Head-to-Head Model Comparison

Gemini 3.8 Flash vs GPT-4.1 mini

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien

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Gemini 3.8 FlashProfile

GPT-4.1 miniProfile

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Popular Matchups:

Google

Gemini 3.8 Flash

Ultraschnelle Echtzeit-Inferenz

Gemini 3.8 Flash von Google ist ein KI-Modell für ultraschnelle Echtzeit-Antworten, niedrige Latenz und Hochdurchsatz-Routing mit 1.0M Kontextfenster, 175 Token/s Durchsatz, einem Intelligenz-Index von 87/100 und $0.75/$3.75 pro 1M Token.

Intelligence Index87 / 100

Input Token:

$0.75 / 1M

Output Token:

$3.75 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

74.8%

OpenAI

GPT-4.1 mini

Kompaktes Effizienz-Modell

GPT-4.1 mini von OpenAI ist ein KI-Modell für maximale Kosteneffizienz pro Token und ressourcenschonende Produktions-Workloads mit 1.0M Kontextfenster, 165 Token/s Durchsatz, einem Intelligenz-Index von 36/100 und $0.4/$1.6 pro 1M Token.

Intelligence Index36 / 100

Input Token:

$0.40 / 1M

Output Token:

$1.60 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

36.5%

Wichtigste Unterschiede auf einen Blick

GPT-4.1 mini is 2.2x cheaper for API usage (input + output tokens combined).
Gemini 3.8 Flash has a larger context window (1M vs 1M tokens).
Gemini 3.8 Flash is faster at 175 tok/s vs 165 tok/s.
Gemini 3.8 Flash leads on SWE-bench coding (74.8% vs 36.5%).

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

Feature	Gemini 3.8 Flash	GPT-4.1 mini
Intelligence Index Composite reasoning & knowledge score (0-100)	87	36
Provider	Google	OpenAI
Input Price Cost per million input tokens	$0.75 / 1M	$0.40 / 1M
Output Price Cost per million output tokens	$3.75 / 1M	$1.60 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.66 / 1M	$0.31 / 1M
Context Window Maximum input + output tokens	1.05M tokens~1,573 A4 pages of text	1.05M tokens~1,571 A4 pages of text
Max Output Tokens Per single request	66K	33K
Throughput Speed Tokens generated per second	175 tok/s	165 tok/s
SWE-bench Verified Real-world software engineering	74.8%	36.5%
GPQA Diamond Graduate-level reasoning	84%	49.5%
Cached Input Price Prompt caching discount per 1M tokens	$0.19 / 1M	$0.10 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework

Coding

Gemini 3.8 Flash

+38.3% SWE-bench lead

Cost

GPT-4.1 mini

2.2x cheaper monthly runtime

Speed

Gemini 3.8 Flash

+6% faster generation

Context

Gemini 3.8 Flash

1.05M tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Gemini 3.8 FlashGPT-4.1 mini

SWE-bench Verified (Autonomous Coding)

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

74.8%vs36.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

84%vs49.5%

MMLU-Pro (Multi-task Language Understanding)

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

88.8%vs68%

LMSYS Chatbot Arena Elo

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

1480ptsvs1258pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs6.2%

Terminal-Bench (Agentic Coding & Terminal Use)

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

N/Avs19%

SciCode (Scientific Computing)

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

N/Avs24.5%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs38%

AIME (Advanced Math Competition)

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

89.5%vs42%

Long-Context Reasoning

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

N/Avs57.5%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

175 tok/s vs165 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

Gemini 3.8 Flash0.39s

GPT-4.1 mini0.30s

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

Gemini 3.8 Flash3.25s

GPT-4.1 mini3.33s

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)

Gemini 3.8 Flash Monthly API Bill

$4.78 / month

$0.75/1M in · $3.75/1M out

GPT-4.1 mini Monthly API Bill

$2.19 / month

$0.4/1M in · $1.6/1M out

GPT-4.1 mini saves approximately $2.59 / month (54%) under this workload.2.2x Savings

Gemini 3.8 Flash · Strengths & Key Advantages

1.0M Kontextfenster und bis zu 66K maximale Output-Token (SWE-bench Verified 74.8% · GPQA Diamond 84%)
Hohe Generierungsgeschwindigkeit von 175 Token/Sekunde bei nur 390 ms Erstlatenz (TTFT)
Effiziente API-Struktur ($0.75/$3.75 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

