GetInfoUs
Head-to-Head Model Comparison

Grok 4.20 (Non-Reasoning) vs GPT-4.1

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien
Teilen
Related topics (12)
AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Grok 4.20 (Non-Reasoning) vs GPT-4.1: Benchmarks, Pricing & Speed Comparison

Grok 4.20 (Non-Reasoning) von xAI und GPT-4.1 von OpenAI gehören zu den führenden KI-Modellen. Grok 4.20 (Non-Reasoning) bietet ein Kontextfenster von 1M Tokens bei API-Preisen von $3.00/$15.00 pro 1M Tokens, während GPT-4.1 1.05M Tokens Kontext zu $2.00/$8.00 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

Beliebige Modelle wechseln & vergleichen

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

Profile
Profile
Multi-Model Arena Stack:
5-Model Matrix Workbench
Popular Matchups:
xAI

Grok 4.20 (Non-Reasoning)

Autonomes Agenten-Modell

Grok 4.20 (Non-Reasoning) von xAI ist ein KI-Modell für autonome Agenten-Workflows, zuverlässiges Tool-Calling und mehrstufige Automatisierung mit 1M Kontextfenster, 135 Token/s Durchsatz, einem Intelligenz-Index von 62/100 und $3/$15 pro 1

Intelligence Index62 / 100
Input Token:

$3.00 / 1M

Output Token:

$15.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

61.5%

OpenAI

GPT-4.1

Frontier-Flaggschiff-Modell

GPT-4.1 von OpenAI ist ein KI-Modell für anspruchsvolles Enterprise-Reasoning, Softwareentwicklung und produktive KI-Workflows mit 1.0M Kontextfenster, 110 Token/s Durchsatz, einem Intelligenz-Index von 48/100 und $2/$8 pro 1M Token.

Intelligence Index48 / 100
Input Token:

$2.00 / 1M

Output Token:

$8.00 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

54.6%

Wichtigste Unterschiede auf einen Blick

  • GPT-4.1 is 1.7x cheaper for API usage (input + output tokens combined).
  • GPT-4.1 has a larger context window (1M vs 1M tokens).
  • Grok 4.20 (Non-Reasoning) is faster at 135 tok/s vs 110 tok/s.
  • Grok 4.20 (Non-Reasoning) leads on SWE-bench coding (61.5% vs 54.6%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGrok 4.20 (Non-Reasoning)GPT-4.1
Intelligence Index
Composite reasoning & knowledge score (0-100)
6248
Provider
xAIOpenAI
Input Price
Cost per million input tokens
$3.00 / 1M$2.00 / 1M
Output Price
Cost per million output tokens
$15.00 / 1M$8.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$2.63 / 1M$1.55 / 1M
Context Window
Maximum input + output tokens
1M tokens~1,500 A4 pages of text1.05M tokens~1,571 A4 pages of text
Max Output Tokens
Per single request
66K33K
Throughput Speed
Tokens generated per second
135 tok/s110 tok/s
SWE-bench Verified
Real-world software engineering
61.5%54.6%
GPQA Diamond
Graduate-level reasoning
65.4%65%
Cached Input Price
Prompt caching discount per 1M tokens
$0.75 / 1M$0.50 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework
Coding
Grok 4.20 (Non-Reasoning)
+6.9% SWE-bench lead
Cost
GPT-4.1
1.7x cheaper monthly runtime
Speed
Grok 4.20 (Non-Reasoning)
+23% faster generation
Context
GPT-4.1
1.05M tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Grok 4.20 (Non-Reasoning)GPT-4.1
SWE-bench Verified (Autonomous Coding)

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

61.5%vs54.6%
GPQA Diamond (Doctorate-Level Science & Math)

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

65.4%vs65%
MMLU-Pro (Multi-task Language Understanding)

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

81.2%vs81%
LMSYS Chatbot Arena Elo

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

1360ptsvs1338pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

9.4%vs9.8%
Terminal-Bench (Agentic Coding & Terminal Use)

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

39.8%vs30%
SciCode (Scientific Computing)

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

36.5%vs36.5%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

60.2%vs58.5%
AIME (Advanced Math Competition)

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

76.5%vs55.5%
Long-Context Reasoning

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

85%vs70%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

135 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 4.20 (Non-Reasoning)0.42s
GPT-4.10.48s
Est. End-to-End Response (500 tokens)TTFT + generation time
Grok 4.20 (Non-Reasoning)4.12s
GPT-4.15.03s

