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.
| Grok 4 | GPT-4.1 | |
|---|---|---|
Web Site
| ||
Provider
| ||
Chat
| ||
Release Date
| ||
Modalities
| text images voice video | text images |
API Providers
| xAI | OpenAI API |
Knowledge Cut-off Date
| - | - |
Open Source
| No | No |
Pricing Input
| $3.00 per million tokens | $2.00 per million tokens |
Pricing Output
| $15.00 per million tokens | $8.00 per million tokens |
MMLU
| - | 90.2% pass@1 Source |
MMLU-Pro
| - | - |
MMMU
| - | 74.8% Source |
HellaSwag
| - | - |
HumanEval
| - | - |
MATH
| - | - |
GPQA
| 87.5% Science Source | 66.3% Diamond Source |
IFEval
| - | - |
SimpleQA
| - | - |
AIME 2024 | - | 48.1% Source |
AIME 2025 | 91.7% Competition Math Source | - |
Aider Polyglot
| - | - |
LiveCodeBench v5
| 79% Competitive Coding Source | - |
Global MMLU (Lite)
| - | 87.3% pass@1 Source |
MathVista
| - | - |
Mobile Application | ||
MathArena | ||
| Avg. Score | 89% | - |
AIME 2025 A test based on problems from the American Invitational Mathematics Examination, designed to assess the mathematical skills of models. | 91% | - |
HMMT February 2025 A test based on problems from the Harvard-MIT Mathematics Tournament, February 2025, designed to assess the mathematical skills of models. | 92% | - |
BRUMO 2025 | 95% | - |
SMT 2025 A test based on problems from the Stanford Math Tournament, 2025, designed to assess the mathematical skills of models. | 86% | - |
CMIMC 2025 A test based on problems from the Canadian Mathematical Olympiad, 2025, designed to assess the mathematical skills of models. | 83% | - |
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