Head-to-Head Model Comparison

o4-mini-deep-research vs Gemini Deep Research

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Vérifié par l’équipe de recherche de GetInfoUsDernière révision : October 2026Politique éditoriale

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Select any AI models from our verified benchmark database to generate live side-by-side matrices.

o4-mini-deep-researchProfile

Gemini Deep ResearchProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

OpenAI

o4-mini-deep-research

Moteur de Raisonnement Profond

o4-mini-deep-research de OpenAI est un modèle d’IA conçu pour le raisonnement logique profond, les mathématiques formelles et l’analyse scientifique, offrant une fenêtre de contexte de 200K, une vitesse de 70 tokens/s, un indice d’intellige

Intelligence Index48 / 100

Input Token:

$2.00 / 1M

Output Token:

$8.00 / 1M

Context Window:

200K tokens

SWE-bench Verified:

48.5%

Google

Gemini Deep Research

IA Agentique et Outils Autonomes

Gemini Deep Research de Google est un modèle d’IA conçu pour les flux de travail agentiques autonomes, l’appel d’outils et l’orchestration multi-étapes, offrant une fenêtre de contexte de 1.0M, une vitesse de 60 tokens/s, un indice d’intell

Intelligence Index91 / 100

Input Token:

$10.00 / 1M

Output Token:

$40.00 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

N/A

Différences Clés en un Coup d’Œil

o4-mini-deep-research is 5.0x cheaper for API usage (input + output tokens combined).
Gemini Deep Research has a larger context window (1M vs 200K tokens).
o4-mini-deep-research is faster at 70 tok/s vs 60 tok/s.

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

Caractéristique	o4-mini-deep-research	Gemini Deep Research
Intelligence Index Composite reasoning & knowledge score (0-100)	48	91
Provider	OpenAI	Google
Input Price Cost per million input tokens	$2.00 / 1M	$10.00 / 1M
Output Price Cost per million output tokens	$8.00 / 1M	$40.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$1.55 / 1M	$7.75 / 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	70 tok/s	60 tok/s
SWE-bench Verified Real-world software engineering	48.5%	N/A
GPQA Diamond Graduate-level reasoning	68%	85%
Cached Input Price Prompt caching discount per 1M tokens	$0.50 / 1M	$2.50 / 1M

Qui gagne où ?

At-a-glance decision framework

Code

Evaluations ongoing

Coût

o4-mini-deep-research

5.0x cheaper monthly runtime

Vitesse

o4-mini-deep-research

+17% faster generation

Contexte

Gemini Deep Research

1.05M tokens window

Scores de Benchmarks Vérifiés

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

o4-mini-deep-researchGemini Deep Research

SWE-bench Verified (Autonomous Coding)

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

48.5%vsN/A

GPQA Diamond (Doctorate-Level Science & Math)

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

68%vs85%

MMLU-Pro (Multi-task Language Understanding)

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

80.5%vs89.5%

LMSYS Chatbot Arena Elo

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

1318ptsvs1495pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

11.8%vs17.5%

Terminal-Bench (Agentic Coding & Terminal Use)

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

33.5%vsN/A

SciCode (Scientific Computing)

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

35%vsN/A

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

60%vsN/A

AIME (Advanced Math Competition)

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

85%vsN/A

Long-Context Reasoning

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

76%vsN/A

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

70 tok/s vs60 tok/s

Latence et Temps de Réponse

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-mini-deep-research2.40s

Gemini Deep Research1.50s

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

o4-mini-deep-research9.54s

Gemini Deep Research9.83s

Simulateur Interactif de Coût de Tokens

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-deep-research Monthly API Bill

$10.95 / month

$2/1M in · $8/1M out

Gemini Deep Research Monthly API Bill

$54.75 / month

$10/1M in · $40/1M out

o4-mini-deep-research saves approximately $43.80 / month (80%) under this workload.5.0x Savings

o4-mini-deep-research · Strengths & Key Advantages

Fenêtre de contexte de 200K et capacité de sortie maximale de 100K tokens (SWE-bench Verified 48.5% · GPQA Diamond 68%)
Débit rapide de 70 tokens/seconde avec une latence au premier token (TTFT) de 2400 ms
Tarification API optimisée ($2/$8 par million de tokens) avec prise en charge du cache de prompts et des schémas JSON stricts

Considerations & Trade-offs

Modèle cloud propriétaire ne permettant pas le téléchargement local des poids sur serveur isolé
Pour les charges à très fort volume, l’activation du cache de prompts et de l’API Batch est recommandée