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 4.20 (Non-Reasoning) Monthly API Bill
$19.13 / month
$3/1M in · $15/1M out
GPT-4.1 Monthly API Bill
$10.95 / month
$2/1M in · $8/1M out
GPT-4.1 saves approximately $8.18 / month (43%) under this workload.1.7x Savings

Grok 4.20 (Non-Reasoning) · Strengths & Key Advantages

  • 1M Kontextfenster und bis zu 66K maximale Output-Token (SWE-bench Verified 61.5% · GPQA Diamond 65.4%)
  • Hohe Generierungsgeschwindigkeit von 135 Token/Sekunde bei nur 420 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($3/$15 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

GPT-4.1 · Strengths & Key Advantages

  • 1.0M Kontextfenster und bis zu 33K maximale Output-Token (SWE-bench Verified 54.6% · GPQA Diamond 65%)
  • Hohe Generierungsgeschwindigkeit von 110 Token/Sekunde bei nur 480 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($2/$8 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.

MetricGrok 4.20 (Non-Reasoning)GPT-4.1
Cost per Task
Estimated USD to finish one task
$0.88$0.72
Output Tokens / Task
Tokens generated per task
35K52K
Reasoning Tokens / Task
Thinking tokens burned per task
0K0K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

FeatureGrok 4.20 (Non-Reasoning)GPT-4.1
Official API Model ID (SDK)grok-4-20-non-reasoning-latestgpt-4-1
API ProtocolxAI REST API (OpenAI-Compatible)OpenAI Responses API & Chat Completions v1
Release Date2026-032025-04
Knowledge CutoffNovember 2025June 2024
ArchitectureColossus-Scale Sparse MoEDense multimodal transformer
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)5,000 – 15,000 RPM (Production Tier) · 4,000,000 – 10,000,000 TPM10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)Flat rate ($3/1M in) across full 1000K windowFlat rate ($2/1M in) across full 1048K window
Prompt Cache Policy & TTLCached input at $0.75/1M tokensAutomatic prefix caching at $0.5/1M (no extra cache-write fee)
Computer Use / GUI AgentAPI / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)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 & SandboxReal-time Web & X (Twitter) Search Grounding + Code ExecutionNative Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning SupportAPI Prompt Engineering & RAG DistillationSupervised 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 ClassificationxAI Production Safety & Alignment VerifiedOpenAI 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

Grok 4.20 (Non-Reasoning) vs GPT-4.1: Schnelle Antworten (FAQ)

Which is more intelligent, Grok 4.20 (Non-Reasoning) or GPT-4.1?

Grok 4.20 (Non-Reasoning) is more intelligent. It scores 62 compared with 48 on the composite Intelligence Index.

Which model is faster?

Grok 4.20 (Non-Reasoning) is faster, generating 135 tokens per second compared with 110 tokens per second.

Which model is cheaper to run?

GPT-4.1 is cheaper at $1.55 per 1M tokens blended, compared with $2.63 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Grok 4.20 (Non-Reasoning) has lower latency, with a time to first token of 0.42s compared with 0.48s.

Which model has a larger context window?

GPT-4.1 has a larger context window, supporting 1.05M tokens compared with 1M tokens.

Fazit

Sowohl Grok 4.20 (Non-Reasoning) (Optimal für autonome Agenten-Workflows, zuverlässiges Tool-Calling und mehrstufige Automatisierung, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch GPT-4.1 (Optimal für anspruchsvolles Enterprise-Reasoning, Softwareentwicklung und produktive KI-Workflows, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Grok 4.20 (Non-Reasoning) führt bei komplexem Reasoning und Coding, Grok 4.20 (Non-Reasoning) liefert den höheren Token-Durchsatz und GPT-4.1 ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Grok 4.20 (Non-Reasoning) oder GPT-4.1?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Grok 4.20 (Non-Reasoning) vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Grok 4.20 (Non-Reasoning) und GPT-4.1?+

Grok 4.20 (Non-Reasoning) kostet $3.00 / $15.00 pro 1M Input-/Output-Tokens, GPT-4.1 liegt bei $2.00 / $8.00. Beim gewichteten Gesamtpreis ist GPT-4.1 günstiger.

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

Grok 4.20 (Non-Reasoning) bietet 1M Tokens Kontext gegenüber 1.05M Tokens bei GPT-4.1. Bei der Generierungsgeschwindigkeit liegt Grok 4.20 (Non-Reasoning) 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 OpenAI, Systemkarten und unabhängige Benchmark-Auswertungen (2026).