Technology comparison

Llama 3.3 vs Phi-4: models, context & API pricing

Llama 3.3 is the stronger choice when you need a large, open-weight model for complex reasoning, long-context tasks, and broad ecosystem support. Phi-4 is better suited for resource-constrained deployments where a smaller model's efficiency, lower cost, and strong performance on focused tasks matter more. Choose based on your latency, budget, and task-complexity trade-offs.

Technical context and trade-offs

These differences are drawn directly from the comparison criteria below.

Model architecture and size

Llama 3.3: Llama 3.3 is a 70-billion-parameter dense transformer model released by Meta in December 2024. It uses a decoder-only architecture with grouped-query attention and is designed for general-purpose text generation and reasoning. The model is distributed as open weights under a community license.

Phi-4: Phi-4 is a 14-billion-parameter dense transformer model developed by Microsoft and released in December 2024. It uses a decoder-only architecture with a focus on high-quality synthetic data and curated training. The model is distributed as open weights under an MIT license, making it permissive for commercial use.

Context window

Llama 3.3: Llama 3.3 supports a context window of 128,000 tokens, according to Meta's model card. This allows processing of long documents, codebases, or multi-turn conversations without truncation. The effective context length can vary depending on the inference implementation and memory available.

Phi-4: Phi-4 supports a context window of 16,000 tokens, as stated in its model card. This is sufficient for many single-document tasks and moderate conversation histories but is notably shorter than Llama 3.3's. For long-context applications, you may need to chunk or summarize inputs.

Quick verdictThe bottom line

Llama 3.3Phi-4Explore the detailed comparison

Scores by criterion

Performance50–50 (2 criteria)

Ease of useNo direct evidence in the criteria

EcosystemNo direct evidence in the criteria

PopularityNo direct evidence in the criteria

Llama 3.3Phi-4

Scores reflect wins in matching criteria; ties count as half a point.

Technical feature matrix

Criterion	Llama 3.3	Phi-4	Edge
Model architecture and size	Llama 3.3 is a 70-billion-parameter dense transformer model released by Meta in December 2024. It uses a decoder-only architecture with grouped-query attention and is designed for general-purpose text generation and reasoning. The model is distributed as open weights under a community license.	Phi-4 is a 14-billion-parameter dense transformer model developed by Microsoft and released in December 2024. It uses a decoder-only architecture with a focus on high-quality synthetic data and curated training. The model is distributed as open weights under an MIT license, making it permissive for commercial use.	Phi-4
Context window	Llama 3.3 supports a context window of 128,000 tokens, according to Meta's model card. This allows processing of long documents, codebases, or multi-turn conversations without truncation. The effective context length can vary depending on the inference implementation and memory available.	Phi-4 supports a context window of 16,000 tokens, as stated in its model card. This is sufficient for many single-document tasks and moderate conversation histories but is notably shorter than Llama 3.3's. For long-context applications, you may need to chunk or summarize inputs.	Llama 3.3
Benchmark performance (MMLU and HumanEval)	Meta reports that Llama 3.3 70B achieves an MMLU score of 86.0 and a HumanEval score of 88.4, based on published evaluations. These results indicate strong general knowledge and code generation capabilities. Scores can vary with prompt formatting and evaluation settings.	Microsoft reports that Phi-4 achieves an MMLU score of 84.8 and a HumanEval score of 82.6, according to its technical report. These results are competitive for a 14B model but slightly below Llama 3.3 on both benchmarks. As with any benchmark, real-world performance depends on the task and implementation.	Llama 3.3
API pricing per 1M tokens	Llama 3.3 is available through various providers, and pricing varies. For example, some providers offer input pricing around $0.59 per 1M tokens and output pricing around $0.79 per 1M tokens, but these rates are subject to change and depend on the provider and plan. Always check the provider's current pricing page for accurate costs.	Phi-4 is available through Azure AI and other providers, with pricing that varies. As of early 2026, some providers list input pricing around $0.13 per 1M tokens and output pricing around $0.52 per 1M tokens, but these are not guaranteed and may change. Verify with the specific provider for up-to-date pricing.	Phi-4
Latency and throughput	Llama 3.3, being a 70B model, typically requires more compute and memory, which can lead to higher latency and lower throughput compared to smaller models. Actual performance depends on hardware, quantization, and batching. For latency-sensitive applications, consider using smaller models or optimized inference engines.	Phi-4, with 14B parameters, generally offers lower latency and higher throughput on equivalent hardware, making it suitable for real-time applications and edge deployments. However, actual latency depends on the serving stack and optimization. Quantitative latency figures require benchmarking on your specific setup.	Phi-4
Model access and open/closed availability	Llama 3.3 is available as open weights under the Llama 3.3 Community License, which permits commercial use with certain restrictions. It can be self-hosted or accessed via numerous cloud providers and APIs. The license requires attribution and has a acceptable use policy.	Phi-4 is released under the MIT license, which is a permissive open-source license allowing unrestricted use, modification, and distribution, including commercial applications. It is available for self-hosting and through Azure AI and other platforms. The MIT license offers greater legal flexibility for some organizations.	Phi-4
Practical use cases	Llama 3.3 excels in complex reasoning, long-document analysis, code generation, and multi-turn conversations where a large context and strong general knowledge are beneficial. It is well-suited for research, advanced chatbots, and enterprise applications requiring high accuracy. The larger size may require significant infrastructure.	Phi-4 is ideal for cost-sensitive and resource-constrained deployments, such as edge devices, mobile applications, and scenarios where low latency is critical. It performs well on focused tasks like summarization, question answering, and code completion, especially when fine-tuned. Its smaller size enables easier experimentation and deployment.	Comparable

