Head-to-Head Model Comparison

GPT-5.1 Chat vs Gemini 3 Pro

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien

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GPT-5.1 ChatProfile

Gemini 3 ProProfile

Multi-Model Arena Stack:

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Popular Matchups:

OpenAI

GPT-5.1 Chat

Frontier-Flaggschiff-Modell

GPT-5.1 Chat von OpenAI ist ein KI-Modell für anspruchsvolles Enterprise-Reasoning, Softwareentwicklung und produktive KI-Workflows mit 128K Kontextfenster, 132 Token/s Durchsatz, einem Intelligenz-Index von 60/100 und kostenlosem Open-Weig

Intelligence Index60 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

74%

Google

Gemini 3 Pro

Frontier-Flaggschiff-Modell

Gemini 3 Pro von Google ist ein KI-Modell für anspruchsvolles Enterprise-Reasoning, Softwareentwicklung und produktive KI-Workflows mit 2M Kontextfenster, 118 Token/s Durchsatz, einem Intelligenz-Index von 62/100 und $2/$12 pro 1M Token.

Intelligence Index62 / 100

Input Token:

$2.00 / 1M

Output Token:

$12.00 / 1M

Context Window:

2M tokens

SWE-bench Verified:

76.2%

Wichtigste Unterschiede auf einen Blick

GPT-5.1 Chat is 1.0x cheaper for API usage (input + output tokens combined).
Gemini 3 Pro has a larger context window (2M vs 128K tokens).
GPT-5.1 Chat is faster at 132 tok/s vs 118 tok/s.
Gemini 3 Pro leads on SWE-bench coding (76.2% vs 74.0%).

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

Feature	GPT-5.1 Chat	Gemini 3 Pro
Intelligence Index Composite reasoning & knowledge score (0-100)	60	62
Provider	OpenAI	Google
Input Price Cost per million input tokens	$0.00 / 1M	$2.00 / 1M
Output Price Cost per million output tokens	$0.00 / 1M	$12.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.00 / 1M	$1.74 / 1M
Context Window Maximum input + output tokens	128K tokens~192 A4 pages of text	2M tokens~3,000 A4 pages of text
Max Output Tokens Per single request	16K	66K
Throughput Speed Tokens generated per second	132 tok/s	118 tok/s
SWE-bench Verified Real-world software engineering	74%	76.2%
GPQA Diamond Graduate-level reasoning	71.8%	73.5%
Cached Input Price Prompt caching discount per 1M tokens	$0.00 / 1M	$0.20 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework

Coding

Gemini 3 Pro

+2.2% SWE-bench lead

Cost

GPT-5.1 Chat

1.0x cheaper monthly runtime

Speed

GPT-5.1 Chat

+12% faster generation

Context

Gemini 3 Pro

2M tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

GPT-5.1 ChatGemini 3 Pro

SWE-bench Verified (Autonomous Coding)

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

74%vs76.2%

GPQA Diamond (Doctorate-Level Science & Math)

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

71.8%vs73.5%

MMLU-Pro (Multi-task Language Understanding)

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

86%vs86.8%

LMSYS Chatbot Arena Elo

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

1367ptsvs1384pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

12%vs12.9%

Terminal-Bench (Agentic Coding & Terminal Use)

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

46%vs47.9%

SciCode (Scientific Computing)

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

43%vs44.8%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

71.5%vs73%

AIME (Advanced Math Competition)

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

91%vs92.8%

Long-Context Reasoning

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

77.8%vs84.6%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

132 tok/s vs118 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-5.1 Chat1.18s

Gemini 3 Pro1.80s

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

GPT-5.1 Chat4.97s

Gemini 3 Pro6.04s

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-5.1 Chat Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Gemini 3 Pro Monthly API Bill

$14.10 / month

$2/1M in · $12/1M out

GPT-5.1 Chat saves approximately $14.10 / month (100%) under this workload.1.0x Savings

GPT-5.1 Chat · Strengths & Key Advantages

128K Kontextfenster und bis zu 16K maximale Output-Token (SWE-bench Verified 74% · GPQA Diamond 71.8%)
Hohe Generierungsgeschwindigkeit von 132 Token/Sekunde bei nur 1180 ms Erstlatenz (TTFT)
Effiziente API-Struktur (kostenlosem Open-Weights-Zugang) 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

