Model Comparison
DeepSeek-R1 vs Phi-4-multimodal-instructWhich is better in 2026?
Comparing DeepSeek-R1 and Phi-4-multimodal-instruct across benchmarks, pricing, and capabilities.
Verdict: DeepSeek-R1 vs Phi-4-multimodal-instruct — which is better?
DeepSeek-R1 (by DeepSeek) and Phi-4-multimodal-instruct (by Microsoft) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.
On price, Phi-4-multimodal-instruct is roughly 15.4x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-R1 also accepts a larger context window (131,072 input tokens), making it the stronger choice for long documents and large codebases.
Choose DeepSeek-R1 if…
- you process long inputs — it offers a 131,072 token context window
Choose Phi-4-multimodal-instruct if…
- cost matters — it's about 15.4x cheaper per token
- you want the most recent training data — it shipped Feb 2025
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-R1 and Phi-4-multimodal-instructdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-R1 ($0.55/1M tokens) is 11.0x more expensive than Phi-4-multimodal-instruct ($0.05/1M tokens).
For output processing, DeepSeek-R1 ($2.19/1M tokens) is 21.9x more expensive than Phi-4-multimodal-instruct ($0.10/1M tokens).
In conclusion, DeepSeek-R1 is more expensive than Phi-4-multimodal-instruct.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-R1 has 665.4B more parameters than Phi-4-multimodal-instruct, making it 11882.1% larger.
Context Window
Maximum input and output token capacity
DeepSeek-R1 accepts 131,072 input tokens compared to Phi-4-multimodal-instruct's 128,000 tokens. DeepSeek-R1 can generate longer responses up to 131,072 tokens, while Phi-4-multimodal-instruct is limited to 128,000 tokens.
Input Capabilities
Supported data types and modalities
Phi-4-multimodal-instruct supports multimodal inputs, whereas DeepSeek-R1 does not.
Phi-4-multimodal-instruct can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-R1
Phi-4-multimodal-instruct
License
Usage and distribution terms
Both models are licensed under MIT.
Both models share the same licensing terms, providing consistent usage rights.
MIT
Open weights
MIT
Open weights
Release Timeline
When each model was launched
DeepSeek-R1 was released on 2025-01-20, while Phi-4-multimodal-instruct was released on 2025-02-01.
Phi-4-multimodal-instruct is 0 month newer than DeepSeek-R1.
Jan 20, 2025
1.4 years ago
Feb 1, 2025
1.4 years ago
1w newerKnowledge Cutoff
When training data ends
Phi-4-multimodal-instruct has a documented knowledge cutoff of 2024-06-01, while DeepSeek-R1's cutoff date is not specified.
We can confirm Phi-4-multimodal-instruct's training data extends to 2024-06-01, but cannot make a direct comparison without DeepSeek-R1's cutoff date.
—
Jun 2024
Provider Availability
DeepSeek-R1 is available from DeepSeek, DeepInfra, Together, Fireworks. Phi-4-multimodal-instruct is available from DeepInfra.
DeepSeek-R1
Phi-4-multimodal-instruct
Outputs Comparison
Key Takeaways
DeepSeek-R1
View detailsDeepSeek
Detailed Comparison
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FAQ
Common questions about DeepSeek-R1 vs Phi-4-multimodal-instruct.