GLM-4.6 vs Phi 4
GLM-4.6 leads the LLM Stats Score 29.0 to 5.4. Phi 4 is 10.0x cheaper per token.
Zhipu AI · Microsoft · Updated for 2026
Which is better?
GLM-4.6 leads the overall LLM Stats Score 29.0 to 5.4, ranking #130 overall.
In the 1 individual benchmarks reported for both models, GLM-4.6 wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, Phi 4 is roughly 10.0x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GLM-4.6 also accepts a larger context window (202,752 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 GLM-4.6
- overall performance matters — it scores 29.0 and ranks #130 on LLM Stats
- your work emphasizes reasoning — 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 202,752 token context window
- you want the most recent training data — it shipped Sep 2025
Choose Phi 4
- cost matters — it's about 10.0x cheaper per token
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
7 reported for GLM-4.6 · 13 for Phi 4
GLM-4.6 outperforms in 1 benchmarks (GPQA), while Phi 4 is better at 0 benchmarks.
GLM-4.6 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, GLM-4.6 ($0.50/1M tokens) is 7.1x more expensive than Phi 4 ($0.07/1M tokens).
For output processing, GLM-4.6 ($2.00/1M tokens) is 14.3x more expensive than Phi 4 ($0.14/1M tokens).
In conclusion, GLM-4.6 is more expensive than Phi 4.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-4.6 has 342.3B more parameters than Phi 4, making it 2328.6% larger.
Context Window
Maximum input and output token capacity
GLM-4.6 accepts 202,752 input tokens compared to Phi 4's 16,384 tokens. GLM-4.6 can generate longer responses up to 202,752 tokens, while Phi 4 is limited to 16,384 tokens.
Input capabilities
Documented input modalities across available providers
GLM-4.6 supports multimodal inputs, whereas Phi 4 does not.
GLM-4.6 can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-4.6
Phi 4
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
GLM-4.6 was released on 2025-09-30, while Phi 4 was released on 2024-12-12.
GLM-4.6 is 10 months newer than Phi 4.
Sep 30, 2025
11 months ago
9mo newerDec 12, 2024
1.8 years ago
Knowledge Cutoff
When training data ends
Phi 4 has a documented knowledge cutoff of 2024-06-01, while GLM-4.6'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 GLM-4.6's cutoff date.
—
Jun 2024
Provider Availability
GLM-4.6 is available from DeepInfra, Fireworks. Phi 4 is available from DeepInfra.
GLM-4.6
Phi 4
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-4.6 and Phi 4 side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-4.6 vs Phi 4.