AI Tool Comparison
Gemma 4 vs Phi-4 Reasoning Vision 15B
A side-by-side breakdown to help you pick the right tool for your workflow.
Gemma 4
Run text, image, and supported audio workloads on your own infrastructure with Gemma 4. Choose an edge, dense, or mixture-of-experts variant to match your hardware and task.
Phi-4 Reasoning Vision 15B
Read documents and images while solving multi-step math, science, and reasoning problems: Phi-4-Reasoning-Vision-15B pairs a reasoning backbone with a vision encoder. Microsoft tested it on A6000 through B200 GPUs; check your own hardware's memory and runtime before deploying.
Bottom Line
Last reviewed: August 2026
Gemma 4 and Phi-4 Reasoning Vision 15B both sit in Models, but they're built around different use cases within it. Gemma 4 carries the higher rating (4.5 vs 4.4), but a gap that size rarely overrides a real workflow fit on its own.
Choose Gemma 4 if…
Best for developers building local or self-hosted assistants who can manage deployment, evaluation, and data handling, and its edge is five deployment options: E2B, E4B, 12B Unified, 26B A4B MoE, and 31B Dense, with audio input on the three smaller dense variants. Choose Gemma 4 when control over model deployment matters and you can support the infrastructure; it is not a managed assistant subscription. Lean toward Phi-4 Reasoning Vision 15B instead if combines a reasoning-tuned backbone with a vision encoder in a single 15B open-weight model, able to read documents and images while solving multi-step problems matters more for your use case.
Choose Phi-4 Reasoning Vision 15B if…
Best for developers who need document and image understanding plus multi-step reasoning, and are willing to check their own hardware's memory and runtime against Microsoft's tested configurations (A6000 through B200) before deploying, and its edge is combines a reasoning-tuned backbone with a vision encoder in a single 15B open-weight model, able to read documents and images while solving multi-step problems. Evaluate Phi-4 Reasoning Vision 15B for image/document reasoning on infrastructure you control; verify memory and runtime needs for the chosen configuration. Lean toward Gemma 4 instead if five deployment options: E2B, E4B, 12B Unified, 26B A4B MoE, and 31B Dense, with audio input on the three smaller dense variants matters more for your use case.
| Attribute | Gemma 4 | Phi-4 Reasoning Vision 15B |
|---|---|---|
| Category | Models | Models |
| Pricing | free | free |
| Pricing Detail | Free Apache 2.0 model weights. Hardware, cloud compute, managed hosting, and serving costs are separate. | Free MIT-licensed model weights; hardware, serving, and any hosted-provider charges are separate. |
| Rating |
Key Features
Gemma 4
- Apache 2.0 downloadable model weights
- E2B, E4B, 12B Unified, 26B A4B MoE, and 31B Dense variants
- Text and image input with text output across the family
- Audio input on E2B, E4B, and 12B Unified only
- 128K context on E2B/E4B; 256K on 12B/26B A4B/31B
- Native function calling for tool-connected applications
- Pretraining in 140+ languages and 35+ languages supported out of the box
- Pre-trained and instruction-tuned weights, with documented fine-tuning options
- Official quantized formats and deployment guidance for local and cloud environments
Phi-4 Reasoning Vision 15B
- Vision + reasoning in one 15B model
- Open weights (MIT license)
- 16,384-token context window
- Tested by Microsoft on A6000, A100, H100, and B200 GPUs (verify your own hardware before deploying)
Pros
Gemma 4
- •Permissive licensing and downloadable weights give developers deployment flexibility
- •Multiple architectures and sizes support different hardware budgets
- •Text, vision, and selected audio input can support several tasks in one deployment
- •Official model cards and deployment documentation explain variant-specific trade-offs
Phi-4 Reasoning Vision 15B
- •Excellent quality-per-parameter
- •Free and open (MIT license)
- •Combines vision and reasoning in one model
Cons
Gemma 4
- Serving, updates, evaluation, and access controls remain your responsibility
- Larger variants and long contexts can require substantial memory and compute
- Generated facts, interpretations, and tool calls still need validation
- Audio input is not available on every variant, and output is text only
Phi-4 Reasoning Vision 15B
- Check your own hardware's memory and runtime; Microsoft's tested configurations are A6000 through B200 GPUs
- Needs ML setup to deploy