Gemini 3 Pro · Strengths & Key Advantages

2M Kontextfenster und bis zu 66K maximale Output-Token (SWE-bench Verified 76.2% · GPQA Diamond 73.5%)
Hohe Generierungsgeschwindigkeit von 118 Token/Sekunde bei nur 1800 ms Erstlatenz (TTFT)
Effiziente API-Struktur ($2/$12 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-5.1 Chat	Gemini 3 Pro
Cost per Task Estimated USD to finish one task	$1.48	$2.40
Output Tokens / Task Tokens generated per task	52K	60K
Reasoning Tokens / Task Thinking tokens burned per task	28K	31K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

Feature	GPT-5.1 Chat	Gemini 3 Pro
Official API Model ID (SDK)	gpt-5-1-chat	models/gemini-3-pro
API Protocol	OpenAI Responses API & Chat Completions v1	Google Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date	2025-11	2025-11
Knowledge Cutoff	October 2024	January 2025
Architecture	Chat-tuned unified reasoning model	Sparse Multimodal MoE
Parameters	Confidential	Confidential
Enterprise Rate Limits (RPM / TPM)	10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM	10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)	Free across full context window (only KV-cache VRAM scales)	Standard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTL	Automatic prefix KV-caching in vLLM / SGLang (0 cost)	Implicit + Explicit Context Caching at $0.2/1M with configurable 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	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	Built-in Google Search Grounding + Managed Python Code Execution Sandbox
Official Fine-Tuning Support	Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)	Vertex AI Supervised Adapter Tuning & Context Distillation
Multimodal Token Billing	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec)
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	Google DeepMind Frontier Safety Framework Verified
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	text, vision, audio, video
Output Modalities	text	text

GPT-5.1 Chat vs Gemini 3 Pro: Schnelle Antworten (FAQ)

Which is more intelligent, GPT-5.1 Chat or Gemini 3 Pro?

Gemini 3 Pro is more intelligent. It scores 62 compared with 60 on the composite Intelligence Index.

Which model is faster?

GPT-5.1 Chat is faster, generating 132 tokens per second compared with 118 tokens per second.

Which model is cheaper to run?

GPT-5.1 Chat is cheaper at $0.00 per 1M tokens blended, compared with $1.74 (7:2:1 cache:input:output ratio).

Which model has lower latency?

GPT-5.1 Chat has lower latency, with a time to first token of 1.18s compared with 1.80s.

Which model has a larger context window?

Gemini 3 Pro has a larger context window, supporting 2M tokens compared with 128K tokens.

Fazit

Sowohl GPT-5.1 Chat (Optimal für anspruchsvolles Enterprise-Reasoning, Softwareentwicklung und produktive KI-Workflows, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Gemini 3 Pro (Optimal für anspruchsvolles Enterprise-Reasoning, Softwareentwicklung und produktive KI-Workflows, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Gemini 3 Pro führt bei komplexem Reasoning und Coding, GPT-5.1 Chat liefert den höheren Token-Durchsatz und GPT-5.1 Chat ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – GPT-5.1 Chat oder Gemini 3 Pro?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Gemini 3 Pro vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von GPT-5.1 Chat und Gemini 3 Pro?+

GPT-5.1 Chat kostet $0.00 / $0.00 pro 1M Input-/Output-Tokens, Gemini 3 Pro liegt bei $2.00 / $12.00. Beim gewichteten Gesamtpreis ist GPT-5.1 Chat günstiger.

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

GPT-5.1 Chat bietet 128K Tokens Kontext gegenüber 2M Tokens bei Gemini 3 Pro. Bei der Generierungsgeschwindigkeit liegt GPT-5.1 Chat 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-5.1 Chat vs Gemini 3 Pro

Beliebige Modelle wechseln & vergleichen

GPT-5.1 Chat

Gemini 3 Pro

Wer gewinnt in welcher Disziplin?

Geprüfte Benchmark-Ergebnisse im Vergleich

Latenz & Antwortzeit

Interaktiver Token-Kosten-Simulator

GPT-5.1 Chat · Strengths & Key Advantages

Considerations & Trade-offs

Gemini 3 Pro · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Technische Spezifikationen

GPT-5.1 Chat vs Gemini 3 Pro: Schnelle Antworten (FAQ)

Fazit

Häufig gestellte Fragen (FAQ)

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