Model details
o3
OpenAI's o3 belongs to the company's o-series of reasoning-focused models, a line that was introduced with o1 on September 12, 2024 and made generally available on December 5, 2024. According to OpenAI coverage, the o-series takes a more deliberate, step-by-step approach to problem-solving compared with the broader GPT family, and o3 was previewed on December 20, 2024 during OpenAI's "12 Days of OpenAI" event as the next generation of that reasoning lineage. Its release is positioned to broaden the o-series' capabilities, with the stated goal of extending reasoning performance, capability, and safety rather than serving as a general conversational model.
In practical terms, o3 is intended for tasks that benefit from careful, structured inference rather than fast free-form generation. Sibling variants in the o3 family, such as o3-mini, have been described as cost-efficient options optimized for STEM reasoning in science, mathematics, and coding, and as supporting developer features like function calling and structured outputs. o3 sits above those lighter siblings in the lineup, aiming to carry the same reasoning-oriented design to more demanding workloads where deeper deliberation and more robust analytical responses are useful.
Quick Info
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- Vercel AI Gateway
- Model key
- openai/o3
- Release date
- Apr 16, 2025
- Last updated
- Apr 16, 2025
- Knowledge cutoff
- 2024-05
- Input modalities
- Output modalities
- Capabilities
Cost
- Input token cost
- $2.00
- Output token cost
- $8.00
Limits
- Input tokens
- 100,000 tokens
- Output tokens
- 100,000 tokens
- Context window
- 200,000 tokens
OpenCode
Model variants
Flex
Transparent token rates
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Rates are shown per one million tokens. Combined means one million input plus one million output tokens.
Latest news about o3
Abacus
OpenAI's o3 model, released April 16, 2025 as part of the o-series family, has 44 official benchmark scores tracked by evals.report across reasoning, coding, multimodal, agent, and tool-use evaluations. Notable highlights include GPQA Diamond at 81.8% accuracy, MMLU-Pro at 85.3%, MathVista at 86.8%, and FrontierMath at 18.69%, with each result carrying source-status flags. SWE-bench Verified coding performance reached 62.3% resolved. The aggregator records ARC-AGI-1 at 60.83% accuracy and the harder ARC-AGI-2 at only 6.53%, alongside an Artificial Analysis Intelligence Index of 38.4 and an Epoch Capabilities Index of 147.3. Tool-use scores include Berkeley Function Calling Leaderboard at 63.05% and τ²-bench Telecom at 58.2% pass^1. METR Task-Completion Time Horizons is listed at 119.7 minutes for a 50% time horizon, with LiveCodeBench Pro at 1010 Codeforces Elo.
Sulat also reports on AI and tech beyond the catalog.Read news.sulat.com
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