·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
America needs to stop getting shocked by Chinese AI2h◆Advancing next-gen AI with materials science innovation2h◆Gritt exits stealth with $34 million for robots to build solar plants—then, everything else3h◆Capacity and Redundancy Trade-offs in Multi-Task Learning9h◆Predictive Training with Latent Imagination for Visual Quadruped Navigation9h◆Where Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making9h◆Did We Actually Fix It? An Independent Adversarial Stress-Test of Post-Point-Adjustment Evaluation Metrics for Time-Series Anomaly Detection9h◆Supervised Reward Inference9h◆PPO-HSC: An Exploratory Reinforcement Learning Framework Based on Wide-Area Policy Coverage Optimization9h◆Is Progressive Disclosure All You Need for Long-Context Agents?9h◆It Depends on the Dataset: When a Brain-Encoding Model's Predicted Responses Beat Their Visual Backbone for Video Memorability9h◆DMFNet: Dual-Backbone Multiscale Fusion Network for Urban Scene Classification9h◆Oracle Gap and Signal Fidelity: A Fixed-Pool Diagnostic for Test-Time Collaboration9h◆Time-Frequency Consistency Learning for Robust Speech Deepfake Detection9h◆Scientific reasoning does not reliably translate into scientific forecasting in frontier AI9h◆Language Triggers Hijack Language Circuits: A Mechanistic Analysis of Backdoor Behaviors in Large Language Models9h◆Kernel Regression with Tensor Trains and Hadamard Overparameterization9h◆AI-Augmented Human Resource Management? Insights from German companies9h◆Diagnosing Correctness Probes under Self-Judgement Confounding9h◆BLAD: A Historically Contextualized, Multilingual Dataset of Bangladeshi Legal Acts (1799 to 2025)9h◆America needs to stop getting shocked by Chinese AI2h◆Advancing next-gen AI with materials science innovation2h◆Gritt exits stealth with $34 million for robots to build solar plants—then, everything else3h◆Capacity and Redundancy Trade-offs in Multi-Task Learning9h◆Predictive Training with Latent Imagination for Visual Quadruped Navigation9h◆Where Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making9h◆Did We Actually Fix It? An Independent Adversarial Stress-Test of Post-Point-Adjustment Evaluation Metrics for Time-Series Anomaly Detection9h◆Supervised Reward Inference9h◆PPO-HSC: An Exploratory Reinforcement Learning Framework Based on Wide-Area Policy Coverage Optimization9h◆Is Progressive Disclosure All You Need for Long-Context Agents?9h◆It Depends on the Dataset: When a Brain-Encoding Model's Predicted Responses Beat Their Visual Backbone for Video Memorability9h◆DMFNet: Dual-Backbone Multiscale Fusion Network for Urban Scene Classification9h◆Oracle Gap and Signal Fidelity: A Fixed-Pool Diagnostic for Test-Time Collaboration9h◆Time-Frequency Consistency Learning for Robust Speech Deepfake Detection9h◆Scientific reasoning does not reliably translate into scientific forecasting in frontier AI9h◆Language Triggers Hijack Language Circuits: A Mechanistic Analysis of Backdoor Behaviors in Large Language Models9h◆Kernel Regression with Tensor Trains and Hadamard Overparameterization9h◆AI-Augmented Human Resource Management? Insights from German companies9h◆Diagnosing Correctness Probes under Self-Judgement Confounding9h◆BLAD: A Historically Contextualized, Multilingual Dataset of Bangladeshi Legal Acts (1799 to 2025)9h◆
DataBubble·

Model Detail

openai logo

OpenAI: GPT-5 Nano

—
Provider: OpenAICategory: multimodal
DB Score
11.4
Downloads
0
Likes
0
Day
+0.0%
Week
+0.0%
Month
+0.0%
Overview

OpenAI: GPT-5 Nano is a multimodal model released by OpenAI. And supports text+image+file->text inputs.

Performance

OpenAI: GPT-5 Nano has been evaluated across multiple task suites. On task-specific evaluations the model scores 34.8% resolved on SWE-Bench.

How we score this →
Pricing & Throughput

OpenAI: GPT-5 Nano is priced at $0.05/M input tokens and $0.4/M output tokens. Operationally the model offers a 272K-token context window, which matters when sizing it for prompt-heavy or latency-sensitive workloads. At this input rate the model sits in the commodity tier and is suitable for high-volume workloads where per-call cost dominates the decision.

Technical

The published knowledge cutoff is 2024-05-31, so newer events will not be reflected in zero-shot answers without retrieval.

Use Cases

OpenAI: GPT-5 Nano is best fit for mixed text-and-image reasoning tasks such as document understanding, high-volume batch jobs where per-call cost dominates the budget, and long-context tasks such as full-codebase analysis or book-length summarization (272K tokens). Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.

Download History
Pricing
Input ($/M tokens)
$0.05
Output ($/M tokens)
$0.4
Context Window
272K
Arena & Community
SWE-Bench
34.8%
Model Info
Modalitytext+image+file->text
Knowledge Cutoff2024-05-31
Related Models
openai logo
clip-vit-large-patch14
OpenAI · 33.1M downloads
openai logo
clip-vit-base-patch32
OpenAI · 21.4M downloads
Qwen logo
Qwen3-VL-2B-Instruct
Qwen · 22.5M downloads
google logo
gemma-4-26B-A4B-it
Google · 13.9M downloads
HomeModelsNews