GetInfoUs
Head-to-Head Model Comparison

Gemini 2.0 Flash vs GPT-4o mini

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
Gemini 2.0 Flash vs GPT-4o mini: Benchmarks, Pricing & Speed Comparison

Gemini 2.0 Flash von Google und GPT-4o mini von OpenAI gehören zu den führenden KI-Modellen. Gemini 2.0 Flash bietet ein Kontextfenster von 1.05M Tokens bei API-Preisen von $0.10/$0.40 pro 1M Tokens, während GPT-4o mini 128K Tokens Kontext zu $0.15/$0.60 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:
Google

Gemini 2.0 Flash

Ultraschnelle Echtzeit-Inferenz

Gemini 2.0 Flash von Google ist ein KI-Modell für ultraschnelle Echtzeit-Antworten, niedrige Latenz und Hochdurchsatz-Routing mit 1.0M Kontextfenster, 240 Token/s Durchsatz, einem Intelligenz-Index von 40/100 und $0.1/$0.4 pro 1M Token.

Intelligence Index40 / 100
Input Token:

$0.10 / 1M

Output Token:

$0.40 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

42%

OpenAI

GPT-4o mini

Kompaktes Effizienz-Modell

GPT-4o mini von OpenAI ist ein KI-Modell für maximale Kosteneffizienz pro Token und ressourcenschonende Produktions-Workloads mit 128K Kontextfenster, 140 Token/s Durchsatz, einem Intelligenz-Index von 30/100 und $0.15/$0.6 pro 1M Token.

Intelligence Index30 / 100
Input Token:

$0.15 / 1M

Output Token:

$0.60 / 1M

Context Window:

128K tokens

SWE-bench Verified:

24.5%

Wichtigste Unterschiede auf einen Blick

  • Gemini 2.0 Flash is 1.6x cheaper for API usage (input + output tokens combined).
  • Gemini 2.0 Flash has a larger context window (1M vs 128K tokens).
  • Gemini 2.0 Flash is faster at 240 tok/s vs 140 tok/s.
  • Gemini 2.0 Flash leads on SWE-bench coding (42.0% vs 24.5%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGemini 2.0 FlashGPT-4o mini
Intelligence Index
Composite reasoning & knowledge score (0-100)
4030
Provider
GoogleOpenAI
Input Price
Cost per million input tokens
$0.10 / 1M$0.15 / 1M
Output Price
Cost per million output tokens
$0.40 / 1M$0.60 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.08 / 1M$0.14 / 1M
Context Window
Maximum input + output tokens
1.05M tokens~1,573 A4 pages of text128K tokens~192 A4 pages of text
Max Output Tokens
Per single request
8K16K
Throughput Speed
Tokens generated per second
240 tok/s140 tok/s
SWE-bench Verified
Real-world software engineering
42%24.5%
GPQA Diamond
Graduate-level reasoning
55.4%40.2%
Cached Input Price
Prompt caching discount per 1M tokens
$0.03 / 1M$0.07 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework
Coding
Gemini 2.0 Flash
+17.5% SWE-bench lead
Cost
Gemini 2.0 Flash
1.6x cheaper monthly runtime
Speed
Gemini 2.0 Flash
+71% faster generation
Context
Gemini 2.0 Flash
1.05M tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Gemini 2.0 FlashGPT-4o mini
SWE-bench Verified (Autonomous Coding)

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

42%vs24.5%
GPQA Diamond (Doctorate-Level Science & Math)

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

55.4%vs40.2%
MMLU-Pro (Multi-task Language Understanding)

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

71%vs58.5%
LMSYS Chatbot Arena Elo

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

1302ptsvs1228pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

8.9%vs6.4%
Terminal-Bench (Agentic Coding & Terminal Use)

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

27.7%vs16.2%
SciCode (Scientific Computing)

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

25.2%vs14.7%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

44.9%vs28.3%
AIME (Advanced Math Competition)

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

63.7%vs46.2%
Long-Context Reasoning

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

63.9%vs52.7%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

240 tok/s vs140 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 2.0 Flash0.28s
GPT-4o mini0.31s
Est. End-to-End Response (500 tokens)TTFT + generation time
Gemini 2.0 Flash2.36s
GPT-4o mini3.88s

