o3-deep-research vs Gemini Deep Research
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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o3-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, both models offer identical input token rates. On output generation, costs are matched. 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, Gemini 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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o3-deep-research
高度な推論モデルOpenAI が提供する o3-deep-research は、最大 200K トークンのコンテキストウィンドウを備え、入力 $10/100万トークン・出力 $40/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$10.00 / 1M
$40.00 / 1M
200K tokens
55%
Gemini Deep Research
高度な推論モデルGoogle が提供する Gemini Deep Research は、最大 1.0M トークンのコンテキストウィンドウを備え、入力 $10/100万トークン・出力 $40/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$10.00 / 1M
$40.00 / 1M
1.05M tokens
N/A
ひと目でわかる主な違い
- 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.
| Feature | o3-deep-research | Gemini Deep Research |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 55 | 91 |
Provider | OpenAI | |
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 |
分野別勝者スコアカード
At-a-glance decision frameworkEvaluations ongoing
検証済みベンチマークスコア比較
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.
レイテンシ＆応答時間
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
リアルタイムAPIトークン料金シミュレーター
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
o3-deep-research · Strengths & Key Advantages
- OpenAI の高度な Agentic reasoning transformer with web and file tools アーキテクチャと 200K トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $10/1M、出力 $40/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
Gemini Deep Research · Strengths & Key Advantages
- Google の高度な Agentic recursive research and synthesis engine アーキテクチャと 1.0M トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $10/1M、出力 $40/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
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 |
技術仕様・スペック
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: よくある質問 (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.
総合評価
Verdict & Model Selection Recommendation: Choose o3-deep-research if your priority is Automated literature review, market research and cited report drafting. 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.
よくある質問 (FAQ)
How does o3-deep-research compare to Gemini Deep Research in API token pricing and cost per token?+
Both o3-deep-research and Gemini Deep Research share matched API token pricing at $10.00 per 1M input tokens and $40.00 per 1M output tokens. Because their token economics are identical, your selection should be guided by benchmark latency, memory horizon, and reasoning capabilities rather than operational token fees. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: o3-deep-research or Gemini Deep Research?+
o3-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: o3-deep-research or Gemini Deep Research?+
o3-deep-research scores 55% on SWE-bench Verified, while Gemini Deep Research scores competitively for software generation. For complex autonomous debugging and agentic engineering, o3-deep-research provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between o3-deep-research and Gemini Deep Research?+
o3-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 o3-deep-research and Gemini Deep Research?+
o3-deep-research generates approximately 45 tokens per second, while Gemini Deep Research delivers 60 tokens per second. In latency-sensitive production environments, Gemini Deep Research provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining o3-deep-research and Gemini Deep Research to optimize costs?+
Yes! Tiered multi-model routing can optimize for latency and accuracy: you can route high-frequency conversational turns and latency-sensitive queries to Gemini Deep Research, and route deep architectural or complex reasoning queries to o3-deep-research. This balanced routing strategy maximizes system throughput and answer quality.
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.
公的ソース: GetInfoUs Independent AI Benchmarks & Official API Specs