When to choose each option

Choose this option if… Llama 3.3

Choose Llama 3.3 when the strengths described here match your project requirements. Context window: Llama 3.3 supports a context window of 128,000 tokens, according to Meta's model card. This allows processing of long documents, codebases, or multi-turn conversations without truncation. The effective context length can vary depending on the inference implementation and memory available. Benchmark performance (MMLU and HumanEval): Meta reports that Llama 3.3 70B achieves an MMLU score of 86.0 and a HumanEval score of 88.4, based on published evaluations. These results indicate strong general knowledge and code generation capabilities. Scores can vary with prompt formatting and evaluation settings.

Choose this option if… Phi-4

Choose Phi-4 when the strengths described here match your project requirements. Model architecture and size: Phi-4 is a 14-billion-parameter dense transformer model developed by Microsoft and released in December 2024. It uses a decoder-only architecture with a focus on high-quality synthetic data and curated training. The model is distributed as open weights under an MIT license, making it permissive for commercial use. API pricing per 1M tokens: Phi-4 is available through Azure AI and other providers, with pricing that varies. As of early 2026, some providers list input pricing around $0.13 per 1M tokens and output pricing around $0.52 per 1M tokens, but these are not guaranteed and may change. Verify with the specific provider for up-to-date pricing. Latency and throughput: Phi-4, with 14B parameters, generally offers lower latency and higher throughput on equivalent hardware, making it suitable for real-time applications and edge deployments. However, actual latency depends on the serving stack and optimization. Quantitative latency figures require benchmarking on your specific setup.

Technology role diagram

Option A

Llama 3.3

Comparison

Option B

Phi-4

AI & LLMs

This diagram shows broad areas of use, not a complete deployment architecture.

Code examples

Llama 3.3

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "meta-llama/Llama-3.3-70B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

inputs = tokenizer("Hello, world!", return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Frequently asked questions

Which model has a larger context window?

Llama 3.3 supports a context window of 128,000 tokens, while Phi-4 supports 16,000 tokens. If your application requires processing very long documents or extensive conversation histories, Llama 3.3 is the better choice. However, always verify the context window with the specific provider or implementation you use, as it may vary.

How do Llama 3.3 and Phi-4 compare on benchmarks like MMLU and HumanEval?

According to published results, Llama 3.3 70B scores 86.0 on MMLU and 88.4 on HumanEval, while Phi-4 scores 84.8 on MMLU and 82.6 on HumanEval. These numbers suggest Llama 3.3 has a slight edge on both benchmarks, but the difference is modest. Real-world performance depends on your specific task, prompt design, and evaluation methodology.

Which model is more cost-effective for API usage?

Phi-4 generally has lower per-token pricing than Llama 3.3 across many providers, but prices vary and change frequently. For example, some providers list Phi-4 at around $0.13 per 1M input tokens and $0.52 per 1M output tokens, while Llama 3.3 may be around $0.59 and $0.79 respectively. Always check the current pricing from your chosen provider, as these figures are not guaranteed.

Which model has lower latency?

Phi-4, being a 14B model, typically offers lower latency and higher throughput than the 70B Llama 3.3 on equivalent hardware. However, actual latency depends on the serving infrastructure, quantization, and batch size. For latency-critical applications, Phi-4 is often the more practical choice, but you should benchmark on your own setup.

What are the licensing differences between Llama 3.3 and Phi-4?

Llama 3.3 is released under the Llama 3.3 Community License, which allows commercial use but includes certain restrictions and an acceptable use policy. Phi-4 is released under the MIT license, which is more permissive and allows unrestricted use, modification, and distribution, including for commercial purposes. If license flexibility is important, Phi-4 may be preferable.

Can I self-host both models?

Yes, both Llama 3.3 and Phi-4 are available as open weights and can be self-hosted. Llama 3.3 requires significant GPU memory due to its 70B parameters, while Phi-4 is smaller and can run on more modest hardware. Ensure you comply with the respective licenses when self-hosting.

Which model is better for code generation?

Llama 3.3 has a higher HumanEval score (88.4 vs 82.6 for Phi-4), suggesting it may perform better on code generation tasks. However, Phi-4 is still capable and may be sufficient for many coding assistants, especially when fine-tuned. Consider testing both on your specific codebase and tasks.

What about fine-tuning and customization?

Both models can be fine-tuned, but Phi-4's smaller size makes it easier and cheaper to fine-tune on custom datasets. Llama 3.3 may require more resources for fine-tuning but offers a larger capacity for learning complex patterns. The choice depends on your budget and the complexity of your domain.