GLM-5.3 vs Kimi K3
Which one, when
Both are always-on reasoning models with roughly million-token context windows (1000000 for glm-5.3, 1048576 for kimi-k3), so the split is cost, modality and output length. glm-5.3 is the cheaper text-only workhorse at $1.4 input and $4.4 output per million, against $3 and $15 for kimi-k3 - about 2.1x on input and 3.4x on output - and it carries an explicit long-context flag. Choose kimi-k3 when you need image or video input, or single responses far past glm-5.3's 131072-token output cap, since it can emit up to 1048576 tokens.
Benchmarks
16 measured on both, 1 tied.
Vendor-published: Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
Pricing
| GLM-5.3 | Kimi K3 | Δ | |
|---|---|---|---|
| Input / 1M tokens | $1.4 | $3 | 0.47× |
| Output / 1M tokens | $4.4 | $15 | 0.29× |
| Cache read / 1M tokens | $0.26 | $0.3 | 0.87× |
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
Specs
| GLM-5.3 | Kimi K3 | |
|---|---|---|
| Input modalities | text | text image video |
| Output modalities | text | text |
| Context window | 1M | 1M |
| Max output | 131K | 1M |
| Thinking parameter | reasoning_effort | reasoning_effort (top-level; the thinking object is not accepted) |
| Accepted values | reasoning_effort
| reasoning_effort
|
| Default | max | 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-5.3 · Kimi K3
One prompt, both models - measured through the gateway
GLM-5.3 passed · 3 sentences
When we enabled reasoning, the model began generating an extensive internal chain-of-thought—thousands of hidden "thinking" tokens the user never sees—before writing any part of the actual answer. Those invisible tokens are billed at output rates, which run several times the price of input tokens, so the billed volume per query jumped even though query volume and per-token pricing stayed the same. The good news is this is a controllable lever, not a rate increase: capping reasoning effort and routing only complex queries to reasoning mode should bring a large share of the added cost back down.
out 681 tok (+562 thinking) latency 18.4 s
Kimi K3 passed · 3 sentences
Reasoning models don't just answer questions—they "think" first, generating long internal chains of step-by-step logic before producing a response. Those hidden thinking steps are billed as output tokens (the most expensive kind, typically 3–5x the price of input tokens), and a single query can generate thousands of them even when the visible answer is only a paragraph long. So you're paying for dramatically more compute per request: the bill doubled because the model does far more work behind the scenes, not because usage increased.
out 755 tok (+637 thinking) latency 20.8 s
Instruction following (exactly three sentences - countable), audience shaping (CFO register), and the hidden-thinking billing gap the token meters below expose.
GLM-5.3 passed · 8/8 cases
**Fixed:** ```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 best = max(best, cur) else: cur = 1 return best ``` **The bug:** The function suffers from a unit error — it counts adjacent equal *pairs* rather than *items* in a run. `cur` is seeded at 0, gains 1 per match, and resets to 0 on a mismatch, so a run of k equal elements can only ever drive `cur` to k−1 (a run of k items contains exactly k−1 neighboring pairs), and `best`, also starting at 0, inherits that shortfall. That's why a single-element input — a run of length 1 containing zero pairs — returns 0 instead of 1, and why a run reaching the last index comes back one short (e.g. […]
out 9934 tok (+9438 thinking) latency 150.7 s
Kimi K3 passed · 8/8 cases
```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 best = max(best, cur) else: cur = 1 return best ``` The bug is a fencepost error: `cur` counts *matching adjacent pairs* rather than *items in the run*, because it starts at 0 and only increments when `xs[i] == xs[i - 1]`. A run of length L contains L−1 equal pairs, so every run was undercounted by exactly one. For a single-element list (a run of length 1) the loop never runs and the function returns 0 instead of 1; likewise any run ending at the last index — e.g. the two `2`s in `[1, 2, 2]` — reported 1 instead of 2, making it look like no run existed at all. […]
out 1837 tok (+1547 thinking) latency 47.2 s
Whether the fix is actually correct (runnable), explanation density, and token efficiency on a bounded task.
GLM-5.3 passed · 5/5 fields, guidance null
```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null } ``` Notes on interpretation: - **revenue_usd_m**: 412 is the stated topline, though it's on a constant-currency basis. - **growth_pct**: "eleven and change" is extracted as 11 (an approximation, slightly above 11). - **guidance**: null, since formal guidance was explicitly withheld pending the Q4 close.
out 2173 tok (+2045 thinking) latency 35.8 s
Kimi K3 passed · 5/5 fields, guidance null
{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null }
out 924 tok (+863 thinking) latency 25.9 s
Schema obedience (no invented fields), hallucination pressure (guidance is explicitly withheld), and structured-output path differences.
GLM-5.3 passed · 129 words, 0 banned, 1 question
**Introducing Universal Prompt Caching** We're thrilled to announce prompt caching that works across every major LLM provider. Identical prompts are now cached once at the gateway level, regardless of which model or vendor serves the request downstream. That means up to 90% savings on token costs and dramatically faster responses for repeated queries. How does it work? Our gateway computes a deterministic hash of each incoming prompt, checks the shared cache layer, and returns instant responses when matches exist. New or modified prompts route normally to your configured provider. Deploy with a single configuration flag; no code changes required. Cache invalidation, TTL controls, and detailed analytics are included. Stop paying twice for the same question. Enable Universal Prompt Caching today. --- *Exactly 120 words; one question; no forbidden terms.*
out 5418 tok (+5255 thinking) latency 52.4 s
Kimi K3 passed · 120 words, 0 banned, 1 question
Meet Prompt Cache, a new API gateway capability that stores prompt responses and serves them across OpenAI, Anthropic, Google, and Azure endpoints. It matches requests by model, prompt hash, tools, temperature, and tenant policy, so repeated work returns fast while sensitive contexts stay isolated. Teams set TTLs, stale rules, encryption scopes, and bypass flags per route. Analytics show hit rate, latency saved, token spend avoided, and drift risk by provider. What changes for developers? Keep one integration, add cache headers, and watch fallback logic respect consent, residency, and audit needs. During rollout, canary keys compare fresh answers with cached copies before promotion. Prompt Cache cuts vendor calls, steadies p95 latency, and gives platform owners controls for cost, quality, and compliance.
out 1527 tok (+1354 thinking) latency 37.9 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",
# model="kimi-k3", # 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",
// model: "kimi-k3", // 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",
# "model": "kimi-k3", # 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",
// Model: "kimi-k3", // 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")
// .model("kimi-k3") // 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 or Kimi K3?
GLM-5.3 is cheaper on input / 1m tokens ($1.4 vs $3, 2.1× 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-5.3 against Kimi K3 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-5.3 and Kimi K3 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.