Head-to-Head Model Comparison

GPT-5.4 mini vs Gemini 3.1 Flash-Lite

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien

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GPT-5.4 miniProfile

Gemini 3.1 Flash-LiteProfile

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OpenAI

GPT-5.4 mini

Kompaktes Effizienz-Modell

GPT-5.4 mini von OpenAI ist ein KI-Modell für maximale Kosteneffizienz pro Token und ressourcenschonende Produktions-Workloads mit 400K Kontextfenster, 175 Token/s Durchsatz, einem Intelligenz-Index von 59/100 und $0.5/$4 pro 1M Token.

Intelligence Index59 / 100

Input Token:

$0.50 / 1M

Output Token:

$4.00 / 1M

Context Window:

400K tokens

SWE-bench Verified:

68.5%

Google

Gemini 3.1 Flash-Lite

Kompaktes Effizienz-Modell

Gemini 3.1 Flash-Lite von Google ist ein KI-Modell für maximale Kosteneffizienz pro Token und ressourcenschonende Produktions-Workloads mit 1.0M Kontextfenster, 250 Token/s Durchsatz, einem Intelligenz-Index von 60/100 und $0.15/$0.6 pro 1M

Intelligence Index60 / 100

Input Token:

$0.15 / 1M

Output Token:

$0.60 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

N/A

Wichtigste Unterschiede auf einen Blick

Gemini 3.1 Flash-Lite is 5.4x cheaper for API usage (input + output tokens combined).
Gemini 3.1 Flash-Lite has a larger context window (1M vs 400K tokens).
Gemini 3.1 Flash-Lite is faster at 250 tok/s vs 175 tok/s.

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

Feature	GPT-5.4 mini	Gemini 3.1 Flash-Lite
Intelligence Index Composite reasoning & knowledge score (0-100)	59	60
Provider	OpenAI	Google
Input Price Cost per million input tokens	$0.50 / 1M	$0.15 / 1M
Output Price Cost per million output tokens	$4.00 / 1M	$0.60 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.53 / 1M	$0.11 / 1M
Context Window Maximum input + output tokens	400K tokens~600 A4 pages of text	1.05M tokens~1,573 A4 pages of text
Max Output Tokens Per single request	128K	33K
Throughput Speed Tokens generated per second	175 tok/s	250 tok/s
SWE-bench Verified Real-world software engineering	68.5%	N/A
GPQA Diamond Graduate-level reasoning	72.5%	N/A
Cached Input Price Prompt caching discount per 1M tokens	$0.05 / 1M	$0.04 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework

Coding

Evaluations ongoing

Cost

Gemini 3.1 Flash-Lite

5.4x cheaper monthly runtime

Speed

Gemini 3.1 Flash-Lite

+43% faster generation

Context

Gemini 3.1 Flash-Lite

1.05M tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

GPT-5.4 miniGemini 3.1 Flash-Lite

SWE-bench Verified (Autonomous Coding)

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

68.5%vsN/A

GPQA Diamond (Doctorate-Level Science & Math)

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

72.5%vsN/A

MMLU-Pro (Multi-task Language Understanding)

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

84.8%vs78.5%

LMSYS Chatbot Arena Elo

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

1375ptsvs1355pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

12.2%vsN/A

Terminal-Bench (Agentic Coding & Terminal Use)

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

41.5%vsN/A

SciCode (Scientific Computing)

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

40%vsN/A

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

67%vsN/A

AIME (Advanced Math Competition)

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

89.6%vsN/A

Long-Context Reasoning

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

78%vsN/A

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

175 tok/s vs250 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-5.4 mini0.76s

Gemini 3.1 Flash-Lite0.27s

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

GPT-5.4 mini3.62s

Gemini 3.1 Flash-Lite2.27s

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-5.4 mini Monthly API Bill

$4.42 / month

$0.5/1M in · $4/1M out

Gemini 3.1 Flash-Lite Monthly API Bill

$0.82 / month

$0.15/1M in · $0.6/1M out

Gemini 3.1 Flash-Lite saves approximately $3.61 / month (82%) under this workload.5.4x Savings

GPT-5.4 mini · Strengths & Key Advantages

400K Kontextfenster und bis zu 128K maximale Output-Token (SWE-bench Verified 68.5% · GPQA Diamond 72.5%)
Hohe Generierungsgeschwindigkeit von 175 Token/Sekunde bei nur 760 ms Erstlatenz (TTFT)
Effiziente API-Struktur ($0.5/$4 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

Gemini 3.1 Flash-Lite · Strengths & Key Advantages

1.0M Kontextfenster und bis zu 33K maximale Output-Token (MMLU-Pro 78.5% · Arena Elo 1355)
Hohe Generierungsgeschwindigkeit von 250 Token/Sekunde bei nur 270 ms Erstlatenz (TTFT)
Effiziente API-Struktur ($0.15/$0.6 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 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.

