Gemini 3.1 Pro vs GLM-5.2
Google's Gemini 3.1 Pro and Z.ai's GLM-5.2 both target production language workloads, but they price and behave differently. Here is the side-by-side.
Pricing checked against provider documentation on . How we verify
The short answer
GLM-5.2 is the cheaper option — roughly 2.1x less on a blended workload, and it suits mit-licensed coding agents. Gemini 3.1 Pro justifies its premium when you need best price-to-reasoning ratio.
Google • Gemini 3.1
$2.00 / $12.00
/ 1M tokens (input / output)
Google's shipping flagship while 3.5 Pro remains unreleased, and the cheapest frontier model by a wide margin. It still tops several hard-reasoning boards including GPQA Diamond and ARC-AGI-2, making it the value choice for research-style workloads — provided you keep prompts under 200K tokens, where the price doubles.
Z.ai • GLM-5
$1.40 / $4.40
/ 1M tokens (input / output)
A 744B-parameter MoE with 40B active, MIT-licensed, and tuned for long-horizon coding work. It sits between DeepSeek and Kimi on price and is the usual challenger when a cheaper model keeps looping or missing repository conventions — enough capability to finish the task without flagship pricing.
Input price
GLM-5.2 is 30% cheaper than Gemini 3.1 Pro on input tokens.
Output price
GLM-5.2 is 63% cheaper than Gemini 3.1 Pro on output tokens — usually the side that dominates the bill.
Monthly cost at three workload sizes
Standard (non-batch, non-cached) rates. Reasoning models will exceed these figures because thinking tokens bill as output.
| Workload | Gemini 3.1 Pro | GLM-5.2 | Difference |
|---|---|---|---|
| Light — 1M in / 200K out | $4 | $2 | $2 |
| Moderate — 10M in / 2M out | $44 | $23 | $21 |
| Heavy — 100M in / 20M out | $440 | $228 | $212 |
Specification comparison
| Attribute | Gemini 3.1 Pro | GLM-5.2 |
|---|---|---|
| Input (/ 1M tokens) | $2.00 | $1.40 |
| Output (/ 1M tokens) | $12.00 | $4.40 |
| Cached input | — | $0.26 |
| Context window | 1,048,576 tokens | 1,000,000 tokens |
| Max output | 65,536 tokens | 131,072 tokens |
| Native reasoning | Yes | Yes |
| Knowledge cutoff | 2025-06 | 2026-02 |
| Relative latency | medium | medium |
| Open weights | No | MIT |
| API model ID | gemini-3.1-pro-preview | glm-5.2 |
| Status | preview | stable |
Gemini 3.1 Pro: Prompts over 200K tokens reprice to $4 input / $18 output per 1M.
GLM-5.2: Independently measured by Artificial Analysis; the vendor's own pricing page was unreachable at time of checking. Treat as approximate and confirm in the console.
Choose Gemini 3.1 Pro if…
- Hard reasoning and research tasks
- Long-context document and video analysis
- Native multimodal pipelines
- Cost-conscious frontier workloads
Choose GLM-5.2 if…
- Coding agents on large repositories
- Long-horizon refactors
- Self-hosted agent stacks
- Structured output pipelines
Frequently asked
Is Gemini 3.1 Pro or GLM-5.2 cheaper?
GLM-5.2 is cheaper. On a blended 3:1 input-to-output workload it costs about 2.1x less than Gemini 3.1 Pro.
Which has the larger context window, Gemini 3.1 Pro or GLM-5.2?
Gemini 3.1 Pro has the larger window at 1.05M tokens versus 1M.
Should I use Gemini 3.1 Pro or GLM-5.2?
Pick Gemini 3.1 Pro for best price-to-reasoning ratio. Pick GLM-5.2 for mit-licensed coding agents. If cost dominates the decision, GLM-5.2 wins; if you need the capability ceiling, benchmark both on your own evals before committing.