Ling 3.0 Flash Fin vs Phi-4-multimodal-instruct
Ling 3.0 Flash Fin leads the LLM Stats Score 43.3 to 2.9. Phi-4-multimodal-instruct is 1.4x cheaper per token.
InclusionAI · Microsoft · Updated for 2026
Which is better?
Ling 3.0 Flash Fin leads the overall LLM Stats Score 43.3 to 2.9, ranking #39 overall.
On price, Phi-4-multimodal-instruct is roughly 1.4x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Ling 3.0 Flash Fin also accepts a larger context window (262,144 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 Ling 3.0 Flash Fin
- overall performance matters — it scores 43.3 and ranks #39 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you process long inputs — it offers a 262,144 token context window
- you want the most recent training data — it shipped Sep 2026
Choose Phi-4-multimodal-instruct
- cost matters — it's about 1.4x cheaper per token
- you need open weights you can self-host or fine-tune
At a glance
The differences that matter most.
Individual benchmarks
6 reported for Ling 3.0 Flash Fin · 15 for Phi-4-multimodal-instruct
Ling 3.0 Flash Fin 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, Ling 3.0 Flash Fin ($0.06/1M tokens) is 1.2x more expensive than Phi-4-multimodal-instruct ($0.05/1M tokens).
For output processing, Ling 3.0 Flash Fin ($0.18/1M tokens) is 1.8x more expensive than Phi-4-multimodal-instruct ($0.10/1M tokens).
In conclusion, Ling 3.0 Flash Fin is more expensive than Phi-4-multimodal-instruct.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Ling 3.0 Flash Fin has 118.4B more parameters than Phi-4-multimodal-instruct, making it 2114.3% larger.
Context Window
Maximum input and output token capacity
Ling 3.0 Flash Fin accepts 262,144 input tokens compared to Phi-4-multimodal-instruct's 128,000 tokens. Ling 3.0 Flash Fin can generate longer responses up to 262,144 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 Ling 3.0 Flash Fin does not.
Phi-4-multimodal-instruct can handle both text and other forms of data like images, making it suitable for multimodal applications.
Ling 3.0 Flash Fin
Phi-4-multimodal-instruct
Release Timeline
When each model was launched
Ling 3.0 Flash Fin was released on 2026-09-03, while Phi-4-multimodal-instruct was released on 2025-02-01.
Ling 3.0 Flash Fin is 19 months newer than Phi-4-multimodal-instruct.
Sep 3, 2026
5 days ago
1.6yr newerFeb 1, 2025
1.6 years ago
Knowledge Cutoff
When training data ends
Phi-4-multimodal-instruct has a documented knowledge cutoff of 2024-06-01, while Ling 3.0 Flash Fin'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 Ling 3.0 Flash Fin's cutoff date.
—
Jun 2024
Provider Availability
Ling 3.0 Flash Fin is available from DeepInfra. Phi-4-multimodal-instruct is available from DeepInfra.
Ling 3.0 Flash Fin
Phi-4-multimodal-instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against Ling 3.0 Flash Fin and Phi-4-multimodal-instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about Ling 3.0 Flash Fin vs Phi-4-multimodal-instruct.