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)
Gemini 2.0 Flash Monthly API Bill
$0.55 / month
$0.1/1M in · $0.4/1M out
GPT-4o mini Monthly API Bill
$0.88 / month
$0.15/1M in · $0.6/1M out
Gemini 2.0 Flash saves approximately $0.33 / month (38%) under this workload.1.6x Savings

Gemini 2.0 Flash · Strengths & Key Advantages

  • 1.0M Kontextfenster und bis zu 8K maximale Output-Token (SWE-bench Verified 42% · GPQA Diamond 55.4%)
  • Hohe Generierungsgeschwindigkeit von 240 Token/Sekunde bei nur 280 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($0.1/$0.4 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

GPT-4o mini · Strengths & Key Advantages

  • 128K Kontextfenster und bis zu 16K maximale Output-Token (SWE-bench Verified 24.5% · GPQA Diamond 40.2%)
  • Hohe Generierungsgeschwindigkeit von 140 Token/Sekunde bei nur 310 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.

MetricGemini 2.0 FlashGPT-4o mini
Cost per Task
Estimated USD to finish one task
$0.13$0.16
Output Tokens / Task
Tokens generated per task
60K45K
Reasoning Tokens / Task
Thinking tokens burned per task
33K0K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

FeatureGemini 2.0 FlashGPT-4o mini
Official API Model ID (SDK)models/gemini-2-flashgpt-4o-mini
API ProtocolGoogle Gemini API (generateContent) + OpenAI-Compatible SDKOpenAI Responses API & Chat Completions v1
Release Date2024-122024-07
Knowledge CutoffAugust 2024October 2023
ArchitectureMultimodal Flash MoESmall Multimodal Dense
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)Standard rate up to 200K tokens; 2x input/output tier above 200K tokensFlat rate ($0.15/1M in) across full 128K window
Prompt Cache Policy & TTLImplicit + Explicit Context Caching at $0.025/1M with configurable TTLAutomatic prefix caching at $0.075/1M (no extra cache-write fee)
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)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 & SandboxBuilt-in Google Search Grounding + Managed Python Code Execution SandboxNative Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning SupportVertex AI Supervised Adapter Tuning & Context 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) · 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 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 ClassificationGoogle DeepMind Frontier Safety Framework 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, vision, audio, videotext, vision
Output Modalitiestexttext

Gemini 2.0 Flash vs GPT-4o mini: Schnelle Antworten (FAQ)

Which is more intelligent, Gemini 2.0 Flash or GPT-4o mini?

Gemini 2.0 Flash is more intelligent. It scores 40 compared with 30 on the composite Intelligence Index.

Which model is faster?

Gemini 2.0 Flash is faster, generating 240 tokens per second compared with 140 tokens per second.

Which model is cheaper to run?

Gemini 2.0 Flash is cheaper at $0.08 per 1M tokens blended, compared with $0.14 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Gemini 2.0 Flash has lower latency, with a time to first token of 0.28s compared with 0.31s.

Which model has a larger context window?

Gemini 2.0 Flash has a larger context window, supporting 1.05M tokens compared with 128K tokens.

Fazit

Sowohl Gemini 2.0 Flash (Optimal für ultraschnelle Echtzeit-Antworten, niedrige Latenz und Hochdurchsatz-Routing, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch GPT-4o mini (Optimal für maximale Kosteneffizienz pro Token und ressourcenschonende Produktions-Workloads, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Gemini 2.0 Flash führt bei komplexem Reasoning und Coding, Gemini 2.0 Flash liefert den höheren Token-Durchsatz und Gemini 2.0 Flash ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Gemini 2.0 Flash oder GPT-4o mini?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Gemini 2.0 Flash vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Gemini 2.0 Flash und GPT-4o mini?+

Gemini 2.0 Flash kostet $0.10 / $0.40 pro 1M Input-/Output-Tokens, GPT-4o mini liegt bei $0.15 / $0.60. Beim gewichteten Gesamtpreis ist Gemini 2.0 Flash günstiger.

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

Gemini 2.0 Flash bietet 1.05M Tokens Kontext gegenüber 128K Tokens bei GPT-4o mini. Bei der Generierungsgeschwindigkeit liegt Gemini 2.0 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 & 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 Google und OpenAI, Systemkarten und unabhängige Benchmark-Auswertungen (2026).