Technology comparison

Phi-4 vs Gemma 3: models, context & API pricing

Choose Phi-4 if you need top-tier reasoning and coding accuracy in a compact open model and can accommodate its pricing. Choose Gemma 3 if you want flexible model sizes, lower API costs, and strong safety alignment for scalable deployments. Always verify current specs and prices.

Technical context and trade-offs

These differences are drawn directly from the comparison criteria below.

Context Window: The maximum number of tokens the model can process in a single prompt, affecting its ability to handle long documents or conversations.

Phi-4: Phi-4 is designed with a context window that supports handling of substantial text, though the exact maximum length depends on the specific model version and configuration. As of early 2026, the officially documented context window for Phi-4 is 16K tokens, but this may be extended in future releases; always verify with the latest Microsoft documentation.

Gemma 3: Gemma 3, offered in multiple sizes (2B, 7B, and 9B parameters), generally provides a context window of 8K tokens across its variants. This is suitable for many common applications but may be more limited for very long documents compared to Phi-4. Users should confirm the current context window for their chosen model size on the official Gemma 3 documentation.

Benchmark Performance: Evaluation on standardized benchmarks such as MMLU (general knowledge) and HumanEval (coding), when comparable published results are available.

Phi-4: Phi-4 has demonstrated strong performance on benchmarks like MMLU and HumanEval, often outperforming larger models in its class. According to published results from Microsoft (as of early 2026), Phi-4 achieves an MMLU score of approximately 84.8% and a HumanEval score of 82.6%, though these numbers may vary with evaluation settings and should be verified against the latest reports.

Gemma 3: Gemma 3, with its range of sizes, shows competitive performance; the larger 9B variant achieves MMLU and HumanEval scores that are impressive for its size but generally lower than Phi-4's published results. For instance, the Gemma 3 9B model reports an MMLU score around 75-80% and HumanEval around 70-75%, depending on the version and evaluation methodology. Always refer to the official Gemma 3 technical report for current benchmarks.

Quick verdictThe bottom line

Phi-4Gemma 3Explore the detailed comparison

Scores by criterion

Performance50–50 (3 criteria)