GPT-4.1 mini · Strengths & Key Advantages

1.0M Kontextfenster und bis zu 33K maximale Output-Token (SWE-bench Verified 36.5% · GPQA Diamond 49.5%)
Hohe Generierungsgeschwindigkeit von 165 Token/Sekunde bei nur 300 ms Erstlatenz (TTFT)
Effiziente API-Struktur ($0.4/$1.6 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	Gemini 3.8 Flash	GPT-4.1 mini
Cost per Task Estimated USD to finish one task	$1.20	$0.14
Output Tokens / Task Tokens generated per task	44K	48K
Reasoning Tokens / Task Thinking tokens burned per task	21K	0K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

Feature	Gemini 3.8 Flash	GPT-4.1 mini
Official API Model ID (SDK)	models/gemini-3-8-flash	gpt-4-1-mini
API Protocol	Google Gemini API (generateContent) + OpenAI-Compatible SDK	OpenAI Responses API & Chat Completions v1
Release Date	2026-09	2025-04
Knowledge Cutoff	June 2026	June 2024
Architecture	Late-2026 optimized sparse multimodal transformer	Dense multimodal transformer (small)
Parameters	Confidential	Confidential
Enterprise Rate Limits (RPM / TPM)	10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM	10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)	Standard rate up to 200K tokens; 2x input/output tier above 200K tokens	Flat rate ($0.4/1M in) across full 1048K window
Prompt Cache Policy & TTL	Implicit + Explicit Context Caching at $0.1875/1M with configurable TTL	Automatic prefix caching at $0.1/1M (no extra cache-write fee)
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	Built-in Google Search Grounding + Managed Python Code Execution Sandbox	Native Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning Support	Vertex AI Supervised Adapter Tuning & Context Distillation	Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
Multimodal Token Billing	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec)	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	Google DeepMind Frontier Safety Framework Verified	OpenAI Preparedness Framework Audited & System Card Published
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, audio, video	text, vision
Output Modalities	text	text

Gemini 3.8 Flash vs GPT-4.1 mini: Schnelle Antworten (FAQ)

Which is more intelligent, Gemini 3.8 Flash or GPT-4.1 mini?

Gemini 3.8 Flash is more intelligent. It scores 87 compared with 36 on the composite Intelligence Index.

Which model is faster?

Gemini 3.8 Flash is faster, generating 175 tokens per second compared with 165 tokens per second.

Which model is cheaper to run?

GPT-4.1 mini is cheaper at $0.31 per 1M tokens blended, compared with $0.66 (7:2:1 cache:input:output ratio).

Which model has lower latency?

GPT-4.1 mini has lower latency, with a time to first token of 0.30s compared with 0.39s.

Which model has a larger context window?

Gemini 3.8 Flash has a larger context window, supporting 1.05M tokens compared with 1.05M tokens.

Fazit

Sowohl Gemini 3.8 Flash (Optimal für ultraschnelle Echtzeit-Antworten, niedrige Latenz und Hochdurchsatz-Routing, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch GPT-4.1 mini (Optimal für maximale Kosteneffizienz pro Token und ressourcenschonende Produktions-Workloads, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Gemini 3.8 Flash führt bei komplexem Reasoning und Coding, Gemini 3.8 Flash liefert den höheren Token-Durchsatz und GPT-4.1 mini ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Gemini 3.8 Flash oder GPT-4.1 mini?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Gemini 3.8 Flash vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Gemini 3.8 Flash und GPT-4.1 mini?+

Gemini 3.8 Flash kostet $0.75 / $3.75 pro 1M Input-/Output-Tokens, GPT-4.1 mini liegt bei $0.40 / $1.60. Beim gewichteten Gesamtpreis ist GPT-4.1 mini günstiger.

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

Gemini 3.8 Flash bietet 1.05M Tokens Kontext gegenüber 1.05M Tokens bei GPT-4.1 mini. Bei der Generierungsgeschwindigkeit liegt Gemini 3.8 Flash 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)

Gemini 3.8 Flash vs GPT-4.1 mini

Beliebige Modelle wechseln & vergleichen

Gemini 3.8 Flash

GPT-4.1 mini

Wer gewinnt in welcher Disziplin?

Geprüfte Benchmark-Ergebnisse im Vergleich

Latenz & Antwortzeit

Interaktiver Token-Kosten-Simulator

Gemini 3.8 Flash · Strengths & Key Advantages

Considerations & Trade-offs

GPT-4.1 mini · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Technische Spezifikationen

Gemini 3.8 Flash vs GPT-4.1 mini: Schnelle Antworten (FAQ)

Fazit

Häufig gestellte Fragen (FAQ)

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