GLM-5.2 vs GLM-5.3-Flash
Which one, when
Both are Z.ai text-out models with a 1,000,000-token context and reasoning, code and tool support, so the split is price and inputs: glm-5.3-flash costs $0.15 per million input and $0.50 per million output against $1.4 and $4.4 for glm-5.2, roughly 9x cheaper on input and 8.8x on output, and it also accepts image and video input with up to 163,840 output tokens. Pick glm-5.3-flash for high-volume or multimodal work on a tight budget. Pick glm-5.2 when you need thinking switched off for latency-sensitive or deterministic calls, which glm-5.3-flash does not allow.
Benchmarks
6 measured on both.
Vendor-published: Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
Pricing
| GLM-5.2 | GLM-5.3-Flash | Δ | |
|---|---|---|---|
| Input / 1M tokens | $1.4 | $0.15 | 9.3× |
| Output / 1M tokens | $4.4 | $0.5 | 8.8× |
| Cache read / 1M tokens | $0.26 | $0.03 | 8.7× |
Rates from the live catalog at build time; each model page carries the current card.
Where they sit - input price per 1M tokens across all 67 chat models on this billing unit (log scale)
Capabilities
| GLM-5.2 | GLM-5.3-Flash | |
|---|---|---|
| Tool use | yes | yes |
| Thinking control | configurable | always on |
| Structured output | yes | yes |
| Prompt caching | implicit (automatic) | implicit (automatic) |
| Cache lifetime | not published | not published |
| Minimum cached prefix | not published | not published |
Specs
| GLM-5.2 | GLM-5.3-Flash | |
|---|---|---|
| Input modalities | text | text image video |
| Output modalities | text | text |
| Released | 2026-06-16 | - |
| Context window | 1M | 1M |
| Max output | 131K | 164K |
| Thinking parameter |
| reasoning_effort |
| Accepted values | thinking.type
reasoning_effort
| reasoning_effort
|
| Default | enabled, with reasoning_effort at max: the only GLM with an effort dial, and it defaults to the top of it | max |
Specs are transcribed from each vendor’s documentation; a row a vendor does not publish is left out rather than inferred. Full sources: GLM-5.2 · GLM-5.3-Flash
One prompt, both models - measured through the gateway
GLM-5.2 passed · 3 sentences
Enabling reasoning means the LLM now generates thousands of invisible "thinking" tokens to systematically work through complex problems before producing a final answer. Because our cloud providers bill for these internal processing steps at the same rate as standard output, our billable token volume per query has doubled. While this increases our direct API costs, it drastically reduces error rates and manual review labor, ultimately lowering our total cost per resolved transaction.
out 1223 tok (+1138 thinking) latency 17.1 s
GLM-5.3-Flash passed · 3 sentences
Enabling reasoning means the model now generates thousands of invisible "thinking" tokens before each visible answer, and we're billed for those hidden tokens at full output rates—our most expensive tier. Since output tokens cost 3–5x more than input tokens, that hidden thinking volume compounds across every request, which is why the bill doubled rather than edged up. If the spend concerns you, the fix is straightforward: route only complex queries to reasoning models and keep simple tasks on standard ones, which can reclaim a meaningful share of that cost without losing accuracy where it matters.
out 742 tok (+626 thinking) latency 23.2 s
Instruction following (exactly three sentences - countable), audience shaping (CFO register), and the hidden-thinking billing gap the token meters below expose.
GLM-5.2 missed · 1/8 cases (fails [1])
```python def longest_run(xs): if not xs: return 0 best = 1 cur = 1 for i in range(1, len(xs)): if xs[i] == xs[i - 1]: cur += 1 […]
out 4097 tok (+4036 thinking) latency 58.4 s
GLM-5.3-Flash passed · 8/8 cases
```python def longest_run(xs): if not xs: return 0 best = cur = 1 for i in range(1, len(xs)): if xs[i] == xs[i - 1]: cur += 1 best = max(best, cur) else: cur = 1 return best ``` **The bug:** `cur` is supposed to hold the length of the current run, but it's seeded with `0` and reset to `0` on a mismatch instead of `1` — and since the loop starts at index 1, the first element of every run never passes through the `==` branch and so never gets counted at all. That means `cur` actually holds *run length minus 1* for every run, so every run is underreported by one: a single-element input never enters the loop and returns 0 instead of 1, and a run of length n returns n − 1 (e.g., `[3, 3, 3]` returns 2). […]
out 2462 tok (+2138 thinking) latency 29.1 s
Whether the fix is actually correct (runnable), explanation density, and token efficiency on a bounded task.
GLM-5.2 passed · 5/5 fields, guidance "withheld"
```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": "withheld" } ```
out 1947 tok (+1893 thinking) latency 30.9 s
GLM-5.3-Flash passed · 5/5 fields, guidance "Withheld pending the Q4 close"
```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": "Withheld pending the Q4 close" } ``` Notes on judgment calls: revenue of 412 is on a constant-currency basis as stated; growth of "eleven and change" is approximated as 11 since no precise figure is given.
out 717 tok (+616 thinking) latency 9.1 s
Schema obedience (no invented fields), hallucination pressure (guidance is explicitly withheld), and structured-output path differences.