Ease of useNo direct evidence in the criteria

EcosystemNo direct evidence in the criteria

PopularityNo direct evidence in the criteria

Phi-4Gemma 3

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

Technical feature matrix

Criterion	Phi-4	Gemma 3	Edge
Context Window: The maximum number of tokens the model can process in a single prompt, affecting its ability to handle long documents or conversations.	Phi-4 is designed with a context window that supports handling of substantial text, though the exact maximum length depends on the specific model version and configuration. As of early 2026, the officially documented context window for Phi-4 is 16K tokens, but this may be extended in future releases; always verify with the latest Microsoft documentation.	Gemma 3, offered in multiple sizes (2B, 7B, and 9B parameters), generally provides a context window of 8K tokens across its variants. This is suitable for many common applications but may be more limited for very long documents compared to Phi-4. Users should confirm the current context window for their chosen model size on the official Gemma 3 documentation.	Phi-4
Benchmark Performance: Evaluation on standardized benchmarks such as MMLU (general knowledge) and HumanEval (coding), when comparable published results are available.	Phi-4 has demonstrated strong performance on benchmarks like MMLU and HumanEval, often outperforming larger models in its class. According to published results from Microsoft (as of early 2026), Phi-4 achieves an MMLU score of approximately 84.8% and a HumanEval score of 82.6%, though these numbers may vary with evaluation settings and should be verified against the latest reports.	Gemma 3, with its range of sizes, shows competitive performance; the larger 9B variant achieves MMLU and HumanEval scores that are impressive for its size but generally lower than Phi-4's published results. For instance, the Gemma 3 9B model reports an MMLU score around 75-80% and HumanEval around 70-75%, depending on the version and evaluation methodology. Always refer to the official Gemma 3 technical report for current benchmarks.	Phi-4
API Pricing: Cost per 1 million input and output tokens when using the model via a cloud API, which affects operational expenses for high-volume applications.	Phi-4 is available through Azure AI services and other platforms, with pricing that varies by provider and region. As of early 2026, typical pricing for Phi-4 is around $0.50 per 1M input tokens and $1.50 per 1M output tokens on Azure, but these rates are subject to change and may differ for other hosting options. Users should check the provider's current pricing page for exact costs.	Gemma 3, being an open model, can be self-hosted at no licensing cost, but API access via services like Google Cloud Vertex AI or third-party providers incurs fees. Pricing for Gemma 3 API access is generally lower than for proprietary models; for example, some providers offer it at around $0.20 per 1M input tokens and $0.50 per 1M output tokens, though this varies widely. Confirm current rates with your chosen API provider.	Gemma 3
Latency: The response time of the model, influenced by model size, hardware, and serving infrastructure, which impacts user experience in real-time applications.	Phi-4, with 14B parameters, typically offers low latency on modern GPUs, especially when served with optimized inference engines like vLLM or TensorRT-LLM. Actual latency depends on batch size, sequence length, and hardware; for interactive applications, it can achieve sub-second response times for short outputs on high-end GPUs, but verify with your specific setup.	Gemma 3's latency varies by model size: the 2B variant is extremely fast, while the 9B variant requires more compute and may have higher latency. On comparable hardware, the 2B model can be faster than Phi-4, but the 9B model may be slower. For latency-sensitive applications, the smaller Gemma 3 sizes offer advantages, but always benchmark on your target infrastructure.	Gemma 3
Model Access and Availability: Whether the model is open-source or closed, and the ease of obtaining and deploying it, including licensing terms.	Phi-4 is released under the MIT license, making it open and permissively available for commercial and research use. It can be downloaded from Hugging Face and other repositories, and is also offered as a managed service on Azure AI. This dual availability provides flexibility for both self-hosting and cloud deployment.	Gemma 3 is available under the Gemma Terms of Use, which is an open license that permits commercial use with some restrictions (e.g., acceptable use policies). It is distributed via Kaggle, Hugging Face, and Google Cloud, and can be self-hosted or accessed via API. The licensing is slightly more restrictive than MIT but still suitable for most applications.	Phi-4
Practical Use Cases: Typical applications where each model excels, considering performance, cost, and deployment constraints.	Phi-4 excels in tasks requiring complex reasoning, coding assistance, and mathematical problem-solving, thanks to its high benchmark scores. It is well-suited for developers building AI assistants, code generation tools, or educational applications where accuracy is critical. Its open license and moderate size also make it a good fit for on-premises deployments with data privacy requirements.	Gemma 3, with its range of sizes, is versatile: the 2B model is ideal for mobile and edge devices, the 7B for general-purpose server deployments, and the 9B for more demanding tasks. It is particularly strong in safety alignment and multilingual support, making it suitable for customer-facing chatbots, content moderation, and applications requiring robust safety guardrails. Its open nature encourages customization and fine-tuning.	Comparable

When to choose each option

Choose this option if… Phi-4

Choose Phi-4 when the strengths described here match your project requirements. Context Window: The maximum number of tokens the model can process in a single prompt, affecting its ability to handle long documents or conversations.: Phi-4 is designed with a context window that supports handling of substantial text, though the exact maximum length depends on the specific model version and configuration. As of early 2026, the officially documented context window for Phi-4 is 16K tokens, but this may be extended in future releases; always verify with the latest Microsoft documentation. Benchmark Performance: Evaluation on standardized benchmarks such as MMLU (general knowledge) and HumanEval (coding), when comparable published results are available.: Phi-4 has demonstrated strong performance on benchmarks like MMLU and HumanEval, often outperforming larger models in its class. According to published results from Microsoft (as of early 2026), Phi-4 achieves an MMLU score of approximately 84.8% and a HumanEval score of 82.6%, though these numbers may vary with evaluation settings and should be verified against the latest reports. Model Access and Availability: Whether the model is open-source or closed, and the ease of obtaining and deploying it, including licensing terms.: Phi-4 is released under the MIT license, making it open and permissively available for commercial and research use. It can be downloaded from Hugging Face and other repositories, and is also offered as a managed service on Azure AI. This dual availability provides flexibility for both self-hosting and cloud deployment.

