Head-to-Head Model Comparison

o4-mini vs Gemini 3.8 Flash

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien

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o4-miniProfile

Gemini 3.8 FlashProfile

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Popular Matchups:

OpenAI

o4-mini

Deep-Reasoning-Engine

o4-mini von OpenAI ist ein KI-Modell für tiefgehendes logisches Denken, komplexe Mathematik und wissenschaftliche Analysen mit 200K Kontextfenster, 105 Token/s Durchsatz, einem Intelligenz-Index von 50/100 und $1.1/$4.4 pro 1M Token.

Intelligence Index50 / 100

Input Token:

$1.10 / 1M

Output Token:

$4.40 / 1M

Context Window:

200K tokens

SWE-bench Verified:

57.5%

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%

Wichtigste Unterschiede auf einen Blick

Gemini 3.8 Flash is 1.3x cheaper for API usage (input + output tokens combined).
Gemini 3.8 Flash has a larger context window (1M vs 200K tokens).
Gemini 3.8 Flash is faster at 175 tok/s vs 105 tok/s.
Gemini 3.8 Flash leads on SWE-bench coding (74.8% vs 57.5%).

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

Feature	o4-mini	Gemini 3.8 Flash
Intelligence Index Composite reasoning & knowledge score (0-100)	50	87
Provider	OpenAI	Google
Input Price Cost per million input tokens	$1.10 / 1M	$0.75 / 1M
Output Price Cost per million output tokens	$4.40 / 1M	$3.75 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.85 / 1M	$0.66 / 1M
Context Window Maximum input + output tokens	200K tokens~300 A4 pages of text	1.05M tokens~1,573 A4 pages of text
Max Output Tokens Per single request	100K	66K
Throughput Speed Tokens generated per second	105 tok/s	175 tok/s
SWE-bench Verified Real-world software engineering	57.5%	74.8%
GPQA Diamond Graduate-level reasoning	73%	84%
Cached Input Price Prompt caching discount per 1M tokens	$0.28 / 1M	$0.19 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework

Coding

Gemini 3.8 Flash

+17.3% SWE-bench lead

Cost

Gemini 3.8 Flash

1.3x cheaper monthly runtime

Speed

Gemini 3.8 Flash

+67% 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.

o4-miniGemini 3.8 Flash

SWE-bench Verified (Autonomous Coding)

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

57.5%vs74.8%

GPQA Diamond (Doctorate-Level Science & Math)

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

73%vs84%

MMLU-Pro (Multi-task Language Understanding)

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

83%vs88.8%

LMSYS Chatbot Arena Elo

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

1325ptsvs1480pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

11.2%vsN/A

Terminal-Bench (Agentic Coding & Terminal Use)

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

36%vsN/A

SciCode (Scientific Computing)

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

38.5%vsN/A

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

65%vsN/A

AIME (Advanced Math Competition)

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

89.5%vs89.5%

Long-Context Reasoning

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

74.5%vsN/A

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

105 tok/s vs175 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

o4-mini1.80s

Gemini 3.8 Flash0.39s

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

o4-mini6.56s

Gemini 3.8 Flash3.25s

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)

o4-mini Monthly API Bill

$6.02 / month

$1.1/1M in · $4.4/1M out

Gemini 3.8 Flash Monthly API Bill

$4.78 / month

$0.75/1M in · $3.75/1M out

Gemini 3.8 Flash saves approximately $1.24 / month (21%) under this workload.1.3x Savings

o4-mini · Strengths & Key Advantages

200K Kontextfenster und bis zu 100K maximale Output-Token (SWE-bench Verified 57.5% · GPQA Diamond 73%)
Hohe Generierungsgeschwindigkeit von 105 Token/Sekunde bei nur 1800 ms Erstlatenz (TTFT)
Effiziente API-Struktur ($1.1/$4.4 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 3.8 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

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

Metric	o4-mini	Gemini 3.8 Flash
Cost per Task Estimated USD to finish one task	$0.72	$1.20
Output Tokens / Task Tokens generated per task	45K	44K
Reasoning Tokens / Task Thinking tokens burned per task	25K	21K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

Feature	o4-mini	Gemini 3.8 Flash
Official API Model ID (SDK)	o4-mini	models/gemini-3-8-flash
API Protocol	OpenAI Responses API & Chat Completions v1	Google Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date	2025-04	2026-09
Knowledge Cutoff	June 2024	June 2026
Architecture	Compact reasoning transformer	Late-2026 optimized sparse 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 ($1.1/1M in) across full 200K window	Standard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTL	Automatic prefix caching at $0.275/1M (no extra cache-write fee)	Implicit + Explicit Context Caching at $0.1875/1M 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) · 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

o4-mini vs Gemini 3.8 Flash: Schnelle Antworten (FAQ)

Which is more intelligent, o4-mini or Gemini 3.8 Flash?

Gemini 3.8 Flash is more intelligent. It scores 87 compared with 50 on the composite Intelligence Index.

Which model is faster?

Gemini 3.8 Flash is faster, generating 175 tokens per second compared with 105 tokens per second.

Which model is cheaper to run?

Gemini 3.8 Flash is cheaper at $0.66 per 1M tokens blended, compared with $0.85 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Gemini 3.8 Flash has lower latency, with a time to first token of 0.39s compared with 1.80s.

Which model has a larger context window?

Gemini 3.8 Flash has a larger context window, supporting 1.05M tokens compared with 200K tokens.

Fazit

Sowohl o4-mini (Optimal für tiefgehendes logisches Denken, komplexe Mathematik und wissenschaftliche Analysen, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Gemini 3.8 Flash (Optimal für ultraschnelle Echtzeit-Antworten, niedrige Latenz und Hochdurchsatz-Routing, 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 Gemini 3.8 Flash ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – o4-mini oder Gemini 3.8 Flash?+

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 o4-mini und Gemini 3.8 Flash?+

o4-mini kostet $1.10 / $4.40 pro 1M Input-/Output-Tokens, Gemini 3.8 Flash liegt bei $0.75 / $3.75. Beim gewichteten Gesamtpreis ist Gemini 3.8 Flash günstiger.

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

o4-mini bietet 200K Tokens Kontext gegenüber 1.05M Tokens bei Gemini 3.8 Flash. 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)

o4-mini vs Gemini 3.8 Flash

Beliebige Modelle wechseln & vergleichen

o4-mini

Gemini 3.8 Flash

Wer gewinnt in welcher Disziplin?

Geprüfte Benchmark-Ergebnisse im Vergleich

Latenz & Antwortzeit

Interaktiver Token-Kosten-Simulator

o4-mini · Strengths & Key Advantages

Considerations & Trade-offs

Gemini 3.8 Flash · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Technische Spezifikationen

o4-mini vs Gemini 3.8 Flash: Schnelle Antworten (FAQ)

Fazit

Häufig gestellte Fragen (FAQ)

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