o3 vs o3-pro
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
o3 by OpenAI and o3-pro 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.
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o3
Model Penalaran Mendalamo3 dari OpenAI adalah model AI canggih dengan jendela konteks 200K token serta harga API $2/1M token input dan $8/1M token output untuk pemrograman, penalaran, dan otomasi bisnis.
$2.00 / 1M
$8.00 / 1M
200K tokens
69.1%
o3-pro
Model Penalaran Mendalamo3-pro dari OpenAI adalah model AI canggih dengan jendela konteks 200K token serta harga API $20/1M token input dan $80/1M token output untuk pemrograman, penalaran, dan otomasi bisnis.
$20.00 / 1M
$80.00 / 1M
200K tokens
71.5%
Perbedaan Utama Sekilas
- o3 is 10.0x cheaper for API usage (input + output tokens combined).
- o3 is faster at 75 tok/s vs 30 tok/s.
- o3-pro leads on SWE-bench coding (71.5% vs 69.1%).
| Feature | o3 | o3-pro |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 56 | 58 |
Provider | OpenAI | OpenAI |
Input Price Cost per million input tokens | $2.00 / 1M | $20.00 / 1M |
Output Price Cost per million output tokens | $8.00 / 1M | $80.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.55 / 1M | $15.50 / 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 | 75 tok/s | 30 tok/s |
SWE-bench Verified Real-world software engineering | 69.1% | 71.5% |
GPQA Diamond Graduate-level reasoning | 76.5% | 79.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.50 / 1M | $5.00 / 1M |
Siapa Unggul di Mana?
At-a-glance decision frameworkSkor Benchmark Terverifikasi
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.
Latensi & Waktu Respons
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulator Biaya Token Interaktif
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
o3 · Strengths & Key Advantages
- Arsitektur Reasoning transformer with adjustable thinking effort mutakhir dari OpenAI dengan dukungan konteks 200K token
- Efisiensi biaya API yang kompetitif ($2/1M input, $8/1M output) dan respons cepat
- Performa andal dalam pembuatan kode, analisis logika, serta pemahaman multibahasa
Considerations & Trade-offs
- Penggunaan konteks berukuran penuh memerlukan pengelolaan anggaran token yang cermat
- Membutuhkan perancangan prompt yang tepat untuk kasus penggunaan industri spesifik
o3-pro · Strengths & Key Advantages
- Arsitektur High-effort reasoning transformer mutakhir dari OpenAI dengan dukungan konteks 200K token
- Efisiensi biaya API yang kompetitif ($20/1M input, $80/1M output) dan respons cepat
- Performa andal dalam pembuatan kode, analisis logika, serta pemahaman multibahasa
Considerations & Trade-offs
- Penggunaan konteks berukuran penuh memerlukan pengelolaan anggaran token yang cermat
- Membutuhkan perancangan prompt yang tepat untuk kasus penggunaan industri spesifik
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | o3 | o3-pro |
|---|---|---|
Cost per Task Estimated USD to finish one task | $1.35 | $11.68 |
Output Tokens / Task Tokens generated per task | 48K | 58K |
Reasoning Tokens / Task Thinking tokens burned per task | 26K | 38K |
Spesifikasi Teknis
Architecture, release timeline, capabilities, and modalities.
| Feature | o3 | o3-pro |
|---|---|---|
| Official API Model ID (SDK) | o3 | o3-pro |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-04 | 2025-06 |
| Knowledge Cutoff | June 2024 | June 2024 |
| Architecture | Reasoning transformer with adjustable thinking effort | High-effort reasoning transformer |
| 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 ($2/1M in) across full 200K window | Flat rate ($20/1M in) across full 200K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.5/1M (no extra cache-write fee) | Automatic prefix caching at $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 |
o3 vs o3-pro: Jawaban Cepat (FAQ)
Which is more intelligent, o3 or o3-pro?
o3-pro is more intelligent. It scores 58 compared with 56 on the composite Intelligence Index.
Which model is faster?
o3 is faster, generating 75 tokens per second compared with 30 tokens per second.
Which model is cheaper to run?
o3 is cheaper at $1.55 per 1M tokens blended, compared with $15.50 (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 5.20s.
Kesimpulan
Verdict & Model Selection Recommendation: Choose o3 if your priority is Hard maths, science and agentic coding where accuracy beats latency. Its performance in demanding tasks justifies its operational token cost. Choose o3-pro if your workflow prioritizes Batch and mission-critical analysis rather than chat. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Pertanyaan Umum (FAQ)
How does o3 compare to o3-pro in API token pricing and cost per token?+
o3 costs $2.00 per 1M input tokens and $8.00 per 1M output tokens, whereas o3-pro costs $20.00 input and $80.00 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: o3 or o3-pro?+
Both o3 and o3-pro 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: o3 or o3-pro?+
o3 scores 69.1% on SWE-bench Verified while o3-pro scores 71.5%. For autonomous software engineering and multi-file debugging, o3-pro holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between o3 and o3-pro?+
o3 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, o3-pro provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between o3 and o3-pro?+
o3 generates approximately 75 tokens per second, while o3-pro delivers 30 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 o3 and o3-pro 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 o3-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.
Related reading
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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.
Sumber resmi: GetInfoUs Independent AI Benchmarks & Official API Specs