GPT-4.1, launched by OpenAI on April 14, 2025, introduces a 1 million token context window and supports outputs of up to 32,768 tokens per request. It delivers outstanding performance on coding tasks, achieving 54.6% on the SWE-Bench Verified benchmark, and shows a 10.5% improvement over GPT-4o on MultiChallenge for instruction following. The model's knowledge cutoff is set at June 2024. Pricing is $2.00 per million tokens for input and $8.00 per million tokens for output, with a 75% discount applied to cached inputs, making it highly cost-efficient for repeated queries.
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 | 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
| $2.00 per million tokens | $0.23 per million tokens |
Pricing Output
| $8.00 per million tokens | $0.40 per million tokens |
MMLU
| 90.2% pass@1 Source | 86% 0-shot, CoT Source |
MMLU-Pro
| - | 68.9% 5-shot, CoT Source |
MMMU
| 74.8% Source | Not available |
HellaSwag
| - | Not available |
HumanEval
| - | 88.4% pass@1 Source |
MATH
| - | 77% 0-shot, CoT Source |
GPQA
| 66.3% Diamond Source | 50.5% 0-shot, CoT Source |
IFEval
| - | 92.1% Source |
SimpleQA
| - | - |
AIME 2024 | 48.1% Source | - |
AIME 2025 | - | - |
Aider Polyglot
| - | - |
LiveCodeBench v5
| - | - |
Global MMLU (Lite)
| 87.3% pass@1 Source | - |
MathVista
| - | - |
Mobile Application | - |
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