o4-mini-deep-research vs Gemini Deep Research
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
o4-mini-deep-research de OpenAI y Gemini Deep Research de Google compiten directamente en el ecosistema actual de IA. o4-mini-deep-research ofrece una ventana de contexto de 200K tokens a $2.00/$8.00 por millón de tokens (entrada/salida), mientras que Gemini Deep Research ofrece 1.05M tokens de contexto a $10.00/$40.00 por millón de tokens. Analizamos sus benchmarks, latencia y costos reales.
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o4-mini-deep-research
Motor de Razonamiento Profundoo4-mini-deep-research de OpenAI es un modelo de IA diseñado para razonamiento lógico profundo, matemáticas avanzadas y resolución científica paso a paso, con ventana de contexto de 200K, velocidad de 70 tokens/s, índice de inteligencia de 4
$2.00 / 1M
$8.00 / 1M
200K tokens
48.5%
Gemini Deep Research
IA Agéntica y Uso de HerramientasGemini Deep Research de Google es un modelo de IA diseñado para flujos de trabajo agénticos autónomos, llamada a herramientas y automatización compleja, con ventana de contexto de 1.0M, velocidad de 60 tokens/s, índice de inteligencia de 91
$10.00 / 1M
$40.00 / 1M
1.05M tokens
N/A
Diferencias Clave de un Vistazo
- 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.
| Característica | o4-mini-deep-research | Gemini Deep Research |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 48 | 91 |
Provider | OpenAI | |
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 |
¿Quién gana en cada área?
At-a-glance decision frameworkEvaluations ongoing
Puntuaciones de Benchmarks Verificadas
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
Latencia y Tiempo de Respuesta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interactivo de Costos de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
o4-mini-deep-research · Strengths & Key Advantages
- Ventana de contexto de 200K con hasta 100K tokens de salida máxima (SWE-bench Verified 48.5% · GPQA Diamond 68%)
- Rendimiento ágil de 70 tokens/segundo con latencia inicial (TTFT) de solo 2400 ms
- Economía de API competitiva ($2/$8 por 1M de tokens) con soporte para caché de prompts y salidas JSON estructuradas
Considerations & Trade-offs
- Modelo propietario en la nube; no permite descargar los pesos para ejecución local sin conexión
- En volúmenes masivos de tokens, se recomienda activar el caché de prompts y Batch API para optimizar costos
Gemini Deep Research · Strengths & Key Advantages
- Ventana de contexto de 1.0M con hasta 66K tokens de salida máxima (GPQA Diamond 85% · MMLU-Pro 89.5%)
- Rendimiento ágil de 60 tokens/segundo con latencia inicial (TTFT) de solo 1500 ms
- Economía de API competitiva ($10/$40 por 1M de tokens) con soporte para caché de prompts y salidas JSON estructuradas
Considerations & Trade-offs
- Modelo propietario en la nube; no permite descargar los pesos para ejecución local sin conexión
- En volúmenes masivos de tokens, se recomienda activar el caché de prompts y Batch API para optimizar costos
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 |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | 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: Respuestas Rápidas (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.
Veredicto
Tanto o4-mini-deep-research (Ideal para razonamiento lógico profundo, matemáticas avanzadas y resolución científica paso a paso, sistemas RAG empresariales y automatización mediante API.) como Gemini Deep Research (Ideal para flujos de trabajo agénticos autónomos, llamada a herramientas y automatización compleja, sistemas RAG empresariales y automatización mediante API.) destacan en producción: Gemini Deep Research lidera en inteligencia general y programación, o4-mini-deep-research ofrece mayor velocidad de generación de tokens y o4-mini-deep-research resulta más rentable para cargas de trabajo API de alto volumen.
Preguntas frecuentes
¿Cuál es mejor para programación y razonamiento, o4-mini-deep-research o Gemini Deep Research?+
En las pruebas de inteligencia compuesta y resolución de código, Gemini Deep Research obtiene la ventaja. Puedes revisar la tabla SWE-bench y GPQA arriba para comparar cada métrica.
¿Cuánto cuesta la API de o4-mini-deep-research frente a Gemini Deep Research?+
o4-mini-deep-research cuesta $2.00 / $8.00 por millón de tokens de entrada/salida, mientras que Gemini Deep Research cuesta $10.00 / $40.00. En costo combinado, o4-mini-deep-research es la opción más económica.
¿Qué modelo tiene mayor ventana de contexto y menor latencia?+
o4-mini-deep-research cuenta con 200K tokens de contexto frente a 1.05M tokens de Gemini Deep Research. En velocidad de inferencia, o4-mini-deep-research ofrece un rendimiento superior.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Las puntuaciones de benchmarks, precios por token y límites de tasa evolucionan rápidamente. Verifique siempre las especificaciones actuales en la documentación oficial del proveedor.
Fuentes: Documentación oficial de API de OpenAI y Google, tarjetas de sistema y benchmarks independientes (2026).