DeepSeek V4-Pro vs GLM-5.2
DeepSeek's DeepSeek V4-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
DeepSeek V4-Pro is the cheaper option — roughly 2.2x less on a blended workload, and it suits frontier-adjacent reasoning at open-weight prices. GLM-5.2 justifies its premium when you need mit-licensed coding agents.
DeepSeek • DeepSeek V4
$0.66 / $1.98
/ 1M tokens (input / output)
A 1.6T-parameter mixture-of-experts model with 49B active, released under MIT and callable through DeepSeek's own API with configurable reasoning effort. Since 16 August 2026 DeepSeek bills on a peak/off-peak schedule, so the hour you run a job changes the bill by exactly 2x — which makes it unusually well suited to scheduled batch work.
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 2.1x the price than DeepSeek V4-Pro on input tokens.
Output price
GLM-5.2 is 2.2x the price than DeepSeek V4-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 | DeepSeek V4-Pro | GLM-5.2 | Difference |
|---|---|---|---|
| Light — 1M in / 200K out | $1 | $2 | $1 |
| Moderate — 10M in / 2M out | $11 | $23 | $12 |
| Heavy — 100M in / 20M out | $106 | $228 | $122 |
Specification comparison
| Attribute | DeepSeek V4-Pro | GLM-5.2 |
|---|---|---|
| Input (/ 1M tokens) | $0.66 | $1.40 |
| Output (/ 1M tokens) | $1.98 | $4.40 |
| Cached input | $0.02 | $0.26 |
| Context window | 1,000,000 tokens | 1,000,000 tokens |
| Max output | 131,072 tokens | 131,072 tokens |
| Native reasoning | Yes | Yes |
| Knowledge cutoff | 2026-03 | 2026-02 |
| Relative latency | medium | medium |
| Open weights | MIT | MIT |
| API model ID | deepseek-v4-pro | glm-5.2 |
| Status | stable | stable |
DeepSeek V4-Pro: Off-peak rate shown. Peak rates are exactly double ($1.32 input / $3.96 output) during 01:00–04:00 and 06:00–10:00 UTC, Monday to Friday. Cache hits cost roughly 3% of a cache miss.
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 DeepSeek V4-Pro if…
- Overnight batch reasoning jobs
- Self-hosted deployments needing MIT terms
- Cost-sensitive coding agents
- Chinese-language workloads
Choose GLM-5.2 if…
- Coding agents on large repositories
- Long-horizon refactors
- Self-hosted agent stacks
- Structured output pipelines
Frequently asked
Is DeepSeek V4-Pro or GLM-5.2 cheaper?
DeepSeek V4-Pro is cheaper. On a blended 3:1 input-to-output workload it costs about 2.2x less than GLM-5.2.
Which has the larger context window, DeepSeek V4-Pro or GLM-5.2?
Both accept up to 1M tokens, so context is not a differentiator here.
Should I use DeepSeek V4-Pro or GLM-5.2?
Pick DeepSeek V4-Pro for frontier-adjacent reasoning at open-weight prices. Pick GLM-5.2 for mit-licensed coding agents. If cost dominates the decision, DeepSeek V4-Pro wins; if you need the capability ceiling, benchmark both on your own evals before committing.