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Kimi K2.8 Preview vs Phi-3.5-MoE-instruct

Comparing Kimi K2.8 Preview and Phi-3.5-MoE-instruct across benchmarks, pricing, and capabilities.

Moonshot AI · Microsoft · Updated for 2026

Which is better?

Kimi K2.8 Preview and Phi-3.5-MoE-instruct trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.

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

Choose Kimi K2.8 Preview

  • you want the most recent training data — it shipped Sep 2026

Choose Phi-3.5-MoE-instruct

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

At a glance

The differences that matter most.

Benchmark wins
Input price
— / M
— / M
Output price
— / M
— / M
Context window
1,048,576

Individual benchmarks

0 reported for Kimi K2.8 Preview · 31 for Phi-3.5-MoE-instruct

No common benchmarks found

Kimi K2.8 Preview and Phi-3.5-MoE-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 Kimi K2.8 Preview specifies input context (1,048,576 tokens).

Moonshot AI
Kimi K2.8 Preview
Input1,048,576 tokens
Output- tokens
Microsoft
Phi-3.5-MoE-instruct
Input- tokens
Output- tokens
Mon Sep 14 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Kimi K2.8 Preview supports multimodal inputs, whereas Phi-3.5-MoE-instruct does not.

Kimi K2.8 Preview can handle both text and other forms of data like images, making it suitable for multimodal applications.

Kimi K2.8 Preview

Text
Images
Audio
Video

Phi-3.5-MoE-instruct

Text
Images
Audio
Video

License

Usage and distribution terms

Kimi K2.8 Preview is licensed under a proprietary license, while Phi-3.5-MoE-instruct uses MIT.

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

Kimi K2.8 Preview

Proprietary

Closed source

Phi-3.5-MoE-instruct

MIT

Open weights

Release Timeline

When each model was launched

Kimi K2.8 Preview was released on 2026-09-11, while Phi-3.5-MoE-instruct was released on 2024-08-23.

Kimi K2.8 Preview is 25 months newer than Phi-3.5-MoE-instruct.

Kimi K2.8 Preview

Sep 11, 2026

3 days ago

2.1yr newer
Phi-3.5-MoE-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?Start an Issue discussion

Judge for yourself.

Run your own prompts against Kimi K2.8 Preview and Phi-3.5-MoE-instruct side-by-side, then vote on the output you prefer.

Kimi K2.8 Preview
✓ Preferred
Phi-3.5-MoE-instruct
Open in Playground

FAQ

Common questions about Kimi K2.8 Preview vs Phi-3.5-MoE-instruct.

Which is better, Kimi K2.8 Preview or Phi-3.5-MoE-instruct?

Kimi K2.8 Preview (Moonshot AI) and Phi-3.5-MoE-instruct (Microsoft) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

How does Kimi K2.8 Preview compare to Phi-3.5-MoE-instruct in benchmarks?

Phi-3.5-MoE-instruct scores ARC-C: 91.0%, OpenBookQA: 89.6%, GSM8k: 88.7%, PIQA: 88.6%, RULER: 87.1%.

What are the context window sizes for Kimi K2.8 Preview and Phi-3.5-MoE-instruct?

Kimi K2.8 Preview supports 1.0M tokens and Phi-3.5-MoE-instruct 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 Kimi K2.8 Preview and Phi-3.5-MoE-instruct?

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

Who makes Kimi K2.8 Preview and Phi-3.5-MoE-instruct?

Kimi K2.8 Preview is developed by Moonshot AI and Phi-3.5-MoE-instruct is developed by Microsoft.