o1-pro vs o3-mini
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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o1-pro by OpenAI and o3-mini 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, o3-mini holds a notable pricing advantage for token ingestion. On output generation, o3-mini 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, o3-mini 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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o1-pro
Maximum Compute ReasonerA compute-heavy o1 tier that spends far more thinking time on difficult analysis and planning.
$150.00 / 1M
$600.00 / 1M
200K tokens
51.5%
o3-mini
High-Efficiency ReasoningOpenAI's specialized, cost-efficient reasoning model designed for high-performance STEM, math competitions, and complex code generation with adjustable reasoning effort.
$1.10 / 1M
$4.40 / 1M
200K tokens
49%
Key Differences at a Glance
- o3-mini is 127.6x cheaper for API usage (input + output tokens combined).
- o3-mini is faster at 85 tok/s vs 22 tok/s.
- o1-pro leads on SWE-bench coding (51.5% vs 49.0%).
| Feature | o1-pro | o3-mini |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 57 | 51 |
Provider | OpenAI | OpenAI |
Input Price Cost per million input tokens | $150.00 / 1M | $1.10 / 1M |
Output Price Cost per million output tokens | $600.00 / 1M | $4.40 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $116.25 / 1M | $1.05 / 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 | 22 tok/s | 85 tok/s |
SWE-bench Verified Real-world software engineering | 51.5% | 49% |
GPQA Diamond Graduate-level reasoning | 79% | 79.7% |
Cached Input Price Prompt caching discount per 1M tokens | $37.50 / 1M | $0.55 / 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.
o1-pro · Strengths & Key Advantages
- Highest accuracy of the o1 family on hard analytical problems
- Very reliable on long, multi-step planning tasks
- Handles research-grade analytical plans without supervision
Considerations & Trade-offs
- Extremely expensive at $150 / $600 per 1M tokens
- Slow — responses can take minutes
- o3-pro and GPT-5 Pro now deliver better results for less
o3-mini · Strengths & Key Advantages
- World-class competitive mathematics and STEM reasoning (79.7% on GPQA Diamond)
- Configurable reasoning effort parameter (low, medium, high)
- Massive 100K token maximum output generation capacity
- Affordable API pricing ($1.10 input / $4.40 output per 1M tokens)
Considerations & Trade-offs
- Text-only modality (no native vision or audio processing)
- Higher initial latency due to internal chain-of-thought generation
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | o1-pro | o3-mini |
|---|---|---|
Cost per Task Estimated USD to finish one task | $94.20 | $1.30 |
Output Tokens / Task Tokens generated per task | 62K | 45K |
Reasoning Tokens / Task Thinking tokens burned per task | 45K | 25K |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | o1-pro | o3-mini |
|---|---|---|
| Official API Model ID (SDK) | o1-pro | o3-mini |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-03 | 2025-01 |
| Knowledge Cutoff | October 2023 | October 2023 |
| Architecture | High-compute reasoning transformer | Chain-of-Thought Reasoning Dense |
| 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 ($150/1M in) across full 200K window | Flat rate ($1.1/1M in) across full 200K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $37.5/1M (no extra cache-write fee) | Automatic prefix caching at $0.55/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| 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) |
| 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 |
| Output Modalities | text | text |
o1-pro vs o3-mini: Quick Answers
Which is more intelligent, o1-pro or o3-mini?
o1-pro is more intelligent. It scores 57 compared with 51 on the composite Intelligence Index.
Which model is faster?
o3-mini is faster, generating 85 tokens per second compared with 22 tokens per second.
Which model is cheaper to run?
o3-mini is cheaper at $1.05 per 1M tokens blended, compared with $116.25 (7:2:1 cache:input:output ratio).
Which model has lower latency?
o3-mini has lower latency, with a time to first token of 1.40s compared with 4.20s.
Verdict
Verdict & Model Selection Recommendation: Choose o1-pro if your priority is Rare, high-stakes analytical problems where cost is irrelevant. Its performance in demanding tasks justifies its operational token cost. Choose o3-mini if your workflow prioritizes Competitive coding, mathematical research, algorithmic optimization, and backend logic. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Frequently asked questions
How does o1-pro compare to o3-mini in API token pricing and cost per token?+
o1-pro costs $150.00 per 1M input tokens and $600.00 per 1M output tokens, whereas o3-mini costs $1.10 input and $4.40 output. Overall, o3-mini 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: o1-pro or o3-mini?+
Both o1-pro and o3-mini 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: o1-pro or o3-mini?+
o1-pro scores 51.5% on SWE-bench Verified while o3-mini scores 49%. For autonomous software engineering and multi-file debugging, o1-pro holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between o1-pro and o3-mini?+
o1-pro features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, o3-mini provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between o1-pro and o3-mini?+
o1-pro generates approximately 22 tokens per second, while o3-mini delivers 85 tokens per second. In latency-sensitive production environments, o3-mini provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining o1-pro and o3-mini to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to o3-mini to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to o1-pro. 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