GPT-4.1 Nano, launched by OpenAI on April 14, 2025, is the company's fastest and most affordable model to date. Designed for low-latency tasks such as classification, autocomplete, and fast inference scenarios, it combines compact architecture with robust capabilities. Despite its size, it supports an impressive 1 million token context window and delivers strong benchmark results, achieving 80.1% on MMLU and 50.3% on GPQA. With a knowledge cutoff of June 2024, GPT-4.1 Nano offers exceptional value at just $0.10 per million input tokens and $0.40 per million output tokens, with a 75% discount applied to cached inputs, making it ideal for high-volume, cost-sensitive deployments.
Llama 3.3 70B Instruct, created by Meta, is a multilingual large language model specifically fine-tuned for instruction-based tasks and optimized for conversational applications. It is capable of processing and generating text in multiple languages, with a context window supporting up to 128,000 tokens. Launched on December 6, 2024, the model surpasses numerous open-source and proprietary chat models in various industry benchmarks. It utilizes Grouped-Query Attention (GQA) to improve scalability and has been trained on a diverse dataset comprising over 15 trillion tokens from publicly available sources. The model's knowledge is current up to December 2023.
| GPT-4.1 Nano | Llama 3.3 70B Instruct | |
|---|---|---|
Web Site
| ||
Provider
| ||
Chat
| ||
Release Date
| ||
Modalities
| text images | text |
API Providers
| OpenAI API | Fireworks, Together, DeepInfra, Hyperbolic |
Knowledge Cut-off Date
| - | 12.2024 |
Open Source
| No | Yes |
Pricing Input
| $0.10 per million tokens | $0.23 per million tokens |
Pricing Output
| $0.40 per million tokens | $0.40 per million tokens |
MMLU
| 80.1% Source | 86% 0-shot, CoT Source |
MMLU-Pro
| - | 68.9% 5-shot, CoT Source |
MMMU
| 55.4% Source | Not available |
HellaSwag
| - | Not available |
HumanEval
| - | 88.4% pass@1 Source |
MATH
| - | 77% 0-shot, CoT Source |
GPQA
| 50.3% Diamond Source | 50.5% 0-shot, CoT Source |
IFEval
| 74.5% Source | 92.1% Source |
SimpleQA
| - | - |
AIME 2024 | 29.4% Source | - |
AIME 2025 | - | - |
Aider Polyglot
| - | - |
LiveCodeBench v5
| - | - |
Global MMLU (Lite)
| 66.9% Source | - |
MathVista
| 56.2% Image Reasoning Source | - |
Mobile Application | - |
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