o4-mini-deep-research vs o3-deep-research
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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o4-mini-deep-research by OpenAI and o3-deep-research by OpenAI 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, both models provide an identical memory horizon with 200K 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
Budget ResearcherA cheaper deep-research model built on o4-mini for long searches and report drafting.
$2.00 / 1M
$8.00 / 1M
200K tokens
48.5%
o3-deep-research
Agentic ResearcherAn agentic model for long-running research, synthesis and cited report generation.
$10.00 / 1M
$40.00 / 1M
200K tokens
55%
Key Differences at a Glance
- o4-mini-deep-research is 5.0x cheaper for API usage (input + output tokens combined).
- o4-mini-deep-research is faster at 70 tok/s vs 45 tok/s.
- o3-deep-research leads on SWE-bench coding (55.0% vs 48.5%).
| Feature | o4-mini-deep-research | o3-deep-research |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 48 | 55 |
Provider | OpenAI | 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 | 200K tokens~300 A4 pages of text |
Max Output Tokens Per single request | 100K | 100K |
Throughput Speed Tokens generated per second | 70 tok/s | 45 tok/s |
SWE-bench Verified Real-world software engineering | 48.5% | 55% |
GPQA Diamond Graduate-level reasoning | 68% | 74.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.50 / 1M | $2.50 / 1M |
Who Wins Where?
At-a-glance decision frameworkEmpirical Head-to-Head Benchmark Scores
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.
Latency & Response Time
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Real-Time Interactive Token Cost Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
o4-mini-deep-research · Strengths & Key Advantages
- Roughly five times cheaper than o3-deep-research
- Handles long multi-source research runs end to end
- Good enough for routine monitoring and briefs
- Good citation discipline for the price point
Considerations & Trade-offs
- Less depth and nuance in synthesis than o3-deep-research
- More prone to missing subtle contradictions across sources
o3-deep-research · Strengths & Key Advantages
- Runs autonomous multi-step research with citations
- Synthesises large evidence sets into structured reports
- Deep integration with web search and file retrieval
- Produces well-structured, fully cited research reports
Considerations & Trade-offs
- Slow and expensive per run
- Overkill for anything that is not a genuine research task
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | o4-mini-deep-research | o3-deep-research |
|---|---|---|
Cost per Task Estimated USD to finish one task | $1.10 | $7.28 |
Output Tokens / Task Tokens generated per task | 75K | 90K |
Reasoning Tokens / Task Thinking tokens burned per task | 30K | 42K |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | o4-mini-deep-research | o3-deep-research |
|---|---|---|
| Official API Model ID (SDK) | o4-mini-deep-research | o3-deep-research |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-06 | 2025-06 |
| Knowledge Cutoff | June 2024 | June 2024 |
| Architecture | Compact agentic reasoning transformer | Agentic reasoning transformer with web and file tools |
| 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) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($2/1M in) across full 200K window | Flat rate ($10/1M in) across full 200K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.5/1M (no extra cache-write fee) | Automatic prefix caching at $2.5/1M (no extra cache-write fee) |
| 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 | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) |
| 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 | OpenAI Preparedness Framework Audited & System Card Published |
| 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 o3-deep-research: Quick Answers
Which is more intelligent, o4-mini-deep-research or o3-deep-research?
o3-deep-research is more intelligent. It scores 55 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 45 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?
o4-mini-deep-research has lower latency, with a time to first token of 2.40s compared with 3.40s.
Verdict
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 o3-deep-research if your workflow prioritizes Automated literature review, market research and cited report drafting. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Frequently asked questions
How does o4-mini-deep-research compare to o3-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 o3-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 o3-deep-research?+
Both o4-mini-deep-research and o3-deep-research offer an identical context window of 200K tokens (~150k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: o4-mini-deep-research or o3-deep-research?+
o4-mini-deep-research scores 48.5% on SWE-bench Verified while o3-deep-research scores 55%. For autonomous software engineering and multi-file debugging, o3-deep-research holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between o4-mini-deep-research and o3-deep-research?+
o4-mini-deep-research features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, o3-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 o3-deep-research?+
o4-mini-deep-research generates approximately 70 tokens per second, while o3-deep-research delivers 45 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 o3-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 o3-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.
Sources: GetInfoUs Independent AI Benchmarks & Official API Specs