With native support for a 128K token context window, function calling, and robust tool usage, GPT-4.1 brings AI closer to behaving like a reliable, adaptive assistant—ready to work, build, and collaborate across tasks with speed and precision.
- Software Engineers: Build smarter apps, code assistants, and debuggers with GPT-4.1’s powerful reasoning and tool-calling abilities.
- Product Teams & Startups: Create responsive and intelligent AI features across web and mobile platforms.
- Researchers & Analysts: Summarize papers, analyze large datasets, and interact with data in natural language.
- Educators & EdTech Providers: Power AI tutors, generate quizzes, and explain concepts with clarity and accuracy.
- Customer Support & CX Teams: Deploy chatbots and assistants that understand context, follow instructions, and offer accurate help.
- Writers & Content Teams: Use GPT-4.1 to ideate, draft, and refine content with improved coherence and tone control.
🛠️ How to Use GPT-4.1?
- Step 1: Access the OpenAI API: Log in to the OpenAI Platform and select GPT-4.1 via the Playground or API.
- Step 2: Choose the Model ID: Use gpt-4-1106-preview or any new model ID associated with GPT-4.1 (based on OpenAI’s rollout naming).
- Step 3: Set Up Your Prompt or System Message: Define clear goals, roles, or instructions to align the assistant’s behavior with your task.
- Step 4: Use Tools (Optional): Enable tools like the code interpreter, retrieval plugins, or image input support, depending on your use case.
- Step 5: Generate & Analyze Output: Let GPT-4.1 complete tasks, generate responses, or process inputs with improved speed and contextual awareness.
- Step 6: Integrate into Workflow: Use API endpoints, Assistants API, or platform SDKs to embed GPT-4.1 into your apps or pipelines.
- 128K Context Window: Handle longer documents, richer chats, and deep contextual tasks effortlessly.
- Multimodal Input Support: Understands text, code, and images (API availability depends on use case).
- Improved Tool Use: Native support for function calling, code execution, file analysis, and web browsing (when tools are enabled).
- High Factual Accuracy: Significant reduction in hallucinations and better adherence to instructions.
- Memory-Ready Architecture: Structured to support memory and assistant-based use cases (with evolving support from OpenAI).
- Smarter Reasoning: Handles complex, multi-step problems better than previous models, especially in math, coding, and logic-heavy scenarios.
- Massive Context Memory: 128K tokens open up longer workflows, deeper analysis, and uninterrupted chats.
- Excellent Reasoning Capabilities: Especially good with logic, math, and structured decision-making.
- Tool Usage Built-In: Makes code execution, image handling, and function calling seamless.
- ]Fewer Hallucinations: One of the most accurate models to date in terms of factual output.
- Fast and Cost-Effective (via Turbo): Delivers impressive performance with reasonable pricing.
- Model Access May Be Limited: Full GPT-4.1 capability might be gated or reserved for API partners or enterprise use.
- Lack of Transparency: Still no public information on parameters or model architecture.
- Requires Structured Setup for Tools: Tooling (e.g. code interpreter) needs additional integration work.
- Overconfidence in Edge Cases: Can still hallucinate or fabricate facts if not properly prompted.
- Not Fully Available in All Products: Some capabilities may be tied to the Assistants API or not directly in ChatGPT.
Plus
$ 20.00
Pro
$ 200.00
API
$2/$8
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DeepSeek-V3
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DeepSeek-V3
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DeepSeek-Coder-V2
DeepSeek‑Coder V2 is an open-source, Mixture‑of‑Experts (MoE) code-focused variant of DeepSeek‑V2, purpose-built for code generation, completion, debugging, and mathematical reasoning. Trained with an additional 6 trillion tokens of code and text, it supports up to 338 programming languages and a massive 128K‑token context window, rivaling or exceeding commercial code models in performance.
DeepSeek-Coder-V2
DeepSeek‑Coder V2 is an open-source, Mixture‑of‑Experts (MoE) code-focused variant of DeepSeek‑V2, purpose-built for code generation, completion, debugging, and mathematical reasoning. Trained with an additional 6 trillion tokens of code and text, it supports up to 338 programming languages and a massive 128K‑token context window, rivaling or exceeding commercial code models in performance.
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Meta Llama 3.2
Llama 3.2 is Meta’s multimodal and lightweight update to its Llama 3 line, released on September 25, 2024. The family includes 1B and 3B text-only models optimized for edge devices, as well as 11B and 90B Vision models capable of image understanding. It offers a 128K-token context window, Grouped-Query Attention for efficient inference, and opens up on-device, private AI with strong multilingual (e.g. Hindi, Spanish) support.
Meta Llama 3.2
Llama 3.2 is Meta’s multimodal and lightweight update to its Llama 3 line, released on September 25, 2024. The family includes 1B and 3B text-only models optimized for edge devices, as well as 11B and 90B Vision models capable of image understanding. It offers a 128K-token context window, Grouped-Query Attention for efficient inference, and opens up on-device, private AI with strong multilingual (e.g. Hindi, Spanish) support.
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DeepSeek R1 Distill refers to a family of dense, smaller models distilled from DeepSeek’s flagship DeepSeek R1 reasoning model. Released early 2025, these models come in sizes ranging from 1.5B to 70B parameters (e.g., DeepSeek‑R1‑Distill‑Qwen‑32B) and retain powerful reasoning and chain-of-thought abilities in a more efficient architecture. Benchmarks show distilled variants outperform models like OpenAI’s o1‑mini, while remaining open‑source under MIT license.
DeepSeek R1 Distill refers to a family of dense, smaller models distilled from DeepSeek’s flagship DeepSeek R1 reasoning model. Released early 2025, these models come in sizes ranging from 1.5B to 70B parameters (e.g., DeepSeek‑R1‑Distill‑Qwen‑32B) and retain powerful reasoning and chain-of-thought abilities in a more efficient architecture. Benchmarks show distilled variants outperform models like OpenAI’s o1‑mini, while remaining open‑source under MIT license.
DeepSeek R1 Distill Qwen‑32B is a 32-billion-parameter dense reasoning model released in early 2025. Distilled from the flagship DeepSeek R1 using Qwen 2.5‑32B as a base, it delivers state-of-the-art performance among dense LLMs—outperforming OpenAI’s o1‑mini on benchmarks like AIME, MATH‑500, GPQA Diamond, LiveCodeBench, and CodeForces rating.
DeepSeek R1 Distill Qwen‑32B is a 32-billion-parameter dense reasoning model released in early 2025. Distilled from the flagship DeepSeek R1 using Qwen 2.5‑32B as a base, it delivers state-of-the-art performance among dense LLMs—outperforming OpenAI’s o1‑mini on benchmarks like AIME, MATH‑500, GPQA Diamond, LiveCodeBench, and CodeForces rating.
DeepSeek R1 Distill Qwen‑32B is a 32-billion-parameter dense reasoning model released in early 2025. Distilled from the flagship DeepSeek R1 using Qwen 2.5‑32B as a base, it delivers state-of-the-art performance among dense LLMs—outperforming OpenAI’s o1‑mini on benchmarks like AIME, MATH‑500, GPQA Diamond, LiveCodeBench, and CodeForces rating.
Editorial Note
This page was researched and written by the ATB Editorial Team. Our team researches each AI tool by reviewing its official website, testing features, exploring real use cases, and considering user feedback. Every page is fact-checked and regularly updated to ensure the information stays accurate, neutral, and useful for our readers.
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