Mercury 3 vs Phi-4-multimodal-instruct
Mercury 3 leads the LLM Stats Score 36.1 to 2.3. Phi-4-multimodal-instruct is 2.6x cheaper per token.
Inception · Microsoft · Updated for 2026
Which is better?
Mercury 3 leads the overall LLM Stats Score 36.1 to 2.3, ranking #98 overall.
On price, Phi-4-multimodal-instruct is roughly 2.6x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Mercury 3 also accepts a larger context window (1,000,000 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 Mercury 3
- overall performance matters — it scores 36.1 and ranks #98 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you process long inputs — it offers a 1,000,000 token context window
- you want the most recent training data — it shipped Oct 2026
Choose Phi-4-multimodal-instruct
- cost matters — it's about 2.6x cheaper per token
- you need open weights you can self-host or fine-tune
At a glance
The differences that matter most.
Individual benchmarks
12 reported for Mercury 3 · 15 for Phi-4-multimodal-instruct
Mercury 3 and Phi-4-multimodal-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
Pricing Analysis
Price comparison per million tokens
For input processing, Mercury 3 ($0.10/1M tokens) is 2.0x more expensive than Phi-4-multimodal-instruct ($0.05/1M tokens).
For output processing, Mercury 3 ($0.35/1M tokens) is 3.5x more expensive than Phi-4-multimodal-instruct ($0.10/1M tokens).
In conclusion, Mercury 3 is more expensive than Phi-4-multimodal-instruct.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Mercury 3 accepts 1,000,000 input tokens compared to Phi-4-multimodal-instruct's 128,000 tokens. Mercury 3 can generate longer responses up to 131,072 tokens, while Phi-4-multimodal-instruct is limited to 128,000 tokens.
Input capabilities
Documented input modalities across available providers
Phi-4-multimodal-instruct supports multimodal inputs, whereas Mercury 3 does not.
Phi-4-multimodal-instruct can handle both text and other forms of data like images, making it suitable for multimodal applications.
Mercury 3
Phi-4-multimodal-instruct
License
Usage and distribution terms
Mercury 3 is licensed under a proprietary license, while Phi-4-multimodal-instruct uses MIT.
License differences may affect how you can use these models in commercial or open-source projects.
Proprietary
Closed source
MIT
Open weights
Release Timeline
When each model was launched
Mercury 3 was released on 2026-10-19, while Phi-4-multimodal-instruct was released on 2025-02-01.
Mercury 3 is 21 months newer than Phi-4-multimodal-instruct.
Oct 19, 2026
0 days ago
1.7yr newerFeb 1, 2025
1.7 years ago
Knowledge Cutoff
When training data ends
Phi-4-multimodal-instruct has a documented knowledge cutoff of 2024-06-01, while Mercury 3'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 Mercury 3's cutoff date.
—
Jun 2024
Provider Availability
Mercury 3 is available from Inception. Phi-4-multimodal-instruct is available from DeepInfra.
Mercury 3
Phi-4-multimodal-instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against Mercury 3 and Phi-4-multimodal-instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about Mercury 3 vs Phi-4-multimodal-instruct.