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Head-to-Head Model Comparison

Grok 4.5 vs Grok 4.3

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
Grok 4.5 vs Grok 4.3: Benchmarks, Pricing & Speed Comparison

Grok 4.5 von xAI und Grok 4.3 von xAI gehören zu den führenden KI-Modellen. Grok 4.5 bietet ein Kontextfenster von 500K Tokens bei API-Preisen von $2.00/$6.00 pro 1M Tokens, während Grok 4.3 1M Tokens Kontext zu $1.25/$2.50 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

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xAI

Grok 4.5

Deep-Reasoning-Engine

Grok 4.5 von xAI ist ein KI-Modell für tiefgehendes logisches Denken, komplexe Mathematik und wissenschaftliche Analysen mit 500K Kontextfenster, 130 Token/s Durchsatz, einem Intelligenz-Index von 75/100 und $2/$6 pro 1M Token.

Intelligence Index75 / 100
Input Token:

$2.00 / 1M

Output Token:

$6.00 / 1M

Context Window:

500K tokens

SWE-bench Verified:

74.2%

xAI

Grok 4.3

Frontier-Flaggschiff-Modell

Grok 4.3 von xAI ist ein KI-Modell für anspruchsvolles Enterprise-Reasoning, Softwareentwicklung und produktive KI-Workflows mit 1M Kontextfenster, 110 Token/s Durchsatz, einem Intelligenz-Index von 71/100 und $1.25/$2.5 pro 1M Token.

Intelligence Index71 / 100
Input Token:

$1.25 / 1M

Output Token:

$2.50 / 1M

Context Window:

1M tokens

SWE-bench Verified:

71.4%

Wichtigste Unterschiede auf einen Blick

  • Grok 4.3 is 2.0x cheaper for API usage (input + output tokens combined).
  • Grok 4.3 has a larger context window (1M vs 500K tokens).
  • Grok 4.5 is faster at 130 tok/s vs 110 tok/s.
  • Grok 4.5 leads on SWE-bench coding (74.2% vs 71.4%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGrok 4.5Grok 4.3
Intelligence Index
Composite reasoning & knowledge score (0-100)
7571
Provider
xAIxAI
Input Price
Cost per million input tokens
$2.00 / 1M$1.25 / 1M
Output Price
Cost per million output tokens
$6.00 / 1M$2.50 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$1.35 / 1M$0.72 / 1M
Context Window
Maximum input + output tokens
500K tokens~750 A4 pages of text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
66K66K
Throughput Speed
Tokens generated per second
130 tok/s110 tok/s
SWE-bench Verified
Real-world software engineering
74.2%71.4%
GPQA Diamond
Graduate-level reasoning
75.9%73.5%
Cached Input Price
Prompt caching discount per 1M tokens
$0.50 / 1M$0.31 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework
Coding
Grok 4.5
+2.8% SWE-bench lead
Cost
Grok 4.3
2.0x cheaper monthly runtime
Speed
Grok 4.5
+18% faster generation
Context
Grok 4.3
1M tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Grok 4.5Grok 4.3
SWE-bench Verified (Autonomous Coding)

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

74.2%vs71.4%
GPQA Diamond (Doctorate-Level Science & Math)

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

75.9%vs73.5%
MMLU-Pro (Multi-task Language Understanding)

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

86.8%vs85.2%
LMSYS Chatbot Arena Elo

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

1435ptsvs1410pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

13.9%vs12.8%
Terminal-Bench (Agentic Coding & Terminal Use)

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

49.5%vs47%
SciCode (Scientific Computing)

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

47%vs44.5%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

73.5%vs71.2%
AIME (Advanced Math Competition)

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

87.8%vs85%
Long-Context Reasoning

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

87.2%vs88.5%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

130 tok/s vs110 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
Grok 4.50.50s
Grok 4.30.65s
Est. End-to-End Response (500 tokens)TTFT + generation time
Grok 4.54.35s
Grok 4.35.20s

