o4-mini-deep-research vs Gemini Deep Research
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
o4-mini-deep-research by OpenAI and Gemini Deep Research by Google represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, o4-mini-deep-research holds a notable pricing advantage for token ingestion. On output generation, o4-mini-deep-research is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Gemini Deep Research provides an expanded memory horizon with 1,049K tokens. In latency-sensitive production environments, o4-mini-deep-research leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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o4-mini-deep-research
Модель глубокого рассужденияo4-mini-deep-research от OpenAI — передовая модель искусственного интеллекта с контекстным окном 200K токенов и стоимостью API $2/1M входящих и $8/1M исходящих токенов для программирования, аналитики и автоматизации.
$2.00 / 1M
$8.00 / 1M
200K tokens
48.5%
Gemini Deep Research
Модель глубокого рассужденияGemini Deep Research от Google — передовая модель искусственного интеллекта с контекстным окном 1.0M токенов и стоимостью API $10/1M входящих и $40/1M исходящих токенов для программирования, аналитики и автоматизации.
$10.00 / 1M
$40.00 / 1M
1.05M tokens
N/A
Ключевые отличия кратко
- 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.
| Feature | 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 |
Кто лидирует в каждой категории?
At-a-glance decision frameworkEvaluations ongoing
Результаты проверенных бенчмарков
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.
Задержка и скорость ответа
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Интерактивный калькулятор стоимости токенов
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
o4-mini-deep-research · Strengths & Key Advantages
- Современная архитектура Compact agentic reasoning transformer от OpenAI с поддержкой до 200K токенов контекста
- Оптимальная стоимость использования ($2/1M вход, $8/1M выход) и высокая скорость генерации
- Высокие результаты в задачах написания кода, логического мышления и многоязычной обработки
Considerations & Trade-offs
- При работе с максимальным контекстом требуется контроль расхода токенов
- Для узкоспециализированных задач рекомендуется точная настройка системных промптов
Gemini Deep Research · Strengths & Key Advantages
- Современная архитектура Agentic recursive research and synthesis engine от Google с поддержкой до 1.0M токенов контекста
- Оптимальная стоимость использования ($10/1M вход, $40/1M выход) и высокая скорость генерации
- Высокие результаты в задачах написания кода, логического мышления и многоязычной обработки
Considerations & Trade-offs
- При работе с максимальным контекстом требуется контроль расхода токенов
- Для узкоспециализированных задач рекомендуется точная настройка системных промптов
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 |
Технические характеристики
Architecture, release timeline, capabilities, and modalities.
| Feature | 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: Быстрые ответы (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.
Вердикт
Verdict & Model Selection Recommendation: Choose o4-mini-deep-research if your priority is High-volume research briefs where cost per run dominates. Its performance in demanding tasks justifies its operational token cost. Choose Gemini Deep Research if your workflow prioritizes Financial due diligence, competitive intelligence, and scientific literature reviews. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Часто задаваемые вопросы (FAQ)
How does o4-mini-deep-research compare to Gemini Deep Research in API token pricing and cost per token?+
o4-mini-deep-research costs $2.00 per 1M input tokens and $8.00 per 1M output tokens, whereas Gemini Deep Research costs $10.00 input and $40.00 output. Overall, o4-mini-deep-research is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: o4-mini-deep-research or Gemini Deep Research?+
o4-mini-deep-research supports a context window of 200K tokens (~150k words), while Gemini Deep Research supports 1,049K tokens (~786k words). Therefore, Gemini Deep Research can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: o4-mini-deep-research or Gemini Deep Research?+
o4-mini-deep-research scores 48.5% on SWE-bench Verified, while Gemini Deep Research scores competitively for software generation. For complex autonomous debugging and agentic engineering, o4-mini-deep-research provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between o4-mini-deep-research and Gemini Deep Research?+
o4-mini-deep-research features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Gemini Deep Research provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between o4-mini-deep-research and Gemini Deep Research?+
o4-mini-deep-research generates approximately 70 tokens per second, while Gemini Deep Research delivers 60 tokens per second. In latency-sensitive production environments, o4-mini-deep-research provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining o4-mini-deep-research and Gemini Deep Research to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to o4-mini-deep-research to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Gemini Deep Research. This tiered routing pattern cuts overall API bills by up to 70%.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
Официальные источники: GetInfoUs Independent AI Benchmarks & Official API Specs