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Gemini 4 Argon vs Phi-3.5-mini-instruct

Gemini 4 Argon leads the LLM Stats Score 55.1 to -3.8.

Google · Microsoft · Updated for 2026

Which is better?

Gemini 4 Argon leads the overall LLM Stats Score 55.1 to -3.8, ranking #4 overall.

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

Choose Gemini 4 Argon

  • overall performance matters — it scores 55.1 and ranks #4 on LLM Stats
  • your work emphasizes reasoning and coding — it leads those capability indexes
  • you want the most recent training data — it shipped Sep 2026

Choose Phi-3.5-mini-instruct

  • you need open weights you can self-host or fine-tune

At a glance

The differences that matter most.

Gemini 4 Argon
Google
Phi-3.5-mini-instruct
Microsoft
Core performance indexes
LLM Stats Score
55.1
#4
-3.8
#362
Reasoning
52.4
#7
-4.7
#358
Coding
44.1
#4
-6.9
#276
Cost, coverage & limits
Benchmark wins
—
—
Input price
— / M
$0.10 / M
Output price
— / M
$0.10 / M
Context window
—
128,000

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
Gemini 4 Argon
Phi-3.5-mini-instruct
Long context
30.6#3
7.9#91
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

19 reported for Gemini 4 Argon · 31 for Phi-3.5-mini-instruct

No common benchmarks found

Gemini 4 Argon and Phi-3.5-mini-instructdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Human preference

Blind head-to-head votes and playground preference scores

Context Window

Maximum input and output token capacity

Only Phi-3.5-mini-instruct specifies input context (128,000 tokens). Only Phi-3.5-mini-instruct specifies output context (128,000 tokens).

Google
Gemini 4 Argon
Input- tokens
Output- tokens
Microsoft
Phi-3.5-mini-instruct
Input128,000 tokens
Output128,000 tokens
Thu Oct 08 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Gemini 4 Argon supports multimodal inputs, whereas Phi-3.5-mini-instruct does not.

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

Gemini 4 Argon

Text
Images
Audio
Video

Phi-3.5-mini-instruct

Text
Images
Audio
Video

License

Usage and distribution terms

Gemini 4 Argon is licensed under a proprietary license, while Phi-3.5-mini-instruct uses MIT.

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

Gemini 4 Argon

Proprietary

Closed source

Phi-3.5-mini-instruct

MIT

Open weights

Release Timeline

When each model was launched

Gemini 4 Argon was released on 2026-09-30, while Phi-3.5-mini-instruct was released on 2024-08-23.

Gemini 4 Argon is 26 months newer than Phi-3.5-mini-instruct.

Gemini 4 Argon

Sep 30, 2026

1 weeks ago

2.1yr newer
Phi-3.5-mini-instruct

Aug 23, 2024

2.1 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

Outputs Comparison

Notice missing or incorrect data?

Judge for yourself.

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

Gemini 4 Argon
✓ Preferred
Phi-3.5-mini-instruct
Open in Playground

FAQ

Common questions about Gemini 4 Argon vs Phi-3.5-mini-instruct.

Which is better, Gemini 4 Argon or Phi-3.5-mini-instruct?

Gemini 4 Argon leads the LLM Stats Score 55.1 to -3.8. Gemini 4 Argon is made by Google 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 Gemini 4 Argon compare to Phi-3.5-mini-instruct in benchmarks?

Gemini 4 Argon scores Graphwalks BFS <128k: 99.7%, Vibe Code Bench: 91.9%, LVBench: 91.7%, LABBench2: 88.8%, Graphwalks BFS >128k: 84.2%. Phi-3.5-mini-instruct scores GSM8k: 86.2%, ARC-C: 84.6%, RULER: 84.1%, PIQA: 81.0%, OpenBookQA: 79.2%.

What are the context window sizes for Gemini 4 Argon and Phi-3.5-mini-instruct?

Gemini 4 Argon supports an unknown number of 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 Gemini 4 Argon and Phi-3.5-mini-instruct?

Key differences include LLM Stats Score (55.1 vs -3.8), multimodal support (yes vs no), licensing (Proprietary vs MIT). See the full comparison above for benchmark-by-benchmark results.

Who makes Gemini 4 Argon and Phi-3.5-mini-instruct?

Gemini 4 Argon is developed by Google and Phi-3.5-mini-instruct is developed by Microsoft.

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