Inkling 2 Small vs Phi 4
Inkling 2 Small leads the LLM Stats Score 47.1 to 4.8. Phi 4 is 5.4x cheaper per token.
Thinking Machines Lab · Microsoft · Updated for 2026
Which is better?
Inkling 2 Small leads the overall LLM Stats Score 47.1 to 4.8, ranking #40 overall.
In the 1 individual benchmarks reported for both models, Inkling 2 Small wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, Phi 4 is roughly 5.4x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Inkling 2 Small also accepts a larger context window (256,000 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 Inkling 2 Small
- overall performance matters — it scores 47.1 and ranks #40 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- you process long inputs — it offers a 256,000 token context window
- you want the most recent training data — it shipped Nov 2026
Choose Phi 4
- cost matters — it's about 5.4x cheaper per token
- you need open weights you can self-host or fine-tune
At a glance
The differences that matter most.
Individual benchmarks
11 reported for Inkling 2 Small · 13 for Phi 4
Inkling 2 Small outperforms in 1 benchmarks (GPQA), while Phi 4 is better at 0 benchmarks.
Inkling 2 Small significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, Inkling 2 Small ($0.30/1M tokens) is 4.3x more expensive than Phi 4 ($0.07/1M tokens).
For output processing, Inkling 2 Small ($1.00/1M tokens) is 7.1x more expensive than Phi 4 ($0.14/1M tokens).
In conclusion, Inkling 2 Small is more expensive than Phi 4.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Inkling 2 Small has 579.3B more parameters than Phi 4, making it 3940.8% larger.
Context Window
Maximum input and output token capacity
Inkling 2 Small accepts 256,000 input tokens compared to Phi 4's 16,384 tokens. Only Phi 4 specifies output context (16,384 tokens).
Input capabilities
Documented input modalities across available providers
Inkling 2 Small supports multimodal inputs, whereas Phi 4 does not.
Inkling 2 Small can handle both text and other forms of data like images, making it suitable for multimodal applications.
Inkling 2 Small
Phi 4
License
Usage and distribution terms
Inkling 2 Small is licensed under a proprietary license, while Phi 4 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
Inkling 2 Small was released on 2026-11-05, while Phi 4 was released on 2024-12-12.
Inkling 2 Small is 23 months newer than Phi 4.
Nov 5, 2026
4 days ago
1.9yr newerDec 12, 2024
1.9 years ago
Knowledge Cutoff
When training data ends
Phi 4 has a documented knowledge cutoff of 2024-06-01, while Inkling 2 Small's cutoff date is not specified.
We can confirm Phi 4's training data extends to 2024-06-01, but cannot make a direct comparison without Inkling 2 Small's cutoff date.
—
Jun 2024
Provider Availability
Inkling 2 Small is available from Thinking Machines Lab. Phi 4 is available from DeepInfra.
Inkling 2 Small
Phi 4
Outputs Comparison
Judge for yourself.
Run your own prompts against Inkling 2 Small and Phi 4 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Inkling 2 Small vs Phi 4.