Model Comparison

Comprehensive side-by-side analysis of model capabilities and performance

Moonshot AI

Kimi-k1.5

Moonshot AI

Kimi-k1.5 is a multimodal language model developed by Moonshot AI. This model demonstrates exceptional performance with an average score of 81.7% across 9 benchmarks. It excels particularly in MATH-500 (96.2%), CLUEWSC (91.4%), C-Eval (88.3%). The model shows particular specialization in math tasks with an average performance of 85.5%. As a multimodal model, it can process and understand text, images, and other input formats seamlessly. Released in 2025, it represents Moonshot AI's latest advancement in AI technology.

Microsoft

Phi-4-multimodal-instruct

Microsoft

Phi-4-multimodal-instruct is a multimodal language model developed by Microsoft. It achieves strong performance with an average score of 72.0% across 15 benchmarks. It excels particularly in ScienceQA Visual (97.5%), DocVQA (93.2%), MMBench (86.7%). The model shows particular specialization in general tasks with an average performance of 75.8%. It supports a 256K token context window for handling large documents. The model is available through 1 API provider. As a multimodal model, it can process and understand text, images, and other input formats seamlessly. It's licensed for commercial use, making it suitable for enterprise applications. Released in 2025, it represents Microsoft's latest advancement in AI technology.

Moonshot AI

Kimi-k1.5

Moonshot AI

2025-01-20

Microsoft

Phi-4-multimodal-instruct

Microsoft

2025-02-01

12 days newer

Performance Metrics

Context window and performance specifications

Moonshot AI

Kimi-k1.5

Max Context:-
Microsoft

Phi-4-multimodal-instruct

Larger context
Max Context:256.0K
Parameters:5.6B

Average performance across 2 common benchmarks

Moonshot AI

Kimi-k1.5

+13.7%
Average Score:72.4%
Microsoft

Phi-4-multimodal-instruct

Average Score:58.8%

Performance comparison across key benchmark categories

Moonshot AI

Kimi-k1.5

math
+23.1%
85.5%
general
+9.7%
85.4%
vision
+0.3%
70.0%
Microsoft

Phi-4-multimodal-instruct

math
62.4%
general
75.8%
vision
69.7%
Benchmark Scores - Detailed View
Side-by-side comparison of all benchmark scores
Knowledge Cutoff
Training data recency comparison

Phi-4-multimodal-instruct

2024-06-01

More recent knowledge cutoff means awareness of newer technologies and frameworks

Provider Availability & Performance

Available providers and their performance metrics

Moonshot AI

Kimi-k1.5

0 providers
Microsoft

Phi-4-multimodal-instruct

1 providers

DeepInfra

Throughput: 25 tok/s
Latency: 0.5ms
Moonshot AI

Kimi-k1.5

+13.7%
Avg Score:72.4%
Providers:0
Microsoft

Phi-4-multimodal-instruct

Avg Score:58.8%
Providers:1