Head-to-Head Model Comparison

o3-deep-research vs Gemini Deep Research

Benchmarks, API Token Pricing, Context Window & Speed Comparison

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

Cambia e Confronta Qualsiasi Modello

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

o3-deep-researchProfile

Gemini Deep ResearchProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

OpenAI

o3-deep-research

Motore di Ragionamento Profondo

o3-deep-research 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 45 token/s, indice di intelligenza di 55/100 e tarif

Intelligence Index55 / 100

Input Token:

$10.00 / 1M

Output Token:

$40.00 / 1M

Context Window:

200K tokens

SWE-bench Verified:

55%

Google

Gemini Deep Research

IA Agentica e Strumenti Autonomi

Gemini Deep Research di Google è un modello di IA progettato per i workflow agentici autonomi, il tool calling e l’automazione multi-step, con finestra di contesto da 1.0M, velocità di 60 token/s, indice di intelligenza di 91/100 e tariffa

Intelligence Index91 / 100

Input Token:

$10.00 / 1M

Output Token:

$40.00 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

N/A

Differenze Chiave a Colpo d’Occhio

Both models cost the same under this workload — choose on capability, not price.
Gemini Deep Research has a larger context window (1M vs 200K tokens).
Gemini Deep Research is faster at 60 tok/s vs 45 tok/s.

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

Feature	o3-deep-research	Gemini Deep Research
Intelligence Index Composite reasoning & knowledge score (0-100)	55	91
Provider	OpenAI	Google
Input Price Cost per million input tokens	$10.00 / 1M	$10.00 / 1M
Output Price Cost per million output tokens	$40.00 / 1M	$40.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$7.75 / 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	45 tok/s	60 tok/s
SWE-bench Verified Real-world software engineering	55%	N/A
GPQA Diamond Graduate-level reasoning	74.5%	85%
Cached Input Price Prompt caching discount per 1M tokens	$2.50 / 1M	$2.50 / 1M

Chi vince in ogni categoria?

At-a-glance decision framework

Coding

Evaluations ongoing

Cost

Tied

Identical monthly runtime cost

Speed

Gemini Deep Research

+33% faster generation

Context

Gemini Deep Research

1.05M tokens window

Punteggi Benchmark Verificati

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

o3-deep-researchGemini Deep Research

SWE-bench Verified (Autonomous Coding)

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

55%vsN/A

GPQA Diamond (Doctorate-Level Science & Math)

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

74.5%vs85%

MMLU-Pro (Multi-task Language Understanding)

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

85.5%vs89.5%

LMSYS Chatbot Arena Elo

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

1336ptsvs1495pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

13.2%vs17.5%

Terminal-Bench (Agentic Coding & Terminal Use)

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

41%vsN/A

SciCode (Scientific Computing)

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

43%vsN/A

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

68%vsN/A

AIME (Advanced Math Competition)

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

87.5%vsN/A

Long-Context Reasoning

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

82%vsN/A

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

45 tok/s vs60 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

o3-deep-research3.40s

Gemini Deep Research1.50s

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

o3-deep-research14.51s

Gemini Deep Research9.83s

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)

o3-deep-research Monthly API Bill

$54.75 / month

$10/1M in · $40/1M out

Gemini Deep Research Monthly API Bill

$54.75 / month

$10/1M in · $40/1M out

These models cost the same under this workload — no savings either way.Break-even

o3-deep-research · Strengths & Key Advantages

Finestra di contesto da 200K con capacità massima di output fino a 100K token (SWE-bench Verified 55% · GPQA Diamond 74.5%)
Throughput elevato di 45 token/secondo con latenza al primo token (TTFT) di soli 3400 ms
Economia API competitiva ($10/$40 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
Per volumi di traffico elevati in produzione si consiglia di attivare il prompt caching e la Batch API

Gemini Deep Research · Strengths & Key Advantages

Finestra di contesto da 1.0M con capacità massima di output fino a 66K token (GPQA Diamond 85% · MMLU-Pro 89.5%)
Throughput elevato di 60 token/secondo con latenza al primo token (TTFT) di soli 1500 ms
Economia API competitiva ($10/$40 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
Per volumi di traffico elevati in produzione si consiglia di attivare il prompt caching e la Batch API

Token Use & Task Economics

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

Metric	o3-deep-research	Gemini Deep Research
Cost per Task Estimated USD to finish one task	$7.28	$8.50
Output Tokens / Task Tokens generated per task	90K	50K
Reasoning Tokens / Task Thinking tokens burned per task	42K	35K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

Feature	o3-deep-research	Gemini Deep Research
Official API Model ID (SDK)	o3-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	Agentic reasoning transformer with web and file tools	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 ($10/1M 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 $2.5/1M (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

o3-deep-research vs Gemini Deep Research: Risposte Rapide (FAQ)

Which is more intelligent, o3-deep-research or Gemini Deep Research?

Gemini Deep Research is more intelligent. It scores 91 compared with 55 on the composite Intelligence Index.

Which model is faster?

Gemini Deep Research is faster, generating 60 tokens per second compared with 45 tokens per second.

Which model has lower latency?

Gemini Deep Research has lower latency, with a time to first token of 1.50s compared with 3.40s.

Which model has a larger context window?

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

Verdetto

Sia o3-deep-research (Ideale per il ragionamento logico profondo, la matematica avanzata e l’analisi scientifica, sistemi RAG aziendali e integrazioni API scalabili.) sia Gemini Deep Research (Ideale per i workflow agentici autonomi, il tool calling e l’automazione multi-step, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: Gemini Deep Research guida nei benchmark di ragionamento e programmazione, Gemini Deep Research garantisce una generazione più rapida e o3-deep-research offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, o3-deep-research o Gemini Deep Research?+

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

Qual è la differenza di prezzo API tra o3-deep-research e Gemini Deep Research?+

o3-deep-research costa $10.00 / $40.00 per milione di token di input/output, mentre Gemini Deep Research costa $10.00 / $40.00. Nel costo combinato, o3-deep-research risulta più economico.

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

o3-deep-research supporta 200K token di contesto contro 1.05M token di Gemini Deep Research, mentre Gemini Deep Research 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)

o3-deep-research vs Gemini Deep Research

Cambia e Confronta Qualsiasi Modello

o3-deep-research

Gemini Deep Research

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

o3-deep-research · Strengths & Key Advantages

Considerations & Trade-offs

Gemini Deep Research · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

o3-deep-research vs Gemini Deep Research: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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