GLM-5.3-Flash vs GPT-6.1 Terra
Which one, when
For prompts up to 272000 input tokens, glm-5.3-flash is cheaper, accepts video and allows a longer response. Choose gpt-6.1-terra when turning reasoning off is a requirement; glm-5.3-flash has no documented off setting.
Benchmarks
GPT-6.1 Terra: the vendor has not published benchmark scores.
Vendor-published: Alibaba (Qwen) Anthropic DeepSeek Google Moonshot OpenAI Tencent Z.ai
Pricing
| GLM-5.3-Flash | GPT-6.1 Terra | Δ | |
|---|---|---|---|
| Input / 1M tokens | $0.15 | $1 | 0.15× |
| Output / 1M tokens | $0.5 | $5 | 0.1× |
| Cache read / 1M tokens | $0.03 | $0.05 | 0.6× |
| Cache write | - | 1.25x (30m) / 2.5x (12h) | - |
Rates from the live catalogue at build time; each model page carries the current rate card.
Where they sit · input price per 1M tokens across all 80 chat models on this billing unit (log scale)
Capabilities
| GLM-5.3-Flash | GPT-6.1 Terra | |
|---|---|---|
| Tool calling | yes | yes |
| Thinking control | always on | configurable |
| Structured output | yes | yes |
| Prompt caching | implicit (automatic) | implicit + explicit |
| Cache lifetime | not published | at least 30m by default, 12h option on the Responses API |
| Minimum cached prefix | not published | 1,024 tokens |
Specs
| GLM-5.3-Flash | GPT-6.1 Terra | |
|---|---|---|
| Input modalities | text image video | text image |
| Output modalities | text | text |
| Released | 2026-08-26 | 2026-10-26 |
| Knowledge cutoff | - | 2026-06 |
| Context window | 1M | 1.1M |
| Max output | 164K | 128K |
| Thinking parameter | reasoning_effort | reasoning.effort |
| Accepted values | reasoning_effort
| reasoning.effort
|
| Default | max | medium |
Specs are transcribed from each vendor’s documentation; a row a vendor does not publish is left out rather than inferred. Full sources: GLM-5.3-Flash · GPT-6.1 Terra
One prompt, both models, measured through the gateway
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
GPT-6.1 Terra passed · 3 sentences
Enabling reasoning caused the model to process additional internal tokens before producing each answer, even though those tokens are not visible to users. Those tokens are generally billable, increasing the cost per request and potentially doubling the bill even if usage stayed the same. To confirm the cause, compare request counts, model pricing, and token usage—including reasoning tokens—before and after the change.
out 196 tok (+115 thinking) latency 4.6 s
Instruction following (exactly three sentences, so countable), audience shaping (CFO register), and the hidden-thinking billing gap the token meters below expose.
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
GPT-6.1 Terra passed · 8/8 cases
```python def longest_run(xs): if len(xs) == 0: return 0 best = cur = 1 for i in range(1, len(xs)): if xs[i] == xs[i - 1]: cur += 1 else: cur = 1 best = max(best, cur) return best ``` The original code counted matching *adjacent pairs* rather than items, so every nonempty run was undercounted by one, including a single-element input. It did update `best` for matches at the last index, but still undercounted those runs. The fix starts each run at length one and updates `best` as the run grows.
out 331 tok (+172 thinking) latency 6.3 s
Whether the fix is actually correct (runnable), explanation density, and token efficiency on a bounded task.
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
GPT-6.1 Terra passed · 5/5 fields, guidance "Withheld pending the Q4 close"
{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": null, "growth_pct": null, "guidance": "Withheld pending the Q4 close" }
out 317 tok (+261 thinking) latency 6.9 s
Schema obedience (no invented fields), hallucination pressure (guidance is explicitly withheld), and structured-output path differences.
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
GPT-6.1 Terra passed · 120 words, 0 banned, 1 question
Meet Prompt Cache, a new API gateway feature that stores reusable prompts across providers, helping teams reduce latency and control costs without changing how they build. When a request matches a cached prompt, the gateway retrieves the stored result instead of sending the same work again. Need to route traffic between models or vendors? Prompt Cache works across supported providers, giving your applications a consistent caching layer as your stack evolves. Set expiration policies, monitor cache performance, and choose which requests qualify through familiar gateway controls. Your team can spend less time repeating requests and more time improving the experiences customers rely on. Start using Prompt Cache today and make every request work harder for your application and your budget.
out 579 tok (+440 thinking) latency 9.1 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-5.3-flash",
# model="gpt-6.1-terra", # 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-5.3-flash",
// model: "gpt-6.1-terra", // 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-5.3-flash",
# "model": "gpt-6.1-terra", # 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-5.3-flash",
// Model: "gpt-6.1-terra", // 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-5.3-flash")
// .model("gpt-6.1-terra") // 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-5.3-Flash or GPT-6.1 Terra?
GLM-5.3-Flash is cheaper on the "Input / 1M tokens" row ($0.15 vs $1, 6.7× apart). Other rows may point the other way; the table above carries the full rate card, and real cost depends on your mix.
Can I A/B test GLM-5.3-Flash against GPT-6.1 Terra without two integrations?
Yes. Both are served through the same OpenAI-compatible endpoint with one API key. Switching is a one-line change to the model id, so you can route a fraction of traffic to each and compare bills directly.
Do GLM-5.3-Flash and GPT-6.1 Terra support prompt caching?
Yes. Both bill cache 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.