Choose this option if… Gemma 3

Choose Gemma 3 when the strengths described here match your project requirements. API Pricing: Cost per 1 million input and output tokens when using the model via a cloud API, which affects operational expenses for high-volume applications.: Gemma 3, being an open model, can be self-hosted at no licensing cost, but API access via services like Google Cloud Vertex AI or third-party providers incurs fees. Pricing for Gemma 3 API access is generally lower than for proprietary models; for example, some providers offer it at around $0.20 per 1M input tokens and $0.50 per 1M output tokens, though this varies widely. Confirm current rates with your chosen API provider. Latency: The response time of the model, influenced by model size, hardware, and serving infrastructure, which impacts user experience in real-time applications.: Gemma 3's latency varies by model size: the 2B variant is extremely fast, while the 9B variant requires more compute and may have higher latency. On comparable hardware, the 2B model can be faster than Phi-4, but the 9B model may be slower. For latency-sensitive applications, the smaller Gemma 3 sizes offer advantages, but always benchmark on your target infrastructure.

Technology role diagram

Option A

Phi-4

AI & LLMs

Option B

Gemma 3

AI & LLMs

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

Code examples

Phi-4

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("microsoft/phi-4")
tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-4")

inputs = tokenizer("Hello, world!", return_tensors="pt")
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, Phi-4 or Gemma 3?

Phi-4 supports a context window of up to 16K tokens, while Gemma 3 models generally have an 8K token context window. This makes Phi-4 better suited for tasks involving longer documents or extended conversations. However, context windows can change with updates, so always verify the current specifications on the official documentation pages.

Is Phi-4 or Gemma 3 more accurate on benchmarks like MMLU and HumanEval?

Published results indicate that Phi-4 achieves higher scores on both MMLU and HumanEval compared to Gemma 3's largest model, making it more accurate for general knowledge and coding tasks. For example, Phi-4 reports around 84.8% on MMLU and 82.6% on HumanEval, while Gemma 3 9B scores lower, though exact numbers depend on evaluation settings. Always refer to the latest official benchmarks for the most current comparisons.

Which model is cheaper to use via API?

Gemma 3 generally has lower API pricing than Phi-4, with some providers offering input tokens at around $0.20 per 1M and output at $0.50 per 1M, whereas Phi-4 typically costs about $0.50 per 1M input and $1.50 per 1M output on Azure. However, prices vary by provider and region, so check the current pricing pages for accurate costs.

Can I self-host both models?

Yes, both Phi-4 and Gemma 3 are open models that can be self-hosted. Phi-4 is released under the MIT license, which is very permissive, while Gemma 3 uses the Gemma Terms of Use, which also allows commercial use with some restrictions. Ensure you comply with the respective licenses when deploying.

Which model is faster in terms of latency?

Latency depends on model size and hardware. Gemma 3's 2B variant is likely faster than Phi-4's 14B parameters, but the 9B Gemma 3 may be slower. For real-time applications, the smaller Gemma 3 models offer lower latency, but always benchmark on your specific infrastructure to get accurate results.

What are the best use cases for Phi-4?

Phi-4 is ideal for tasks requiring advanced reasoning, coding, and mathematics, such as AI assistants, code generation, and educational tools. Its high benchmark scores and open license make it a strong choice for on-premises deployments where accuracy and data privacy are critical.

What are the best use cases for Gemma 3?

Gemma 3 excels in scenarios that need a range of model sizes, from mobile (2B) to server (9B), and emphasizes safety and multilingual support. It is well-suited for customer-facing chatbots, content moderation, and edge deployments, and its open license allows for customization and fine-tuning.

Are there any licensing restrictions I should know about?

Phi-4 uses the MIT license, which is very permissive and allows almost any use with attribution. Gemma 3 uses the Gemma Terms of Use, which permits commercial use but includes usage restrictions and requires adherence to acceptable use policies. Review the full licenses before deployment to ensure compliance.