OpenAI o1 vs o3
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
OpenAI o1 by OpenAI and o3 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 holds a notable pricing advantage for token ingestion. On output generation, o3 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 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.
Switch & Compare Any Models
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
OpenAI o1
Reasoning MastermindOpenAI’s premier thinking model trained with large-scale reinforcement learning to deliberate through complex science, math, and software engineering questions.
$15.00 / 1M
$60.00 / 1M
200K tokens
48.9%
o3
Deliberate ReasonerThe o-series flagship reasoner — strong on difficult maths, coding and multi-step analysis.
$2.00 / 1M
$8.00 / 1M
200K tokens
69.1%
Key Differences at a Glance
- o3 is 8.0x cheaper for API usage (input + output tokens combined).
- o3 is faster at 75 tok/s vs 60 tok/s.
- o3 leads on SWE-bench coding (69.1% vs 48.9%).
| Feature | OpenAI o1 | o3 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 55 | 56 |
Provider | OpenAI | OpenAI |
Input Price Cost per million input tokens | $15.00 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $60.00 / 1M | $8.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $14.25 / 1M | $1.55 / 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 | 60 tok/s | 75 tok/s |
SWE-bench Verified Real-world software engineering | 48.9% | 69.1% |
GPQA Diamond Graduate-level reasoning | 77.3% | 76.5% |
Cached Input Price Prompt caching discount per 1M tokens | $7.50 / 1M | $0.50 / 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.
OpenAI o1 · Strengths & Key Advantages
- Phenomenal GPQA Diamond (77.3%) and AIME math performance
- Extended internal chain-of-thought auto-corrects subtle logical errors before responding
- 200K context window with up to 100K output tokens
Considerations & Trade-offs
- Significant latency before initial token output due to reasoning deliberation
- Higher pricing tier ($15/$60 per 1M tokens) suitable for deep reasoning over casual chat
o3 · Strengths & Key Advantages
- Powerful deliberate reasoning with vision support
- Significantly cheaper than o1-pro for comparable accuracy
- Adjustable reasoning effort to trade cost against quality
- Strong at tool-augmented agentic coding loops
Considerations & Trade-offs
- Outperformed by GPT-5 on reasoning and coding
- Thinking tokens inflate the effective cost per task
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | OpenAI o1 | o3 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.20 | $1.35 |
Output Tokens / Task Tokens generated per task | 24K | 48K |
Reasoning Tokens / Task Thinking tokens burned per task | 15K | 26K |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | OpenAI o1 | o3 |
|---|---|---|
| Official API Model ID (SDK) | o1 | o3 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2024-12 | 2025-04 |
| Knowledge Cutoff | October 2023 | June 2024 |
| Architecture | Chain-of-Thought Reasoning Foundation | Reasoning transformer with adjustable thinking effort |
| 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 ($2/1M in) across full 200K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $7.5/1M (no extra cache-write fee) | Automatic prefix caching at $0.5/1M (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 |
OpenAI o1 vs o3: Quick Answers
Which is more intelligent, OpenAI o1 or o3?
o3 is more intelligent. It scores 56 compared with 55 on the composite Intelligence Index.
Which model is faster?
o3 is faster, generating 75 tokens per second compared with 60 tokens per second.
Which model is cheaper to run?
o3 is cheaper at $1.55 per 1M tokens blended, compared with $14.25 (7:2:1 cache:input:output ratio).
Which model has lower latency?
o3 has lower latency, with a time to first token of 2.80s compared with 3.20s.
Verdict
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 o3 if your workflow prioritizes Hard maths, science and agentic coding where accuracy beats latency. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Frequently asked questions
How does OpenAI o1 compare to o3 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 o3 costs $2.00 input and $8.00 output. Overall, o3 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 o3?+
Both OpenAI o1 and o3 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: OpenAI o1 or o3?+
OpenAI o1 scores 48.9% on SWE-bench Verified while o3 scores 69.1%. For autonomous software engineering and multi-file debugging, o3 holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between OpenAI o1 and o3?+
OpenAI o1 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, o3 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between OpenAI o1 and o3?+
OpenAI o1 generates approximately 60 tokens per second, while o3 delivers 75 tokens per second. In latency-sensitive production environments, o3 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining OpenAI o1 and o3 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to o3 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.
Related reading
- Command A Reasoning vs o3: Benchmarks, Pricing & Speed ComparisonCompare
- DeepSeek R1 vs OpenAI o1: Benchmarks, Pricing & Speed ComparisonCompare
- Grok 4.20 (Reasoning) vs o3: Benchmarks, Pricing & Speed ComparisonCompare
- OpenAI o1 vs Claude 3.7 Sonnet: Benchmarks, Pricing & Speed ComparisonCompare
- OpenAI o1 vs o1-mini: Benchmarks, Pricing & Speed ComparisonCompare
- o3 vs Claude Opus 4.5: Benchmarks, Pricing & Speed ComparisonCompare
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