Phi-4-reasoning

reasoning

Phi-4-Reasoning is a 14-billion-parameter model trained on top of the base Phi-4 through supervised fine-tuning, using over 1.4 million carefully curated prompts and high-quality responses generated by the o3-mini1 model. The model specializes in tasks requiring complex, multi-step reasoning and demonstrates the ability to generate detailed reasoning chains while efficiently utilizing computational resources during inference.

The model’s performance metrics are impressive: it outperforms significantly larger open models such as DeepSeek-R1-Distill-Llama-70B and approaches the performance level of the full DeepSeek-R1 model across various benchmarks. On the AIME 2025 math problems, Phi-4-Reasoning shows an improvement of more than 50 percentage points compared to the base Phi-4, and on LiveCodeBench programming tasks, it improves by over 25 percentage points. A notable feature of Phi-4-Reasoning is its ability to generalize and transfer knowledge to tasks not explicitly included in the training data, with improvements also observed on general tasks, including instruction following and toxic content detection.

Phi-4-Reasoning is ideally suited for applications that demand reliable logical reasoning under limited computational resources—such as educational platforms for solving mathematical problems, automated programming systems, scientific research tools, and any applications where a balance between high-quality reasoning and resource efficiency is essential.


Announce Date: 30.04.2025
Parameters: 15B
Context: 33K
Layers: 40
Attention Type: Full Attention
Developer: Microsoft
Transformers Version: 4.51.1
License: MIT

Public endpoint

Use our pre-built public endpoints for free to test inference and explore Phi-4-reasoning capabilities. You can obtain an API access token on the token management page after registration and verification.
Model Name Context Type GPU Status Link
There are no public endpoints for this model yet.

Private server

Rent your own physically dedicated instance with hourly or long-term monthly billing.

We recommend deploying private instances in the following scenarios:

  • maximize endpoint performance,
  • enable full context for long sequences,
  • ensure top-tier security for data processing in an isolated, dedicated environment,
  • use custom weights, such as fine-tuned models or LoRA adapters.

Recommended server configurations for hosting Phi-4-reasoning

Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa2-2.16.32.160
32,768.0
tensor
2 $0.57 1.377 Launch
teslaa10-2.16.64.160
32,768.0
tensor
2 $0.93 54.68 3.702 Launch
rtx3090-2.16.64.160
32,768.0
tensor
2 $1.56 62.54 4.200 Launch
rtx3090-2.16.64.160.nvlink
32,768.0
tensor
2 $1.56 4.200 Launch
rtx5090-1.16.64.160
32,768.0
1 $1.59 1.973 Launch
rtx3080-4.16.64.160
32,768.0
pipeline
4 $1.82 2.177 Launch
rtx4090-2.16.64.160
32,768.0
tensor
2 $1.92 80.98 4.003 Launch
teslaa100-1.16.64.160
32,768.0
1 $2.37 83.01 9.024 Launch
h100-1.16.64.160
32,768.0
1 $3.83 84.90 8.977 Launch
h100nvl-1.16.96.160
32,768.0
1 $4.11 11.061 Launch
teslaa100-2.24.96.160.nvlink
32,768.0
tensor
2 $4.61 20.410 Launch
h200-1.16.128.160
32,768.0
1 $4.74 17.937 Launch
h200-2.24.256.160.nvlink
32,768.0
tensor
2 $9.40 38.236 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa10-2.16.64.160
32,768.0
tensor
2 $0.93 1.624 Launch
teslaa2-3.32.128.160
32,768.0
pipeline
3 $1.06 5.77 1.118 Launch
rtx3090-2.16.64.160
32,768.0
tensor
2 $1.56 1.936 Launch
rtx3090-2.16.64.160.nvlink
32,768.0
tensor
2 $1.56 1.936 Launch
rtx4090-2.16.64.160
32,768.0
tensor
2 $1.92 58.27 1.924 Launch
teslaa100-1.16.64.160
32,768.0
1 $2.37 48.84 6.974 Launch
rtx5090-2.16.64.160
32,768.0
tensor
2 $2.93 4.229 Launch
h100-1.16.64.160
32,768.0
1 $3.83 57.95 6.964 Launch
h100nvl-1.16.96.160
32,768.0
1 $4.11 9.012 Launch
teslaa100-2.24.96.160.nvlink
32,768.0
tensor
2 $4.61 18.331 Launch
h200-1.16.128.160
32,768.0
1 $4.74 15.887 Launch
h200-2.24.256.160.nvlink
32,768.0
tensor
2 $9.40 36.157 Launch

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