How to migrate from
GPT-4o to Llama 3.3

Executive Summary

Many developers are switching from GPT-4o to Llama 3.3 to take advantage of lower costs, faster inference, and the flexibility of an open-source framework. Meta’s latest model, Llama 3.3 70B, delivers nearly 2,400 tokens per second on Cerebras hardware, at one-fourth the cost of GPT-4o. 

This guide explains how to transition from OpenAI’s GPT-4o to Meta’s open-source Llama 3.3. It covers:

1. Model Comparisons:
   A detailed analysis of the technical differences in performance, cost, and behavior between the models
2. Prompt Engineering Strategies:
   In-depth strategies covering function calling, multi-step reasoning, context management, and more
3. Best Practices & Troubleshooting:
   General tricks and practices to overcome common pitfalls

Introduction

Migrating from GPT-4o to Llama 3.3 unlocks significant benefits, including 4× cheaper inference, 35× faster throughput (on providers like Cerebras), and the ability to fully customize models. Unlike proprietary models, Llama 3.3 provides an open‑source alternative that can be fine‑tuned or deployed on‑premises for greater control over infrastructure and data privacy.

However, Llama 3.3 is not a direct drop‑in replacement for GPT‑4o. Differences in model size, training data, and behavior mean that prompts must be adapted to achieve the best results. This guide outlines these differences and provides practical strategies to optimize Llama’s output for high performance and accuracy.

Difference in Model Performance

Llama 3.3 70B delivers GPT‑4o‑level performance while being faster and significantly cheaper.

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Llama 3.3 is based on a transformer architecture that emphasizes efficiency and scalability. It offers a range of models—from 8‑billion parameter models to 70‑billion parameter models designed for complex tasks.

The most advanced model in the series, Llama 3.3 70B, excels in structured applications such as tool integration, instruction adherence, and coding workflows. Additionally, Llama 3.3 is recognized for its cost‑effectiveness and faster inference speeds, especially when deployed with providers like Cerebras.

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Here is a breakdown of how Llama 3.3 70B and GPT‑4o perform across different use cases:

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Strategies for Better Results

Different models require different prompts. When migrating from GPT‑4o to Llama 3.3 70B, a common mistake is reusing old prompts without adjusting them for the new model—which can lead to sub‑optimal performance. To fully leverage the strengths of Llama 3.3, it’s important to understand the model and fine‑tune your prompts accordingly.

Below are key techniques to maximize the performance of Llama 3.3 70B across different use cases.

Function Calling and Decision Making

Both GPT‑4o and Llama 3.3 70B can call external functions to improve their responses. When using external functions, the models must first decide whether to call a function, then execute that decision. While GPT‑4o was trained extensively to do this, Llama 3.3 may require additional instructions to reliably determine when to call and execute functions. With the right prompts, Llama 3.3 can achieve robust and efficient function calling, even beating GPT‑4o's performance.

For example, imagine you are building a restaurant booking assistant. The assistant has access to the functions search_restaurant and book_table.

Example

Original Prompt: I need a table for two at 7 PM in a casual Italian restaurant tonight.

Llama 3.3 Output: Luigi's is a great casual Italian restaurant in New York that I recommend.‍

‍Wrong response. This prompt fails to call the necessary functions to search for available restaurants and book a table, instead providing an unverified, generic recommendation.

Prompting Strategies

How to improve

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Complex, Multi-Step Reasoning

Llama 3.3 is highly capable in multi‑step reasoning tasks—especially when given clear, structured guidance. However, it can sometimes skip intermediate steps or jump to conclusions prematurely. By breaking complex tasks into clear, sequential steps, you can achieve more rigorous and accurate reasoning.

For example, imagine you are building a command‑line agent with access to your local files through tools.

Example

Original Prompt: Update the parameters in parameters.yaml with the new values in values.txt.‍

‍Llama 3.3 Output: "I have generated the new parameter.yaml."

‍Wrong response. Fails to look at values.txt

Prompting Strategies

How to improve

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Context Switching and Long Context

Both GPT‑4o and Llama 3.3 have 128K context windows, allowing them to process and reference long documents, maintain extended conversations, and retain crucial details over time. However, both models may struggle with long conversations. Two common mistakes are:

1. Forgetting previous details: When a conversation is too long, the model might forget earlier details.
2. Retaining irrelevant information: When switching topics rapidly, the model might cling to past context that is no longer relevant.

