o1-pro vs o3-mini
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
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.
Changez et Comparez les Modèles
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o1-pro
Raisonneur à calcul maximalun compute-heavy o1 palier qui spends far plus pensée temps sur difficult analyse et planning.
$150.00 / 1M
$600.00 / 1M
200K tokens
51.5%
o3-mini
Raisonnement à haute efficacitéd'OpenAI spécialisé, coût-efficient modèle de raisonnement conçu pour élevé-performances STEM, mathématiques competitions, et complexe génération de code avec adjustable raisonnement effort.
$1.10 / 1M
$4.40 / 1M
200K tokens
49%
Différences Clés en un Coup d’Œil
- 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%).
| Caractéristique | 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 |
Qui gagne où ?
At-a-glance decision frameworkScores de Benchmarks Vérifiés
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.
Latence et Temps de Réponse
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulateur Interactif de Coût de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
o1-pro · Strengths & Key Advantages
- le plus élevé précision du o1 family sur hard analytical problems
- très fiable sur long, multi-étapes planning tâches
- Handles recherche-grade analytical plans sans supervision
Considerations & Trade-offs
- Extremely expensive à $150 / $600 par 1M tokens
- Lent — les réponses peuvent prendre plusieurs minutes
- o3-pro et GPT-5 Pro now deliver meilleur results pour moins
o3-mini · Strengths & Key Advantages
- monde-classe compétitif mathematics et STEM raisonnement (79.7% sur GPQA Diamond)
- Configurable raisonnement effort paramètre (faible, medium, élevé)
- massif 100K token maximum sortie génération capacité
- abordable API tarification ($1.10 entrée / $4.40 sortie par 1M tokens)
Considerations & Trade-offs
- texte-seulement modality (no natif vision ou audio traitement)
- supérieur initial latence due à internal chaîne de pensée génération
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 |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | 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: Réponses Rapides (FAQ)
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.
Questions fréquentes
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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Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: GetInfoUs Independent AI Benchmarks & Official API Specs