OpenAI o1 vs o1-mini
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
OpenAI o1 by OpenAI and o1-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, o1-mini holds a notable pricing advantage for token ingestion. On output generation, o1-mini is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, OpenAI o1 provides an expanded memory horizon with 200K tokens. In latency-sensitive production environments, o1-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.
بدّل وقارن بين أي نموذجين
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
OpenAI o1
نموذج استدلال عميقنموذج OpenAI o1 المقدم من OpenAI يوفر نافذة سياق تصل إلى 200K رمز، بتكلفة $15 لكل مليون رمز إدخال و$60 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.
$15.00 / 1M
$60.00 / 1M
200K tokens
48.9%
o1-mini
نموذج استدلال عميقنموذج o1-mini المقدم من OpenAI يوفر نافذة سياق تصل إلى 128K رمز، بتكلفة $1.1 لكل مليون رمز إدخال و$4.4 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.
$1.10 / 1M
$4.40 / 1M
128K tokens
44%
أهم الفروقات في لمحة
- o1-mini is 13.6x cheaper for API usage (input + output tokens combined).
- OpenAI o1 has a larger context window (200K vs 128K tokens).
- o1-mini is faster at 90 tok/s vs 60 tok/s.
- OpenAI o1 leads on SWE-bench coding (48.9% vs 44.0%).
| Feature | OpenAI o1 | o1-mini |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 55 | 44 |
Provider | OpenAI | OpenAI |
Input Price Cost per million input tokens | $15.00 / 1M | $1.10 / 1M |
Output Price Cost per million output tokens | $60.00 / 1M | $4.40 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $14.25 / 1M | $1.05 / 1M |
Context Window Maximum input + output tokens | 200K tokens~300 A4 pages of text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 100K | 66K |
Throughput Speed Tokens generated per second | 60 tok/s | 90 tok/s |
SWE-bench Verified Real-world software engineering | 48.9% | 44% |
GPQA Diamond Graduate-level reasoning | 77.3% | 68.5% |
Cached Input Price Prompt caching discount per 1M tokens | $7.50 / 1M | $0.55 / 1M |
من يتفوق في كل مجال؟
At-a-glance decision frameworkنتائج الاختبارات المعيارية الموثقة
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.
زمن الاستجابة والسرعة
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
محاكي تكلفة الرموز التفاعلي
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
OpenAI o1 · Strengths & Key Advantages
- بنية Chain-of-Thought Reasoning Foundation متطورة من OpenAI مع سعة سياق تبلغ 200K رمز
- كفاءة عالية في التكلفة ($15/1M إدخال و$60/1M إخراج) وسرعة استجابة ممتازة
- أداء قوي في اختبارات البرمجة، الاستدلال المنطقي، ودعم اللغات المتعددة
Considerations & Trade-offs
- يتطلب إدارة دقيقة لاستهلاك الرموز عند استخدام نافذة السياق الكاملة
- قد يحتاج إلى ضبط دقيق للتعليمات (Prompt Engineering) في المهام التخصصية المعقدة
o1-mini · Strengths & Key Advantages
- بنية Reasoning transformer with hidden chain-of-thought متطورة من OpenAI مع سعة سياق تبلغ 128K رمز
- كفاءة عالية في التكلفة ($1.1/1M إدخال و$4.4/1M إخراج) وسرعة استجابة ممتازة
- أداء قوي في اختبارات البرمجة، الاستدلال المنطقي، ودعم اللغات المتعددة
Considerations & Trade-offs
- يتطلب إدارة دقيقة لاستهلاك الرموز عند استخدام نافذة السياق الكاملة
- قد يحتاج إلى ضبط دقيق للتعليمات (Prompt Engineering) في المهام التخصصية المعقدة
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | OpenAI o1 | o1-mini |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.20 | $0.85 |
Output Tokens / Task Tokens generated per task | 24K | 45K |
Reasoning Tokens / Task Thinking tokens burned per task | 15K | 26K |
المواصفات التقنية
Architecture, release timeline, capabilities, and modalities.
| Feature | OpenAI o1 | o1-mini |
|---|---|---|
| Official API Model ID (SDK) | o1 | o1-mini |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2024-12 | 2024-09 |
| Knowledge Cutoff | October 2023 | October 2023 |
| Architecture | Chain-of-Thought Reasoning Foundation | Reasoning transformer with hidden chain-of-thought |
| 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 ($15/1M in) across full 200K window | Flat rate ($1.1/1M in) across full 128K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $7.5/1M (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 |
OpenAI o1 vs o1-mini: إجابات سريعة (FAQ)
Which is more intelligent, OpenAI o1 or o1-mini?
OpenAI o1 is more intelligent. It scores 55 compared with 44 on the composite Intelligence Index.
Which model is faster?
o1-mini is faster, generating 90 tokens per second compared with 60 tokens per second.
Which model is cheaper to run?
o1-mini is cheaper at $1.05 per 1M tokens blended, compared with $14.25 (7:2:1 cache:input:output ratio).
Which model has lower latency?
o1-mini has lower latency, with a time to first token of 2.20s compared with 3.20s.
Which model has a larger context window?
OpenAI o1 has a larger context window, supporting 200K tokens compared with 128K tokens.
الحكم النهائي
Verdict & Model Selection Recommendation: Choose OpenAI o1 if your priority is Doctorate-level scientific research, competitive algorithmic coding, and high-stakes mathematical proofs. Its performance in demanding tasks justifies its operational token cost. Choose o1-mini if your workflow prioritizes Budget STEM and coding reasoning where cost matters. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
الأسئلة الشائعة (FAQ)
How does OpenAI o1 compare to o1-mini in API token pricing and cost per token?+
OpenAI o1 costs $15.00 per 1M input tokens and $60.00 per 1M output tokens, whereas o1-mini costs $1.10 input and $4.40 output. Overall, o1-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: OpenAI o1 or o1-mini?+
OpenAI o1 supports a context window of 200K tokens (~150k words), while o1-mini supports 128K tokens (~96k words). Therefore, OpenAI o1 can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: OpenAI o1 or o1-mini?+
OpenAI o1 scores 48.9% on SWE-bench Verified while o1-mini scores 44%. For autonomous software engineering and multi-file debugging, OpenAI o1 holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between OpenAI o1 and o1-mini?+
OpenAI o1 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, o1-mini provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between OpenAI o1 and o1-mini?+
OpenAI o1 generates approximately 60 tokens per second, while o1-mini delivers 90 tokens per second. In latency-sensitive production environments, o1-mini provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining OpenAI o1 and o1-mini to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to o1-mini to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to OpenAI o1. 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.
المصادر الرسمية: GetInfoUs Independent AI Benchmarks & Official API Specs