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GPT-6 Astra vs Grok-4

Benchmarks, API Token Pricing, Context Window & Speed Comparison

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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
GPT-6 Astra vs Grok-4: Benchmarks, Pricing & Speed Comparison

GPT-6 Astra von OpenAI und Grok-4 von xAI gehören zu den führenden KI-Modellen. GPT-6 Astra bietet ein Kontextfenster von 272K Tokens bei API-Preisen von $10.00/$50.00 pro 1M Tokens, während Grok-4 1M Tokens Kontext zu $2.50/$10.00 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

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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%

xAI

Grok-4

Frontier-Flaggschiff-Modell

Grok-4 von xAI ist ein KI-Modell für anspruchsvolles Enterprise-Reasoning, Softwareentwicklung und produktive KI-Workflows mit 1M Kontextfenster, 125 Token/s Durchsatz, einem Intelligenz-Index von 92/100 und $2.5/$10 pro 1M Token.

Intelligence Index92 / 100
Input Token:

$2.50 / 1M

Output Token:

$10.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

76.5%

Wichtigste Unterschiede auf einen Blick

  • Grok-4 is 4.7x cheaper for API usage (input + output tokens combined).
  • Grok-4 has a larger context window (1M vs 272K tokens).
  • Grok-4 is faster at 125 tok/s vs 100 tok/s.
  • GPT-6 Astra leads on SWE-bench coding (82.4% vs 76.5%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGPT-6 AstraGrok-4
Intelligence Index
Composite reasoning & knowledge score (0-100)
9692
Provider
OpenAIxAI
Input Price
Cost per million input tokens
$10.00 / 1M$2.50 / 1M
Output Price
Cost per million output tokens
$50.00 / 1M$10.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$7.70 / 1M$1.68 / 1M
Context Window
Maximum input + output tokens
272K tokens~408 A4 pages of text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
128K64K
Throughput Speed
Tokens generated per second
100 tok/s125 tok/s
SWE-bench Verified
Real-world software engineering
82.4%76.5%
GPQA Diamond
Graduate-level reasoning
80.9%77.8%
Cached Input Price
Prompt caching discount per 1M tokens
$1.00 / 1M$0.25 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework
Coding
GPT-6 Astra
+5.9% SWE-bench lead
Cost
Grok-4
4.7x cheaper monthly runtime
Speed
Grok-4
+25% faster generation
Context
Grok-4
1M tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

GPT-6 AstraGrok-4
SWE-bench Verified (Autonomous Coding)

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

82.4%vs76.5%
GPQA Diamond (Doctorate-Level Science & Math)

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

80.9%vs77.8%
MMLU-Pro (Multi-task Language Understanding)

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

89.9%vs87.4%
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%vs15.6%
Terminal-Bench (Agentic Coding & Terminal Use)

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

63.8%vs54.2%
SciCode (Scientific Computing)

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

54.9%vs48.7%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

79.8%vs75.3%
AIME (Advanced Math Competition)

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

93.6%vs89.2%
Long-Context Reasoning

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

91.2%vs86.5%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

100 tok/s vs125 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
Grok-40.68s
Est. End-to-End Response (500 tokens)TTFT + generation time
GPT-6 Astra5.95s
Grok-44.68s

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 Astra Monthly API Bill
$61.50 / month
$10/1M in · $50/1M out
Grok-4 Monthly API Bill
$13.13 / month
$2.5/1M in · $10/1M out
Grok-4 saves approximately $48.38 / month (79%) under this workload.4.7x 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

Grok-4 · Strengths & Key Advantages

  • 1M Kontextfenster und bis zu 64K maximale Output-Token (SWE-bench Verified 76.5% · GPQA Diamond 77.8%)
  • Hohe Generierungsgeschwindigkeit von 125 Token/Sekunde bei nur 680 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($2.5/$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

Token Use & Task Economics

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

MetricGPT-6 AstraGrok-4
Cost per Task
Estimated USD to finish one task
$2.15$1.65
Output Tokens / Task
Tokens generated per task
58K48K
Reasoning Tokens / Task
Thinking tokens burned per task
32K24K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

FeatureGPT-6 AstraGrok-4
Official API Model ID (SDK)gpt-6-astragrok-4-latest
API ProtocolOpenAI Responses API & Chat Completions v1xAI REST API (OpenAI-Compatible)
Release Date2026-092026-07
Knowledge CutoffJune 2026Real-time (Up to October 2026)
ArchitectureOmni-Adaptive Autoregressive with Dynamic CoTColossus-Scale Dense Mixture-of-Experts
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM5,000 – 15,000 RPM (Production Tier) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)Flat rate ($10/1M in) across full 272K windowFlat rate ($2.5/1M in) across full 1000K window
Prompt Cache Policy & TTLAutomatic prefix caching at $1/1M (no extra cache-write fee)Cached input at $0.25/1M tokens
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 RAGReal-time Web & X (Twitter) Search Grounding + Code Execution
Official Fine-Tuning SupportSupervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)API Prompt Engineering & RAG Distillation
Multimodal Token BillingText 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 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 PublishedxAI Production Safety & Alignment Verified
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, vision, audiotext, vision
Output Modalitiestext, audiotext

GPT-6 Astra vs Grok-4: Schnelle Antworten (FAQ)

Which is more intelligent, GPT-6 Astra or Grok-4?

GPT-6 Astra is more intelligent. It scores 96 compared with 92 on the composite Intelligence Index.

Which model is faster?

Grok-4 is faster, generating 125 tokens per second compared with 100 tokens per second.

Which model is cheaper to run?

Grok-4 is cheaper at $1.68 per 1M tokens blended, compared with $7.70 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Grok-4 has lower latency, with a time to first token of 0.68s compared with 0.95s.

Which model has a larger context window?

Grok-4 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 Grok-4 (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, Grok-4 liefert den höheren Token-Durchsatz und Grok-4 ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – GPT-6 Astra oder Grok-4?+

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 Grok-4?+

GPT-6 Astra kostet $10.00 / $50.00 pro 1M Input-/Output-Tokens, Grok-4 liegt bei $2.50 / $10.00. Beim gewichteten Gesamtpreis ist Grok-4 günstiger.

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

GPT-6 Astra bietet 272K Tokens Kontext gegenüber 1M Tokens bei Grok-4. Bei der Generierungsgeschwindigkeit liegt Grok-4 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 xAI, Systemkarten und unabhängige Benchmark-Auswertungen (2026).