GLM-5.2 passed · 120 words, 0 banned, 1 question
We are introducing Caching for our API Gateway, the smartest way to optimize your workflows. Why pay for the exact same response twice? Now, you can automatically store and reuse prompt results across multiple AI providers, drastically reducing latency and overall operational costs. If a user submits a duplicate query, the gateway serves the cached answer instantly, regardless of whether you route to OpenAI, Anthropic, or others. This directly translates to faster applications and significantly lower monthly API bills. You can easily configure your specific caching rules within the developer dashboard and watch your efficiency soar. Stop wasting your valuable tokens on completely redundant computations. Upgrade to the latest gateway version today and experience the future of intelligent prompt management.
out 11125 tok (+10984 thinking) latency 114.8 s
GLM-5.3-Flash passed · 120 words, 0 banned, 1 question
Introducing Cross-Provider Prompt Cache, the newest feature in our API gateway. Today, teams send identical prompts to multiple LLM providers and pay full price each time. Why duplicate that work and cost? With Cross-Provider Prompt Cache, your gateway stores prompt-response pairs and serves repeated requests from cache, regardless of which provider handles the call. The result: lower latency, reduced spend, and consistent outputs across OpenAI, Anthropic, Google, and self-hosted models. Configure cache policies per route, set TTLs, and invalidate entries instantly through the dashboard or API. Built-in analytics show hit rates and savings in real time. Enable the cache with a single flag, no code changes required. Available today on all paid plans. Contact sales for enterprise volume pricing details.
out 2095 tok (+1937 thinking) latency 20.2 s
Constraint obedience (word budget, banned-word list, the single question), style fingerprint, and length control.
Switch between them with one line
Both ids are in every tab below - the highlighted pair of lines is the only edit. Same endpoint, same key, same request shape.
from openai import OpenAI
client = OpenAI(
base_url="https://synthorai.io/v1",
api_key="sk-syn-...",
)
resp = client.chat.completions.create(
model="glm-5.2",
# model="glm-5.3-flash", # uncomment this line, comment the one above
messages=[{"role": "user", "content": "Summarize this diff"}],
reasoning_effort="medium",
)
print(resp.choices[0].message.content)import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://synthorai.io/v1",
apiKey: "sk-syn-...",
});
const resp = await client.chat.completions.create({
model: "glm-5.2",
// model: "glm-5.3-flash", // uncomment this line, comment the one above
messages: [{ role: "user", content: "Summarize this diff" }],
reasoning_effort: "medium",
});
console.log(resp.choices[0].message.content);curl https://synthorai.io/v1/chat/completions \
-H "Authorization: Bearer sk-syn-..." \
-H "Content-Type: application/json" \
-d '{
"model": "glm-5.2",
# "model": "glm-5.3-flash", # uncomment this line, comment the one above
"messages": [{"role": "user", "content": "Hello"}],
"reasoning_effort": "medium"
}'package main
import (
"context"
"fmt"
"github.com/openai/openai-go/v3"
"github.com/openai/openai-go/v3/option"
)
func main() {
client := openai.NewClient(
option.WithBaseURL("https://synthorai.io/v1"),
option.WithAPIKey("sk-syn-..."),
)
resp, _ := client.Chat.Completions.New(context.TODO(), openai.ChatCompletionNewParams{
Model: "glm-5.2",
// Model: "glm-5.3-flash", // uncomment this line, comment the one above
Messages: []openai.ChatCompletionMessageParamUnion{
openai.UserMessage("Summarize this diff"),
},
ReasoningEffort: openai.ReasoningEffortMedium,
})
fmt.Println(resp.Choices[0].Message.Content)
}import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.chat.completions.*;
import com.openai.models.ReasoningEffort;
OpenAIClient client = OpenAIOkHttpClient.builder()
.baseUrl("https://synthorai.io/v1")
.apiKey("sk-syn-...")
.build();
ChatCompletion resp = client.chat().completions().create(
ChatCompletionCreateParams.builder()
.model("glm-5.2")
// .model("glm-5.3-flash") // uncomment this line, comment the one above
.addUserMessage("Summarize this diff")
.reasoningEffort(ReasoningEffort.MEDIUM)
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));FAQ
Which is cheaper, GLM-5.2 or GLM-5.3-Flash?
GLM-5.3-Flash is cheaper on input / 1m tokens ($0.15 vs $1.4, 9.3× apart). Other rows may point the other way - the table above carries the full card, and real cost depends on your mix.
Can I A/B test GLM-5.2 against GLM-5.3-Flash without two integrations?
Yes. Both are served through the same OpenAI-compatible endpoint with one API key - switching is a one-line model-string change, so you can route a fraction of traffic to each and compare bills directly.
Do GLM-5.2 and GLM-5.3-Flash support prompt caching?
Yes - both bill cached reads below their input rate, so warm-prefix workloads cost less than the list rates suggest. The exact cache-read rows are in the pricing table above.