GLM-4.7 is the safer default for most buyers here. It creates the cleaner cost story, and the savings gap is meaningful enough that you should only move to Gemini 3.1 Pro if you have a clear quality reason.
Standard request winner
GLM-4.7
Saves about 80% versus the pricier option for the baseline request shape.
Scale winner
GLM-4.7
Saves about 80% once usage becomes a recurring operating expense.
Default recommendation
GLM-4.7
Best starting point for most buyers unless you already know you need the premium alternative.
Option A
Google AI
Option B
Zhipu AI
Decision scenarios
This reframes the comparison around real buying situations, not just benchmark curiosity.
Budget-first pick
GLM-4.7 wins the standard request scenario, so it is the safer default if you are still validating usage and want cheaper per-call economics.
Scale decision
GLM-4.7 stays ahead in the high-volume scenario, which matters most once the workload becomes a real operating expense instead of a prototype line item.
Capability-first pick
Gemini 3.1 Pro has the stronger capability signal across context, positioning, and premium model attributes. Pick it when reasoning depth or delivery quality matters more than raw token cost.
Input cost / 1M
Lower is better if prompt volume is the main driver.
Gemini 3.1 Pro
$2.00
GLM-4.7
$0.60
Output cost / 1M
Lower is better for chat, generation, and verbose outputs.
Gemini 3.1 Pro
$12.00
GLM-4.7
$2.20
Standard request total
Based on 10,000 input and 10,000 output tokens.
Gemini 3.1 Pro
$0.14
GLM-4.7
$0.028
Context window
Higher is better when you need fewer prompt-chunking compromises.
Gemini 3.1 Pro
1M
GLM-4.7
128K
Standard request
10,000 input / 10,000 output tokens
Gemini 3.1 Pro
$0.14
$0.02 input + $0.12 output
GLM-4.7
$0.028
$0.006 input + $0.022 output
High-volume scenario
2M input / 2M output tokens
Gemini 3.1 Pro
$28.00
GLM-4.7
$5.60
At scale, the cheaper option saves roughly 80% if your workload shape stays similar.
Cost estimates are generated from published input and output token rates for each provider. We apply identical token scenarios to both models so the result reflects pricing differences first, then layer on context and product-positioning signals to make the page more decision-ready. This page should help you narrow the choice quickly, but final selection should still be validated against your own prompts, quality bar, and latency requirements.
Go Deeper
See how to decide between premium, mid-tier, and bulk models without overpaying.
Map model pricing to budget controls, approval rules, and team-level usage governance.
Turn comparison insights into routing and caching policies that actually save money.