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Llama 4 Scout vs Phi 4 Reasoning

Llama 4 Scout and Phi 4 Reasoning are closely matched at 7.8 and 12.0 on the LLM Stats Score.

Meta · Microsoft · Updated for 2026

Which is better?

Llama 4 Scout and Phi 4 Reasoning are closely matched on the overall LLM Stats Score at 7.8 and 12.0.

In the 3 individual benchmarks reported for both models, Phi 4 Reasoning wins 3; this is a narrower head-to-head signal than the composite indexes.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose Llama 4 Scout

  • you want predictable pricing at $0.08/M input and $0.30/M output

Choose Phi 4 Reasoning

  • your work emphasizes reasoning — it leads those capability indexes
  • you value its reported benchmark strengths — it wins 3 of 3 exact shared results
  • you want the most recent training data — it shipped Apr 2025

At a glance

The differences that matter most.

Core performance indexes
7.8
#282
12.0
#253
6.6
#285
12.6
#242
0.4
#239
6.4
#189
Cost, coverage & limits
Benchmark wins
0 of 3
3 of 3
Input price
$0.08 / M
— / M
Output price
$0.30 / M
— / M
Context window
10,000,000

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
Llama 4 Scout
Phi 4 Reasoning
12.8#231
13.6#226
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

12 reported for Llama 4 Scout · 11 for Phi 4 Reasoning

3 shared

Llama 4 Scout outperforms in 0 benchmarks, while Phi 4 Reasoning is better at 2 benchmarks (GPQA, LiveCodeBench).

Phi 4 Reasoning shows notably better performance in the majority of benchmarks.

Mon Sep 14 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

95.0B diff

Llama 4 Scout has 95.0B more parameters than Phi 4 Reasoning, making it 678.6% larger.

Meta
Llama 4 Scout
109.0Bparameters
Microsoft
Phi 4 Reasoning
14.0Bparameters
109.0B
Llama 4 Scout
14.0B
Phi 4 Reasoning

Context Window

Maximum input and output token capacity

Only Llama 4 Scout specifies input context (10,000,000 tokens). Only Llama 4 Scout specifies output context (10,000,000 tokens).

Meta
Llama 4 Scout
Input10,000,000 tokens
Output10,000,000 tokens
Microsoft
Phi 4 Reasoning
Input- tokens
Output- tokens
Mon Sep 14 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Llama 4 Scout supports multimodal inputs, whereas Phi 4 Reasoning does not.

Llama 4 Scout can handle both text and other forms of data like images, making it suitable for multimodal applications.

Llama 4 Scout

Text
Images
Audio
Video

Phi 4 Reasoning

Text
Images
Audio
Video

License

Usage and distribution terms

Llama 4 Scout is licensed under Llama 4 Community License Agreement, while Phi 4 Reasoning uses MIT.

License differences may affect how you can use these models in commercial or open-source projects.

Llama 4 Scout

Llama 4 Community License Agreement

Open weights

Phi 4 Reasoning

MIT

Open weights

Release Timeline

When each model was launched

Llama 4 Scout was released on 2025-04-05, while Phi 4 Reasoning was released on 2025-04-30.

Phi 4 Reasoning is 1 month newer than Llama 4 Scout.

Llama 4 Scout

Apr 5, 2025

1.4 years ago

Phi 4 Reasoning

Apr 30, 2025

1.4 years ago

3w newer

Knowledge Cutoff

When training data ends

Phi 4 Reasoning has a documented knowledge cutoff of 2025-03-01, while Llama 4 Scout's cutoff date is not specified.

We can confirm Phi 4 Reasoning's training data extends to 2025-03-01, but cannot make a direct comparison without Llama 4 Scout's cutoff date.

Llama 4 Scout

Phi 4 Reasoning

Mar 2025

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion

Judge for yourself.

Run your own prompts against Llama 4 Scout and Phi 4 Reasoning side-by-side, then vote on the output you prefer.

Llama 4 Scout
✓ Preferred
Phi 4 Reasoning
Open in Playground

FAQ

Common questions about Llama 4 Scout vs Phi 4 Reasoning.

Which is better, Llama 4 Scout or Phi 4 Reasoning?

Llama 4 Scout and Phi 4 Reasoning are closely matched on the LLM Stats Score at 7.8 and 12.0. Llama 4 Scout is made by Meta and Phi 4 Reasoning is made by Microsoft. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does Llama 4 Scout compare to Phi 4 Reasoning in benchmarks?

Llama 4 Scout scores DocVQA: 94.4%, MGSM: 90.6%, ChartQA: 88.8%, MMLU: 79.6%, MMLU-Pro: 74.3%. Phi 4 Reasoning scores FlenQA: 97.7%, HumanEval+: 92.9%, IFEval: 83.4%, OmniMath: 76.6%, AIME 2024: 75.3%.

What are the context window sizes for Llama 4 Scout and Phi 4 Reasoning?

Llama 4 Scout supports 10.0M tokens and Phi 4 Reasoning supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Llama 4 Scout and Phi 4 Reasoning?

Key differences include LLM Stats Score (7.8 vs 12.0), multimodal support (yes vs no), licensing (Llama 4 Community License Agreement vs MIT). See the full comparison above for benchmark-by-benchmark results.

Who makes Llama 4 Scout and Phi 4 Reasoning?

Llama 4 Scout is developed by Meta and Phi 4 Reasoning is developed by Microsoft.