Head-to-Head Model Comparison

GPT-6 Astra 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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GPT-6 AstraProfile

Claude Opus 5.5Profile

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OpenAI

GPT-6 Astra

Natives Sprach- & Audiomodell

GPT-6 Astra von OpenAI ist ein KI-Modell für natürliche Sprachsynthese, Echtzeit-Audioverarbeitung und präzise Transkription mit 272K Kontextfenster, 100 Token/s Durchsatz, einem Intelligenz-Index von 96/100 und $10/$50 pro 1M Token.

Intelligence Index96 / 100

Input Token:

$10.00 / 1M

Output Token:

$50.00 / 1M

Context Window:

272K tokens

SWE-bench Verified:

82.4%

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

Claude Opus 5.5 is 2.5x cheaper for API usage (input + output tokens combined).
Claude Opus 5.5 has a larger context window (1M vs 272K tokens).
GPT-6 Astra is faster at 100 tok/s vs 78 tok/s.
Claude Opus 5.5 leads on SWE-bench coding (84.6% vs 82.4%).

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

Feature	GPT-6 Astra	Claude Opus 5.5
Intelligence Index Composite reasoning & knowledge score (0-100)	96	94
Provider	OpenAI	Anthropic
Input Price Cost per million input tokens	$10.00 / 1M	$4.00 / 1M
Output Price Cost per million output tokens	$50.00 / 1M	$20.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$7.70 / 1M	$3.08 / 1M
Context Window Maximum input + output tokens	272K tokens~408 A4 pages of text	1M tokens~1,500 A4 pages of text
Max Output Tokens Per single request	128K	128K
Throughput Speed Tokens generated per second	100 tok/s	78 tok/s
SWE-bench Verified Real-world software engineering	82.4%	84.6%
GPQA Diamond Graduate-level reasoning	80.9%	82.3%
Cached Input Price Prompt caching discount per 1M tokens	$1.00 / 1M	$0.40 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework

Coding

Claude Opus 5.5

+2.2% SWE-bench lead

Cost

Claude Opus 5.5

2.5x cheaper monthly runtime

Speed

GPT-6 Astra

+28% faster generation

Context

Claude Opus 5.5

1M tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

GPT-6 AstraClaude Opus 5.5

SWE-bench Verified (Autonomous Coding)

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

82.4%vs84.6%

GPQA Diamond (Doctorate-Level Science & Math)

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

80.9%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.

1520ptsvs1507pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

18.2%vs19.8%

Terminal-Bench (Agentic Coding & Terminal Use)

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

63.8%vs66.4%

SciCode (Scientific Computing)

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

54.9%vs58.7%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

79.8%vs83.1%

AIME (Advanced Math Competition)

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

93.6%vs95.4%

Long-Context Reasoning

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

91.2%vs92.6%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

100 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 Astra0.95s

Claude Opus 5.51.40s

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

GPT-6 Astra5.95s

Claude Opus 5.57.81s

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-6 Astra Monthly API Bill

$61.50 / month

$10/1M in · $50/1M out

Claude Opus 5.5 Monthly API Bill

$24.60 / month

$4/1M in · $20/1M out

Claude Opus 5.5 saves approximately $36.90 / month (60%) under this workload.2.5x Savings

GPT-6 Astra · Strengths & Key Advantages

272K Kontextfenster und bis zu 128K maximale Output-Token (SWE-bench Verified 82.4% · GPQA Diamond 80.9%)
Hohe Generierungsgeschwindigkeit von 100 Token/Sekunde bei nur 950 ms Erstlatenz (TTFT)
Effiziente API-Struktur ($10/$50 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.

Metric	GPT-6 Astra	Claude Opus 5.5
Cost per Task Estimated USD to finish one task	$2.15	$3.85
Output Tokens / Task Tokens generated per task	58K	68K
Reasoning Tokens / Task Thinking tokens burned per task	32K	42K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

Feature	GPT-6 Astra	Claude Opus 5.5
Official API Model ID (SDK)	gpt-6-astra	claude-opus-5-5-20260901
API Protocol	OpenAI Responses API & Chat Completions v1	Anthropic Messages API + AWS Bedrock + Vertex AI
Release Date	2026-09	2026-09
Knowledge Cutoff	June 2026	June 2026
Architecture	Omni-Adaptive Autoregressive with Dynamic CoT	Extended Autonomous Hybrid Reasoning Transformer
Parameters	Confidential	Confidential
Enterprise Rate Limits (RPM / TPM)	10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM	4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)	Flat rate ($10/1M in) across full 272K window	Standard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTL	Automatic prefix caching at $1/1M (no extra cache-write fee)	$0.4/1M cached read (90% off); 5-min default & 1-hour extended 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	Native Web Search Citations + Code Execution Tool + MCP Connectors
Official Fine-Tuning Support	Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)	Custom Model Tuning via Amazon Bedrock & Prompt Distillation
Multimodal Token Billing	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec)	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	Anthropic 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 Tier	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalities	text, vision, audio	text, vision
Output Modalities	text, audio	text

GPT-6 Astra vs Claude Opus 5.5: Schnelle Antworten (FAQ)

Which is more intelligent, GPT-6 Astra or Claude Opus 5.5?

GPT-6 Astra is more intelligent. It scores 96 compared with 94 on the composite Intelligence Index.

Which model is faster?

GPT-6 Astra is faster, generating 100 tokens per second compared with 78 tokens per second.

Which model is cheaper to run?

Claude Opus 5.5 is cheaper at $3.08 per 1M tokens blended, compared with $7.70 (7:2:1 cache:input:output ratio).

Which model has lower latency?

GPT-6 Astra has lower latency, with a time to first token of 0.95s compared with 1.40s.

Which model has a larger context window?

Claude Opus 5.5 has a larger context window, supporting 1M tokens compared with 272K tokens.

Fazit

Sowohl GPT-6 Astra (Optimal für natürliche Sprachsynthese, Echtzeit-Audioverarbeitung und präzise Transkription, 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 Astra führt bei komplexem Reasoning und Coding, GPT-6 Astra liefert den höheren Token-Durchsatz und Claude Opus 5.5 ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – GPT-6 Astra oder Claude Opus 5.5?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt GPT-6 Astra vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von GPT-6 Astra und Claude Opus 5.5?+

GPT-6 Astra kostet $10.00 / $50.00 pro 1M Input-/Output-Tokens, Claude Opus 5.5 liegt bei $4.00 / $20.00. Beim gewichteten Gesamtpreis ist Claude Opus 5.5 günstiger.

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

GPT-6 Astra bietet 272K Tokens Kontext gegenüber 1M Tokens bei Claude Opus 5.5. Bei der Generierungsgeschwindigkeit liegt GPT-6 Astra 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-6 Astra vs Claude Opus 5.5

Beliebige Modelle wechseln & vergleichen

GPT-6 Astra

Claude Opus 5.5

Wer gewinnt in welcher Disziplin?

Geprüfte Benchmark-Ergebnisse im Vergleich

Latenz & Antwortzeit

Interaktiver Token-Kosten-Simulator

GPT-6 Astra · Strengths & Key Advantages

Considerations & Trade-offs

Claude Opus 5.5 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Technische Spezifikationen

GPT-6 Astra vs Claude Opus 5.5: Schnelle Antworten (FAQ)

Fazit

Häufig gestellte Fragen (FAQ)

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