GLM-4.6 vs GPT OSS 120B High
GLM-4.6 and GPT OSS 120B High are closely matched at 29.0 and 25.4 on the LLM Stats Score. GPT OSS 120B High is 4.4x cheaper per token.
Zhipu AI · OpenAI · Updated for 2026
Which is better?
GLM-4.6 and GPT OSS 120B High are closely matched on the overall LLM Stats Score at 29.0 and 25.4.
In the 3 individual benchmarks reported for both models, GLM-4.6 wins 3; this is a narrower head-to-head signal than the composite indexes.
On price, GPT OSS 120B High is roughly 4.4x 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
- you value its reported benchmark strengths — it wins 3 of 3 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 GPT OSS 120B High
- cost matters — it's about 4.4x 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 · 7 for GPT OSS 120B High
GLM-4.6 outperforms in 3 benchmarks (AIME 2025, GPQA, LiveCodeBench v6), while GPT OSS 120B High 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 5.0x more expensive than GPT OSS 120B High ($0.10/1M tokens).
For output processing, GLM-4.6 ($2.00/1M tokens) is 4.0x more expensive than GPT OSS 120B High ($0.50/1M tokens).
In conclusion, GLM-4.6 is more expensive than GPT OSS 120B High.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-4.6 has 240.2B more parameters than GPT OSS 120B High, making it 205.7% larger.
Context Window
Maximum input and output token capacity
GLM-4.6 accepts 202,752 input tokens compared to GPT OSS 120B High's 131,072 tokens. GLM-4.6 can generate longer responses up to 202,752 tokens, while GPT OSS 120B High is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
GLM-4.6 supports multimodal inputs, whereas GPT OSS 120B High 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
GPT OSS 120B High
License
Usage and distribution terms
GLM-4.6 is licensed under MIT, while GPT OSS 120B High uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
GLM-4.6 was released on 2025-09-30, while GPT OSS 120B High was released on 2025-08-05.
GLM-4.6 is 2 months newer than GPT OSS 120B High.
Sep 30, 2025
11 months ago
1mo newerAug 5, 2025
1.1 years ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
GLM-4.6 is available from DeepInfra, Fireworks. GPT OSS 120B High is available from OpenAI, Fireworks.
GLM-4.6
GPT OSS 120B High
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-4.6 and GPT OSS 120B High side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-4.6 vs GPT OSS 120B High.