Gemini Deep Research · Strengths & Key Advantages

Fenêtre de contexte de 1.0M et capacité de sortie maximale de 66K tokens (GPQA Diamond 85% · MMLU-Pro 89.5%)
Débit rapide de 60 tokens/seconde avec une latence au premier token (TTFT) de 1500 ms
Tarification API optimisée ($10/$40 par million de tokens) avec prise en charge du cache de prompts et des schémas JSON stricts

Considerations & Trade-offs

Modèle cloud propriétaire ne permettant pas le téléchargement local des poids sur serveur isolé
Pour les charges à très fort volume, l’activation du cache de prompts et de l’API Batch est recommandée

Token Use & Task Economics

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

Metric	o4-mini-deep-research	Gemini Deep Research
Cost per Task Estimated USD to finish one task	$1.10	$8.50
Output Tokens / Task Tokens generated per task	75K	50K
Reasoning Tokens / Task Thinking tokens burned per task	30K	35K

Spécifications Techniques

Architecture, release timeline, capabilities, and modalities.

Caractéristique	o4-mini-deep-research	Gemini Deep Research
Official API Model ID (SDK)	o4-mini-deep-research	models/gemini-deep-research
API Protocol	OpenAI Responses API & Chat Completions v1	Google Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date	2025-06	2026-04
Knowledge Cutoff	June 2024	Real-time Google Search Integration
Architecture	Compact agentic reasoning transformer	Agentic recursive research and synthesis engine
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 ($2/1M 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 $0.5/1M (no extra cache-write fee)	Implicit + Explicit Context Caching at $2.5/1M 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

o4-mini-deep-research vs Gemini Deep Research: Réponses Rapides (FAQ)

Which is more intelligent, o4-mini-deep-research or Gemini Deep Research?

Gemini Deep Research is more intelligent. It scores 91 compared with 48 on the composite Intelligence Index.

Which model is faster?

o4-mini-deep-research is faster, generating 70 tokens per second compared with 60 tokens per second.

Which model is cheaper to run?

o4-mini-deep-research is cheaper at $1.55 per 1M tokens blended, compared with $7.75 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Gemini Deep Research has lower latency, with a time to first token of 1.50s compared with 2.40s.

Which model has a larger context window?

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

Verdict

o4-mini-deep-research (Conçu pour le raisonnement logique profond, les mathématiques formelles et l’analyse scientifique, l’intégration RAG et les pipelines de production évolutifs.) et Gemini Deep Research (Conçu pour les flux de travail agentiques autonomes, l’appel d’outils et l’orchestration multi-étapes, l’intégration RAG et les pipelines de production évolutifs.) répondent à des besoins complémentaires : Gemini Deep Research domine sur les benchmarks de raisonnement et de programmation, o4-mini-deep-research assure un débit supérieur et o4-mini-deep-research offre le meilleur rapport coût-efficacité en production.

Questions fréquentes

Quel modèle choisir pour le code et le raisonnement, o4-mini-deep-research ou Gemini Deep Research ?+

Sur l’indice d’intelligence composite et les tests de code, Gemini Deep Research prend l’avantage. Consultez les scores SWE-bench et GPQA ci-dessus pour plus de détails.

Quelle est la différence de prix API entre o4-mini-deep-research et Gemini Deep Research ?+

o4-mini-deep-research est facturé $2.00 / $8.00 par million de tokens (entrée/sortie) contre $10.00 / $40.00 pour Gemini Deep Research. En coût pondéré, o4-mini-deep-research est plus économique.

Quel modèle offre la plus grande fenêtre de contexte et la meilleure vitesse ?+

o4-mini-deep-research prend en charge 200K tokens contre 1.05M tokens pour Gemini Deep Research. Côté vitesse de génération, o4-mini-deep-research est le plus rapide.

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-deep-research vs Gemini Deep Research

Changez et Comparez les Modèles

o4-mini-deep-research

Gemini Deep Research

Qui gagne où ?

Scores de Benchmarks Vérifiés

Latence et Temps de Réponse

Simulateur Interactif de Coût de Tokens

o4-mini-deep-research · Strengths & Key Advantages

Considerations & Trade-offs

Gemini Deep Research · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spécifications Techniques

o4-mini-deep-research vs Gemini Deep Research: Réponses Rapides (FAQ)

Verdict

Questions fréquentes

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