The key is to help Llama 3.3 know what to retain and what to ignore as the context evolves.

Prompting Strategies

How to improve

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Bias and Hallucination in Evaluation Tasks

Llama 3.3 excels in detailed critiques and thorough evaluations, identifying nuances that other models (like GPT‑4o) might overlook. While GPT‑4o generally provides balanced feedback, Llama 3.3 sometimes hallucinates errors or biases—flagging mistakes that don’t actually exist. With structured guidance, you can ensure Llama 3.3 provides fair and accurate evaluations.

Example

Original Prompt: Is there anything wrong with this solution?

‍Llama 3.3 Output: Yes, the second step should be rewritten for clarity, and an alternative approach may be preferable.

‍Wrong response. The provided solution is actually correct, and the response fabricates unnecessary revisions.

Prompting Strategies

How to improve

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Personality and Tone in Responses

Llama 3.3 is often used to build customer support agents, AI avatars, and more—use cases where tone and personality are important. Although it tends to be technical, direct, and factual, you can prompt Llama 3.3 to be warmer, more engaging, and personable for other applications.

Example

Original Prompt: I need pest control now. They’re everywhere.

‍Llama 3.3 Output: That sounds really frustrating. Can you tell me more?

Follow-up Prompt: I have termites everywhere goddamnit.

‍Llama 3.3 Output: That sounds really frustrating. What’s been going on with the termites?

‍Wrong response. The response is repetitive and lacks warmth.

Prompting Strategies

How to improve

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Translation and Cultural Nuances

Llama 3.3 is highly capable of translating multiple languages with speed and efficiency. While it prioritizes direct, word‑to‑word translation, it may sometimes need additional context to capture cultural nuances and idiomatic expressions. In contrast, GPT‑4o naturally captures idiomatic expressions and local tones through extensive multilingual training. With tailored prompts, Llama 3.3 can produce natural, context‑aware translations—making it a strong choice for multilingual tasks.

Example

Original Prompt: Translate the following into Chinese: ‘Life is a marathon not a sprint, so we should savor each moment.

‍Llama 3.3 Output: 生活是一场马拉松，不是短跑，所以我们应该享受每一刻。

‍Wrong response. A literal translation that may not resonate naturally with native speakers.

Prompting Strategies

How to improve

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General Best Practices

In addition to the specific use cases above, here are additional prompting guidelines from Meta

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Resources

1. Vellum AI. Llama 3.3 70B vs GPT‑4o. Benchmark comparison of model performance. Vellum AI.
2. Analytics Vidhya. DeepSeek‑V3 vs GPT‑4o vs Llama 3.3 70B: Find the Best AI Model. Insights on instruction following and math capabilities. Analytics Vidhya.
3. TeleWizard AI Blog. GPT‑4o vs Llama 3.3: Which AI Model Powers The Ultimate Phone Agent Experience? Discussion of language, cultural sensitivity, and context handling. TeleWizard AI.
4. Reddit r/LocalLLaMA. Llama 3.3 is now almost 25× cheaper than GPT‑4o on OpenRouter, but is it worth the hype? User comments on Llama 3.3’s cost advantage. Reddit.
5. AI News Newsletter. [AINews] o3 Solves AIME, GPQA, Codeforces, Makes 11 Years of Progress in ARC‑AGI and 25% in FrontierMath. Observations on Llama 3.3’s function‑calling behavior. AI News.
6. Width.ai. Chain‑of‑Thought Prompting — Improve Accuracy by Getting LLMs to Reason. Guide on prompt engineering techniques for step‑by‑step reasoning. Width.ai.
7. Evidently AI. LLM‑as‑a‑Judge: A Complete Guide to Using LLMs for Evaluations. Tips on breaking down evaluation criteria for LLMs. Evidently AI.
8. LifeWire. What to Know About Llama 3. LifeWire.
9. Cerebras. CEPO Capabilities Documentation. Cerebras Inference Docs.
10. Artificial Analysis AI. Llama 3.3 Instruct 70B Model Providers. Artificial Analysis AI.