OpenAI o1 vs o1-mini
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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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.
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OpenAI o1
Mente maestra del razonamientode OpenAI líder pensamiento modelo trained con grande-escala reinforcement learning un deliberate un través de complejo science, matemáticas, y ingeniería de software questions.
$15.00 / 1M
$60.00 / 1M
200K tokens
48.9%
o1-mini
Razonador compactoun cheap, rápido modelo de razonamiento focused en codificación, mathematics y STEM problem solving.
$1.10 / 1M
$4.40 / 1M
128K tokens
44%
Diferencias Clave de un Vistazo
- 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%).
| Característica | 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 |
¿Quién gana en cada área?
At-a-glance decision frameworkPuntuaciones de Benchmarks Verificadas
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.
Latencia y Tiempo de Respuesta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interactivo de Costos de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
OpenAI o1 · Strengths & Key Advantages
- Phenomenal GPQA Diamond (77.3%) y AIME matemáticas rendimiento
- extendido internal cadena de pensamiento auto-corrects subtle logical errors before responding
- 200K ventana de contexto con up un 100K salida tokens
Considerations & Trade-offs
- Significant latencia before initial token salida due un razonamiento deliberation
- mayor precios nivel ($15/$60 por 1M tokens) suitable para profundo razonamiento sobre casual chat
o1-mini · Strengths & Key Advantages
- razonamiento capacidad un una fracción de o1 precios
- rápido en competitivo codificación y maths problems
- largo 65K salida económico para extendido derivaciones
- Cheapest way un get genuine cadena de pensamiento razonamiento
Considerations & Trade-offs
- No visión soporte — texto solo
- Conocimiento general del mundo limitado
- Clearly weaker que o1 y o3 en hard multipaso tareas
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 |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | 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: Respuestas Rápidas (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.
Veredicto
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.
Preguntas frecuentes
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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Las puntuaciones de benchmarks, precios por token y límites de tasa evolucionan rápidamente. Verifique siempre las especificaciones actuales en la documentación oficial del proveedor.
Fuentes: GetInfoUs Independent AI Benchmarks & Official API Specs