OpenAI o4-mini is the newest lightweight model in the o-series, engineered for efficient and capable reasoning across text and visual tasks. Optimized for speed and performance, it excels in code generation and image-based understanding, while maintaining a balance between latency and reasoning depth. The model supports a 200,000-token context window with up to 100,000 output tokens, making it suitable for extended, high-volume interactions. It handles both text and image inputs, producing textual outputs with advanced reasoning capabilities. With its compact architecture and versatile performance, o4-mini is ideal for a wide array of real-world applications demanding fast, cost-effective intelligence.
Amazon Nova Lite is a versatile multimodal model designed to process text, image, and video inputs, producing text-based outputs. Featuring a 300K-token context window, it is well-suited for real-time interactions, document analysis, and visual question answering. As part of the Amazon Nova foundation models, it supports fine-tuning and distillation, enabling advanced customization.
| o4-mini | Nova Lite | |
|---|---|---|
Web Site
| - | |
Provider
| ||
Chat
| ||
Release Date
| ||
Modalities
| text images | text images video |
API Providers
| OpenAI API | Amazon Bedrock |
Knowledge Cut-off Date
| - | Purposefully not disclosed |
Open Source
| No | No |
Pricing Input
| $1.10 per million tokens | $0.06 per million tokens |
Pricing Output
| $4.40 per million tokens | $0.24 per million tokens |
MMLU
| fort | 80.5% CoT Source |
MMLU-Pro
| - | Not available |
MMMU
| 81.6% Source | Not available |
HellaSwag
| - | Not available |
HumanEval
| 14.28% Source | 85.4% pass@1 Source |
MATH
| - | 73.3% CoT Source |
GPQA
| 81.4% Source | 42% Main Source |
IFEval
| - | 89.7% Source |
SimpleQA
| - | - |
AIME 2024 | 93.4% Source | - |
AIME 2025 | 92.7% Source | - |
Aider Polyglot
| - | - |
LiveCodeBench v5
| - | - |
Global MMLU (Lite)
| - | - |
MathVista
| - | - |
Mobile Application | - | |
MathArena | ||
| Avg. Score | 87% | - |
AIME 2025 A test based on problems from the American Invitational Mathematics Examination, designed to assess the mathematical skills of models. | 92% | - |
HMMT February 2025 A test based on problems from the Harvard-MIT Mathematics Tournament, February 2025, designed to assess the mathematical skills of models. | 83% | - |
BRUMO 2025 | 87% | - |
SMT 2025 A test based on problems from the Stanford Math Tournament, 2025, designed to assess the mathematical skills of models. | 89% | - |
CMIMC 2025 A test based on problems from the Canadian Mathematical Olympiad, 2025, designed to assess the mathematical skills of models. | 84% | - |
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