o3-mini Benchmark Update
Quality: 12.5/100 | Price: $1.925/M tokens | Output: 0 tok/s | MMLU: 0.791% | HumanEval: 0.717%
View sourceOpenAI
Cost-efficient reasoning model with adjustable reasoning effort. Optimised for STEM and coding tasks at a fraction of o3's inference cost.
OpenRouter
Price record: 2026-10-24. Source: openrouter.
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44.1
Quality Score
1318
Arena ELO
Undisclosed
Parameters
200K
Context
This measures the amount of verifiable public evidence we have, not how capable the model is. A missing field means it has not been verified yet, not that its value is zero.
17 of 22 public signals
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Jan 2025
Released
4/5 signals
4/4 signals
5/5 signals
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3/4 signals
Parameters
—
Training compute
Not reported
Dataset scale
Not reported
Base model
Not reported
Source-reported access: API access · Unknown confidence
Gaps we are still tracking
Benchmarks
21
Research
1
General
8
Recent launch, pricing, benchmark, and API signals linked to this model or its provider.
Quality: 12.5/100 | Price: $1.925/M tokens | Output: 0 tok/s | MMLU: 0.791% | HumanEval: 0.717%
View sourceArena-Hard-Auto official Gemini-2.5 judged score 50.0 with CI 0/0
Quality: 12.5/100 | Price: $1.925/M tokens | Output: 0 tok/s | MMLU: 0.791% | HumanEval: 0.717%
Arena-Hard-Auto official Gemini-2.5 judged score 50.0 with CI 0/0
SWE-Bench Verified resolved rate 42.4
View sourceLiveCodeBench pass@1 70.6 across 1055 tasks
View sourceQuality: 12.5/100 | Price: $1.925/M tokens | Output: 0 tok/s | MMLU: 0.791% | HumanEval: 0.717%
View sourceGPT-Rosalind surfaced a new lead for drug discovery: could restoring cholesterol transport protect neurons from degeneration? DCTN1 is a gene linked to Perry syndrome, a rare neurodegenerative disorder. In mouse-brain data, disrupting it increased expression of genes involved in
Our latest model advances bring stronger capabilities for building and migrating software in low-resource programming languages such as COBOL, Fortran, and Visual Basic. These languages power software that banks, governments, and businesses depend on. Progress here means https://t.co/Mulo5P3iCO
Quality: 12.5/100 | Price: $1.925/M tokens | Output: 0 tok/s | MMLU: 0.791% | HumanEval: 0.717%
Quality: 12.5/100 | Price: $1.925/M tokens | Output: 0 tok/s | MMLU: 0.791% | HumanEval: 0.717%
Quality: 12.5/100 | Price: $1.925/M tokens | Output: 0 tok/s | MMLU: 0.791% | HumanEval: 0.717%
Quality: 12.5/100 | Price: $1.925/M tokens | Output: 0 tok/s | MMLU: 0.791% | HumanEval: 0.717%
Quality: 12.5/100 | Price: $1.925/M tokens | Output: 0 tok/s | MMLU: 0.791% | HumanEval: 0.717%
Quality: 12.5/100 | Price: $1.925/M tokens | Output: 0 tok/s | MMLU: 0.791% | HumanEval: 0.717%
Quality: 12.5/100 | Price: $1.925/M tokens | Output: 0 tok/s | MMLU: 0.791% | HumanEval: 0.717%
Quality: 12.5/100 | Price: $1.925/M tokens | Output: 0 tok/s | MMLU: 0.791% | HumanEval: 0.717%
Quality: 12.5/100 | Price: $1.925/M tokens | Output: 0 tok/s | MMLU: 0.791% | HumanEval: 0.717%
Quality: 12.5/100 | Price: $1.925/M tokens | Output: 0 tok/s | MMLU: 0.791% | HumanEval: 0.717%
Quality: 12.5/100 | Price: $1.925/M tokens | Output: 0 tok/s | MMLU: 0.791% | HumanEval: 0.717%
Quality: 12.5/100 | Price: $1.925/M tokens | Output: 0 tok/s | MMLU: 0.791% | HumanEval: 0.717%
Quality: 12.5/100 | Price: $1.925/M tokens | Output: 0 tok/s | MMLU: 0.791% | HumanEval: 0.717%
Quality: 12.5/100 | Price: $1.925/M tokens | Output: 0 tok/s | MMLU: 0.791% | HumanEval: 0.717%
Quality: 12.5/100 | Price: $1.925/M tokens | Output: 0 tok/s | MMLU: 0.791% | HumanEval: 0.717%
Quality: 12.5/100 | Price: $1.925/M tokens | Output: 0 tok/s | MMLU: 0.791% | HumanEval: 0.717%
General-purpose manipulation requires reliable behavior across embodiments, objects, and task demands. We introduce HandsOn, a composable simulation platform and benchmark for cross-embodiment dexterous manipulation. It supports five robotic arms, six dexterous hands, and approximately 200 rigid, articulated, and deformable assets in randomized scenes. The benchmark pairs 18,000 curated human demonstrations with 66 human-verified tasks: Core-50 supports demonstration-based learning and execution tests, while Transfer-16 evaluates asset and relation transfer, skill composition, and deformable manipulation without target-task training demonstrations. Qualitative real-robot rollouts illustrate sim-to-real transfer. Across diffusion, vision-language-action, world-action, and prompted VLM-based methods, the best trained multi-task policy achieves 29.1% Core-50 success. Without benchmark finetuning, GPT-6 using move_eef for palm-and-finger control achieves 5.2% on Core-50 and 1.9% on Transfer-16 over 660 trials. Trained-policy stage diagnostics expose failures in contact establishment, action coordination, and maintaining achieved goals, with additional sequencing errors in composed tasks. HandsOn provides a shared basis for investigating manipulation generalization and the limits of reliable physical interaction.
SWE-Bench Verified resolved rate 42.4
LiveCodeBench pass@1 70.6 across 1055 tasks
Introducing GPT-5 for developers | OpenAI Skip to main content Research Products Business Developers Company Foundation (opens in a new window) Log in Try ChatGPT (opens in a new window) Research Products Business Developers Company Foundation (opens in a new window) Try ChatGPT (opens in a new window) Login OpenAI August 7, 2025 Product Introducing GPT‑5 for developers The best model for coding and agentic tasks. Loading… Share Introduction Introduction Coding Frontend engin