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Llama 4 Scout vs Phi-3.5-mini-instruct

Llama 4 Scout leads the LLM Stats Score 7.8 to -3.8. Phi-3.5-mini-instruct is 1.4x cheaper per token.

Meta · Microsoft · Updated for 2026

Which is better?

Llama 4 Scout leads the overall LLM Stats Score 7.8 to -3.8, ranking #299 overall.

In the 6 individual benchmarks reported for both models, Llama 4 Scout wins 5; this is a narrower head-to-head signal than the composite indexes.

On price, Phi-3.5-mini-instruct is roughly 1.4x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

Llama 4 Scout also accepts a larger context window (10,000,000 input tokens), making it the stronger choice for long documents and large codebases.

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

Choose Llama 4 Scout

  • overall performance matters — it scores 7.8 and ranks #299 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • you value its reported benchmark strengths — it wins 5 of 6 exact shared results
  • you process long inputs — it offers a 10,000,000 token context window
  • you want the most recent training data — it shipped Apr 2025

Choose Phi-3.5-mini-instruct

  • cost matters — it's about 1.4x cheaper per token

At a glance

The differences that matter most.

Llama 4 Scout
Meta
Phi-3.5-mini-instruct
Microsoft
Core performance indexes
LLM Stats Score
7.8
#299
-3.8
#362
Reasoning
6.6
#302
-4.7
#358
Coding
0.3
#255
-6.9
#276
Cost, coverage & limits
Benchmark wins
5 of 6
1 of 6
Input price
$0.08 / M
$0.10 / M
Output price
$0.30 / M
$0.10 / M
Context window
10,000,000
128,000

Capability indexes

Additional strengths measured across groups of related public benchmarks

3 shared
Index
Llama 4 Scout
Phi-3.5-mini-instruct
Math
12.8#238
-1.5#310
Finance
10.2#146
0.6#198
Legal
10.2#130
0.6#186
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

12 reported for Llama 4 Scout · 31 for Phi-3.5-mini-instruct

6 shared

Llama 4 Scout outperforms in 5 benchmarks (GPQA, MATH, MGSM, MMLU, MMLU-Pro), while Phi-3.5-mini-instruct is better at 1 benchmark (MBPP).

Llama 4 Scout significantly outperforms across most benchmarks.

Mon Oct 19 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Pricing Analysis

Price comparison per million tokens

Phi-3.5-mini-instruct costs less

For input processing, Llama 4 Scout ($0.08/1M tokens) is 1.3x cheaper than Phi-3.5-mini-instruct ($0.10/1M tokens).

For output processing, Llama 4 Scout ($0.30/1M tokens) is 3.0x more expensive than Phi-3.5-mini-instruct ($0.10/1M tokens).

In conclusion, Llama 4 Scout is more expensive than Phi-3.5-mini-instruct.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Mon Oct 19 2026 • llm-stats.com
Meta
Llama 4 Scout
Input tokens$0.08
Output tokens$0.30
Best providerDeepinfra
Microsoft
Phi-3.5-mini-instruct
Input tokens$0.10
Output tokens$0.10
Best providerAzure
Notice missing or incorrect data?

Model Size

Parameter count comparison

105.2B diff

Llama 4 Scout has 105.2B more parameters than Phi-3.5-mini-instruct, making it 2768.4% larger.

Meta
Llama 4 Scout
109.0Bparameters
Microsoft
Phi-3.5-mini-instruct
3.8Bparameters
109.0B
Llama 4 Scout
3.8B
Phi-3.5-mini-instruct

Context Window

Maximum input and output token capacity

Llama 4 Scout accepts 10,000,000 input tokens compared to Phi-3.5-mini-instruct's 128,000 tokens. Llama 4 Scout can generate longer responses up to 10,000,000 tokens, while Phi-3.5-mini-instruct is limited to 128,000 tokens.

Meta
Llama 4 Scout
Input10,000,000 tokens
Output10,000,000 tokens
Microsoft
Phi-3.5-mini-instruct
Input128,000 tokens
Output128,000 tokens
Mon Oct 19 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Llama 4 Scout supports multimodal inputs, whereas Phi-3.5-mini-instruct 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-3.5-mini-instruct

Text
Images
Audio
Video

License

Usage and distribution terms

Llama 4 Scout is licensed under Llama 4 Community License Agreement, while Phi-3.5-mini-instruct 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-3.5-mini-instruct

MIT

Open weights

Release Timeline

When each model was launched

Llama 4 Scout was released on 2025-04-05, while Phi-3.5-mini-instruct was released on 2024-08-23.

Llama 4 Scout is 8 months newer than Phi-3.5-mini-instruct.

Llama 4 Scout

Apr 5, 2025

1.5 years ago

7mo newer
Phi-3.5-mini-instruct

Aug 23, 2024

2.2 years ago

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Provider Availability

Llama 4 Scout is available from DeepInfra, Lambda, Novita, Groq, Fireworks, Together. Phi-3.5-mini-instruct is available from Azure.

Llama 4 Scout

deepinfra logo
Deepinfra
Input Price:Input: $0.08/1MOutput Price:Output: $0.30/1M
lambda logo
Lambda
Input Price:Input: $0.08/1MOutput Price:Output: $0.30/1M
novita logo
Novita
Input Price:Input: $0.10/1MOutput Price:Output: $0.50/1M
groq logo
Groq
Input Price:Input: $0.11/1MOutput Price:Output: $0.34/1M
fireworks logo
Fireworks
Input Price:Input: $0.15/1MOutput Price:Output: $0.60/1M
together logo
Together
Input Price:Input: $0.18/1MOutput Price:Output: $0.59/1M

Phi-3.5-mini-instruct

azure logo
Azure
Input Price:Input: $0.10/1MOutput Price:Output: $0.10/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?

Judge for yourself.

Run your own prompts against Llama 4 Scout and Phi-3.5-mini-instruct side-by-side, then vote on the output you prefer.

Llama 4 Scout
✓ Preferred
Phi-3.5-mini-instruct
Open in Playground

FAQ

Common questions about Llama 4 Scout vs Phi-3.5-mini-instruct.

Which is better, Llama 4 Scout or Phi-3.5-mini-instruct?

Llama 4 Scout leads the LLM Stats Score 7.8 to -3.8. Llama 4 Scout is made by Meta and Phi-3.5-mini-instruct 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-3.5-mini-instruct in benchmarks?

Llama 4 Scout scores DocVQA: 94.4%, MGSM: 90.6%, ChartQA: 88.8%, MMLU: 79.6%, MMLU-Pro: 74.3%. Phi-3.5-mini-instruct scores GSM8k: 86.2%, ARC-C: 84.6%, RULER: 84.1%, PIQA: 81.0%, OpenBookQA: 79.2%.

Is Llama 4 Scout cheaper than Phi-3.5-mini-instruct?

Llama 4 Scout is 1.3x cheaper for input tokens. Llama 4 Scout costs $0.08/M input and $0.30/M output via deepinfra. Phi-3.5-mini-instruct costs $0.10/M input and $0.10/M output via azure.

What are the context window sizes for Llama 4 Scout and Phi-3.5-mini-instruct?

Llama 4 Scout supports 10.0M tokens and Phi-3.5-mini-instruct supports 128K 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-3.5-mini-instruct?

Key differences include LLM Stats Score (7.8 vs -3.8), context window (10.0M vs 128K), input pricing ($0.08 vs $0.10/M), 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-3.5-mini-instruct?

Llama 4 Scout is developed by Meta and Phi-3.5-mini-instruct is developed by Microsoft.

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