Head-to-Head Model Comparison

o4-mini vs Gemini 3.8 Flash

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Verificato dal team di ricerca di GetInfoUsUltima verifica: October 2026Politica editoriale

Cambia e Confronta Qualsiasi Modello

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o4-miniProfile

Gemini 3.8 FlashProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

OpenAI

o4-mini

Motore di Ragionamento Profondo

o4-mini di OpenAI è un modello di IA progettato per il ragionamento logico profondo, la matematica avanzata e l’analisi scientifica, con finestra di contesto da 200K, velocità di 105 token/s, indice di intelligenza di 50/100 e tariffa di $1

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

Inferenza Ultra-Rapida

Gemini 3.8 Flash di Google è un modello di IA progettato per le interazioni in tempo reale a bassissima latenza e il routing ad alto volume, con finestra di contesto da 1.0M, velocità di 175 token/s, indice di intelligenza di 87/100 e tarif

Intelligence Index87 / 100

Input Token:

$0.75 / 1M

Output Token:

$3.75 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

74.8%

Differenze Chiave a Colpo d’Occhio

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

Chi vince in ogni categoria?

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

Punteggi Benchmark Verificati

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

Latenza e Tempo di Risposta

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

Simulatore Interattivo dei Costi Token

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

Finestra di contesto da 200K con capacità massima di output fino a 100K token (SWE-bench Verified 57.5% · GPQA Diamond 73%)
Throughput elevato di 105 token/secondo con latenza al primo token (TTFT) di soli 1800 ms
Economia API competitiva ($1.1/$4.4 per 1M di token) con supporto per prompt caching, tool calling e output JSON strutturati

Considerations & Trade-offs

Modello cloud proprietario chiuso; non consente il download locale dei pesi per l’esecuzione offline
Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

Gemini 3.8 Flash · Strengths & Key Advantages

Finestra di contesto da 1.0M con capacità massima di output fino a 66K token (SWE-bench Verified 74.8% · GPQA Diamond 84%)
Throughput elevato di 175 token/secondo con latenza al primo token (TTFT) di soli 390 ms
Economia API competitiva ($0.75/$3.75 per 1M di token) con supporto per prompt caching, tool calling e output JSON strutturati

Considerations & Trade-offs

Modello cloud proprietario chiuso; non consente il download locale dei pesi per l’esecuzione offline
Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

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

Specifiche Tecniche

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: Risposte Rapide (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.

Verdetto

Sia o4-mini (Ideale per il ragionamento logico profondo, la matematica avanzata e l’analisi scientifica, sistemi RAG aziendali e integrazioni API scalabili.) sia Gemini 3.8 Flash (Ideale per le interazioni in tempo reale a bassissima latenza e il routing ad alto volume, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: Gemini 3.8 Flash guida nei benchmark di ragionamento e programmazione, Gemini 3.8 Flash garantisce una generazione più rapida e Gemini 3.8 Flash offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, o4-mini o Gemini 3.8 Flash?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Gemini 3.8 Flash. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra o4-mini e Gemini 3.8 Flash?+

o4-mini costa $1.10 / $4.40 per milione di token di input/output, mentre Gemini 3.8 Flash costa $0.75 / $3.75. Nel costo combinato, Gemini 3.8 Flash risulta più economico.

Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+

o4-mini supporta 200K token di contesto contro 1.05M token di Gemini 3.8 Flash, mentre Gemini 3.8 Flash offre una velocità di generazione superiore.

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

Cambia e Confronta Qualsiasi Modello

o4-mini

Gemini 3.8 Flash

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

o4-mini · Strengths & Key Advantages

Considerations & Trade-offs

Gemini 3.8 Flash · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

o4-mini vs Gemini 3.8 Flash: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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