Metric	GPT-5.4 mini	Gemini 3.1 Flash-Lite
Cost per Task Estimated USD to finish one task	$0.62	$0.38
Output Tokens / Task Tokens generated per task	52K	14K
Reasoning Tokens / Task Thinking tokens burned per task	27K	5K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

Feature	GPT-5.4 mini	Gemini 3.1 Flash-Lite
Official API Model ID (SDK)	gpt-5-4-mini	models/gemini-3-1-flash-lite
API Protocol	OpenAI Responses API & Chat Completions v1	Google Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date	2026-03	2026-03
Knowledge Cutoff	August 2025	November 2025
Architecture	Compact agent-ready reasoning model	Compact efficient multimodal transformer
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)	Flat rate ($0.5/1M in) across full 400K window	Standard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTL	Automatic prefix caching at $0.05/1M (no extra cache-write fee)	Implicit + Explicit Context Caching at $0.035/1M 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 · 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	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)
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
Output Modalities	text	text

GPT-5.4 mini vs Gemini 3.1 Flash-Lite: Schnelle Antworten (FAQ)

Which is more intelligent, GPT-5.4 mini or Gemini 3.1 Flash-Lite?

Gemini 3.1 Flash-Lite is more intelligent. It scores 60 compared with 59 on the composite Intelligence Index.

Which model is faster?

Gemini 3.1 Flash-Lite is faster, generating 250 tokens per second compared with 175 tokens per second.

Which model is cheaper to run?

Gemini 3.1 Flash-Lite is cheaper at $0.11 per 1M tokens blended, compared with $0.53 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Gemini 3.1 Flash-Lite has lower latency, with a time to first token of 0.27s compared with 0.76s.

Which model has a larger context window?

Gemini 3.1 Flash-Lite has a larger context window, supporting 1.05M tokens compared with 400K tokens.

Fazit

Sowohl GPT-5.4 mini (Optimal für maximale Kosteneffizienz pro Token und ressourcenschonende Produktions-Workloads, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Gemini 3.1 Flash-Lite (Optimal für maximale Kosteneffizienz pro Token und ressourcenschonende Produktions-Workloads, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Gemini 3.1 Flash-Lite führt bei komplexem Reasoning und Coding, Gemini 3.1 Flash-Lite liefert den höheren Token-Durchsatz und Gemini 3.1 Flash-Lite ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – GPT-5.4 mini oder Gemini 3.1 Flash-Lite?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Gemini 3.1 Flash-Lite vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von GPT-5.4 mini und Gemini 3.1 Flash-Lite?+

GPT-5.4 mini kostet $0.50 / $4.00 pro 1M Input-/Output-Tokens, Gemini 3.1 Flash-Lite liegt bei $0.15 / $0.60. Beim gewichteten Gesamtpreis ist Gemini 3.1 Flash-Lite günstiger.

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

GPT-5.4 mini bietet 400K Tokens Kontext gegenüber 1.05M Tokens bei Gemini 3.1 Flash-Lite. Bei der Generierungsgeschwindigkeit liegt Gemini 3.1 Flash-Lite 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-5.4 mini vs Gemini 3.1 Flash-Lite

Beliebige Modelle wechseln & vergleichen

GPT-5.4 mini

Gemini 3.1 Flash-Lite

Wer gewinnt in welcher Disziplin?

Geprüfte Benchmark-Ergebnisse im Vergleich

Latenz & Antwortzeit

Interaktiver Token-Kosten-Simulator

GPT-5.4 mini · Strengths & Key Advantages

Considerations & Trade-offs

Gemini 3.1 Flash-Lite · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Technische Spezifikationen

GPT-5.4 mini vs Gemini 3.1 Flash-Lite: Schnelle Antworten (FAQ)

Fazit

Häufig gestellte Fragen (FAQ)

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