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)
Grok 4.5 Monthly API Bill
$9.15 / month
$2/1M in · $6/1M out
Grok 4.3 Monthly API Bill
$4.59 / month
$1.25/1M in · $2.5/1M out
Grok 4.3 saves approximately $4.56 / month (50%) under this workload.2.0x Savings

Grok 4.5 · Strengths & Key Advantages

  • 500K Kontextfenster und bis zu 66K maximale Output-Token (SWE-bench Verified 74.2% · GPQA Diamond 75.9%)
  • Hohe Generierungsgeschwindigkeit von 130 Token/Sekunde bei nur 500 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($2/$6 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 4.3 · Strengths & Key Advantages

  • 1M Kontextfenster und bis zu 66K maximale Output-Token (SWE-bench Verified 71.4% · GPQA Diamond 73.5%)
  • Hohe Generierungsgeschwindigkeit von 110 Token/Sekunde bei nur 650 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($1.25/$2.5 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

Token Use & Task Economics

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

MetricGrok 4.5Grok 4.3
Cost per Task
Estimated USD to finish one task
$0.62$1.35
Output Tokens / Task
Tokens generated per task
44K46K
Reasoning Tokens / Task
Thinking tokens burned per task
20K22K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

FeatureGrok 4.5Grok 4.3
Official API Model ID (SDK)grok-4-5-latestgrok-4-3-latest
API ProtocolxAI REST API (OpenAI-Compatible)xAI REST API (OpenAI-Compatible)
Release Date2026-072026-04
Knowledge CutoffMarch 2026January 2026
ArchitectureAdvanced Sparse MoEColossus-Scale Mixture-of-Experts
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)5,000 – 15,000 RPM (Production Tier) · 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 ($2/1M in) across full 500K windowFlat rate ($1.25/1M in) across full 1000K window
Prompt Cache Policy & TTLCached input at $0.5/1M tokensCached input at $0.31/1M tokens
Computer Use / GUI AgentAPI / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
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 & SandboxReal-time Web & X (Twitter) Search Grounding + Code ExecutionReal-time Web & X (Twitter) Search Grounding + Code Execution
Official Fine-Tuning SupportAPI Prompt Engineering & RAG DistillationAPI Prompt Engineering & RAG 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 ClassificationxAI Production Safety & Alignment VerifiedxAI 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, visiontext, vision
Output Modalitiestexttext

Grok 4.5 vs Grok 4.3: Schnelle Antworten (FAQ)

Which is more intelligent, Grok 4.5 or Grok 4.3?

Grok 4.5 is more intelligent. It scores 75 compared with 71 on the composite Intelligence Index.

Which model is faster?

Grok 4.5 is faster, generating 130 tokens per second compared with 110 tokens per second.

Which model is cheaper to run?

Grok 4.3 is cheaper at $0.72 per 1M tokens blended, compared with $1.35 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Grok 4.5 has lower latency, with a time to first token of 0.50s compared with 0.65s.

Which model has a larger context window?

Grok 4.3 has a larger context window, supporting 1M tokens compared with 500K tokens.

Fazit

Sowohl Grok 4.5 (Optimal für tiefgehendes logisches Denken, komplexe Mathematik und wissenschaftliche Analysen, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Grok 4.3 (Optimal für anspruchsvolles Enterprise-Reasoning, Softwareentwicklung und produktive KI-Workflows, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Grok 4.5 führt bei komplexem Reasoning und Coding, Grok 4.5 liefert den höheren Token-Durchsatz und Grok 4.3 ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Grok 4.5 oder Grok 4.3?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Grok 4.5 vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Grok 4.5 und Grok 4.3?+

Grok 4.5 kostet $2.00 / $6.00 pro 1M Input-/Output-Tokens, Grok 4.3 liegt bei $1.25 / $2.50. Beim gewichteten Gesamtpreis ist Grok 4.3 günstiger.

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

Grok 4.5 bietet 500K Tokens Kontext gegenüber 1M Tokens bei Grok 4.3. Bei der Generierungsgeschwindigkeit liegt Grok 4.5 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 xAI und xAI, Systemkarten und unabhängige Benchmark-Auswertungen (2026).