Gemini 3.1 Pro vs GLM-5
Gemini 3.1 Pro and GLM-5 are closely matched at 43.3 and 37.1 on the LLM Stats Score. GLM-5 is 2.9x cheaper per token.
Google · Zhipu AI · Updated for 2026
Which is better?
Gemini 3.1 Pro and GLM-5 are closely matched on the overall LLM Stats Score at 43.3 and 37.1.
In the 5 individual benchmarks reported for both models, Gemini 3.1 Pro wins 5; this is a narrower head-to-head signal than the composite indexes.
On price, GLM-5 is roughly 2.9x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Gemini 3.1 Pro 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 Gemini 3.1 Pro
- you value its reported benchmark strengths — it wins 5 of 5 exact shared results
- you process long inputs — it offers a 1,000,000 token context window
- you want the most recent training data — it shipped Feb 2026
Choose GLM-5
- cost matters — it's about 2.9x cheaper per token
- you need open weights you can self-host or fine-tune
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
20 reported for Gemini 3.1 Pro · 5 for GLM-5
Gemini 3.1 Pro outperforms in 5 benchmarks (BrowseComp, MCP Atlas, SWE-Bench Verified, t2-bench, Terminal-Bench 2.0), while GLM-5 is better at 0 benchmarks.
Gemini 3.1 Pro 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, Gemini 3.1 Pro ($2.00/1M tokens) is 2.0x more expensive than GLM-5 ($1.00/1M tokens).
For output processing, Gemini 3.1 Pro ($12.00/1M tokens) is 3.8x more expensive than GLM-5 ($3.20/1M tokens).
In conclusion, Gemini 3.1 Pro is more expensive than GLM-5.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Gemini 3.1 Pro accepts 1,000,000 input tokens compared to GLM-5's 200,000 tokens. Gemini 3.1 Pro can generate longer responses up to 1,000,000 tokens, while GLM-5 is limited to 128,000 tokens.
Input capabilities
Documented input modalities across available providers
Gemini 3.1 Pro supports multimodal inputs, whereas GLM-5 does not.
Gemini 3.1 Pro can handle both text and other forms of data like images, making it suitable for multimodal applications.
Gemini 3.1 Pro
GLM-5
License
Usage and distribution terms
Gemini 3.1 Pro is licensed under a proprietary license, while GLM-5 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
Gemini 3.1 Pro was released on 2026-02-19, while GLM-5 was released on 2026-02-11.
Gemini 3.1 Pro is 0 month newer than GLM-5.
Feb 19, 2026
7 months ago
1w newerFeb 11, 2026
7 months ago
Knowledge Cutoff
When training data ends
Gemini 3.1 Pro has a documented knowledge cutoff of 2025-01-31, while GLM-5's cutoff date is not specified.
We can confirm Gemini 3.1 Pro's training data extends to 2025-01-31, but cannot make a direct comparison without GLM-5's cutoff date.
Jan 2025
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Provider Availability
Gemini 3.1 Pro is available from DeepInfra, Google. GLM-5 is available from FriendliAI, ZAI.
Gemini 3.1 Pro
GLM-5
Outputs Comparison
Judge for yourself.
Run your own prompts against Gemini 3.1 Pro and GLM-5 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Gemini 3.1 Pro vs GLM-5.