.05$3.85
Output Tokens / Task
Tokens generated per task
62K68K
Reasoning Tokens / Task
Thinking tokens burned per task
36K42K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

FeatureGPT-6.1 SolClaude Opus 5.5
Official API Model ID (SDK)gpt-6-1-solclaude-opus-5-5-20260901
API ProtocolOpenAI Responses API & Chat Completions v1Anthropic Messages API + AWS Bedrock + Vertex AI
Release Date2026-092026-09
Knowledge CutoffApril 2026June 2026
ArchitectureNear-Astra unified reasoning modelExtended Autonomous Hybrid Reasoning Transformer
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)Flat rate ($2/1M in) across full 1000K windowStandard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTLAutomatic prefix caching at $0.1/1M (no extra cache-write fee)$0.4/1M cached read (90% off); 5-min default & 1-hour extended TTL
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 Citations + Code Execution Tool + MCP Connectors
Official Fine-Tuning SupportSupervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)Custom Model Tuning via Amazon Bedrock & Prompt Distillation
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 PublishedAnthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated
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-6.1 Sol vs Claude Opus 5.5: Schnelle Antworten (FAQ)

Which model is faster?

GPT-6.1 Sol is faster, generating 120 tokens per second compared with 78 tokens per second.

Which model is cheaper to run?

GPT-6.1 Sol is cheaper at $1.47 per 1M tokens blended, compared with $3.08 (7:2:1 cache:input:output ratio).

Which model has lower latency?

GPT-6.1 Sol has lower latency, with a time to first token of 1.12s compared with 1.40s.

Fazit

Sowohl GPT-6.1 Sol (Optimal für anspruchsvolles Enterprise-Reasoning, Softwareentwicklung und produktive KI-Workflows, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Claude Opus 5.5 (Optimal für anspruchsvolles Enterprise-Reasoning, Softwareentwicklung und produktive KI-Workflows, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: GPT-6.1 Sol führt bei komplexem Reasoning und Coding, GPT-6.1 Sol liefert den höheren Token-Durchsatz und GPT-6.1 Sol ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – GPT-6.1 Sol oder Claude Opus 5.5?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt GPT-6.1 Sol vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von GPT-6.1 Sol und Claude Opus 5.5?+

GPT-6.1 Sol kostet $2.00 / $10.00 pro 1M Input-/Output-Tokens, Claude Opus 5.5 liegt bei $4.00 / $20.00. Beim gewichteten Gesamtpreis ist GPT-6.1 Sol günstiger.

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

GPT-6.1 Sol bietet 1M Tokens Kontext gegenüber 1M Tokens bei Claude Opus 5.5. Bei der Generierungsgeschwindigkeit liegt GPT-6.1 Sol 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 Anthropic, Systemkarten und unabhängige Benchmark-Auswertungen (2026).