GLM-5.3-Flash vs MiniMax M3
Cuál usar y cuándo
glm-5.3-flash tiene tarifas inferiores de entrada y salida y anuncia salidas estructuradas. Elija minimax-m3 para un límite de salida mucho mayor y la opción de desactivar el razonamiento.
Benchmarks
MiniMax M3: el proveedor no ha publicado resultados de benchmarks.
Publicado por los proveedores: Alibaba (Qwen) Anthropic DeepSeek Google Moonshot OpenAI Tencent Z.ai
Precios
| GLM-5.3-Flash | MiniMax M3 | Δ | |
|---|---|---|---|
| Entrada / 1M tokens | $0.15 | $0.3 | 0.5× |
| Salida / 1M tokens | $0.5 | $1.2 | 0.42× |
| Lectura de caché / 1M tokens | $0.03 | $0.06 | 0.5× |
| Escritura en caché | - | sin cargo aparte | - |
Tarifas tomadas del catálogo en vivo al generar el sitio; la página de cada modelo tiene la ficha de precios actualizada.
Dónde queda cada uno: precio de entrada por 1M de tokens entre los 80 modelos de chat que se facturan en esta unidad (escala logarítmica)
Capacidades
| GLM-5.3-Flash | MiniMax M3 | |
|---|---|---|
| Uso de herramientas | sí | sí |
| Control del razonamiento | siempre activo | configurable |
| Salida estructurada | sí | - |
| Caché de prompts | implícito (automático) | implícito (automático) |
| Duración de la caché | no publicado | no publicado |
| Prefijo mínimo en caché | no publicado | 512 tokens |
Especificaciones
| GLM-5.3-Flash | MiniMax M3 | |
|---|---|---|
| Modalidades de entrada | texto imagen vídeo | texto imagen vídeo |
| Modalidades de salida | texto | texto |
| Lanzamiento | 2026-08-26 | 2026-06-01 |
| Ventana de contexto | 1M | 1M |
| Salida máxima | 164K | 524K |
| Parámetro de razonamiento | reasoning_effort |
|
| Valores aceptados | reasoning_effort
| thinking.type
reasoning_split
|
| Valor por defecto | max | adaptive: thinking on, with the model deciding when extra reasoning helps |
Las especificaciones se transcriben de la documentación de cada proveedor; si un proveedor no publica un dato, la fila se omite en vez de deducirlo. Fuentes completas: GLM-5.3-Flash · MiniMax M3
Un mismo prompt en los dos modelos, medido a través de la pasarela
GLM-5.3-Flash superado · 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.
salida 742 tok (+626 razonamiento) latencia 23.2 s
MiniMax M3 superado · 3 sentences
Reasoning models don't just answer — they first generate extensive internal "thinking" tokens to work through the problem step-by-step, and you're billed for all of those tokens even though users never see them. A query that previously cost ~200 tokens may now cost 2,000–10,000 once reasoning is enabled, multiplying per-query spend by roughly 5–50x while the visible output stayed the same size — that's the doubling. The lever is tuning: cap reasoning effort on simple queries, cache the reasoning traces, and reserve the full reasoning model for only your hardest 10–20% of requests, which typically restores 60–80% of the savings.
salida 764 tok (+629 razonamiento) latencia 3.5 s
Cumplimiento de la instrucción (exactamente tres frases, se pueden contar), adaptación al público (registro de CFO) y la diferencia de facturación por razonamiento oculto que dejan ver los contadores de tokens de abajo.
GLM-5.3-Flash superado · 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). […]
salida 2462 tok (+2138 razonamiento) latencia 29.1 s
MiniMax M3 superado · 8/8 cases
The bug is that the loop starts at index 1 and treats `cur` as the number of *equal transitions* rather than the run length, so a run of *k* consecutive equal items is reported as `k − 1`, and a single-element list (where the loop body never executes) returns 0 instead of 1. A correct version tracks the actual run length by seeding `cur = 1` and resetting it to 1 (not 0) on a mismatch: ```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 else: cur = 1 if cur > best: best = cur return best ``` Now `[5]` returns `1`, `[1,1,1]` returns `3`, and a run like `[1,2,2,2]` that ends at the last index is correctly captured as `3` because `cur` is updated before `best` is compared on every iteration, including the final one.
salida 845 tok (+596 razonamiento) latencia 3.5 s
Si la corrección funciona de verdad (se puede ejecutar), lo densa que es la explicación y la eficiencia en tokens en una tarea acotada.
GLM-5.3-Flash superado · 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.
salida 717 tok (+616 razonamiento) latencia 9.1 s
MiniMax M3 superado · 5/5 fields, guidance null
```json { "company": null, "quarter": "Q3", "revenue_usd_m": 412, "growth_pct": null, "guidance": null } ```
salida 1002 tok (+955 razonamiento) latencia 3.6 s
Respeto del esquema (sin campos inventados), tentación de alucinar (la guidance se omite a propósito) y diferencias entre las vías de salida estructurada.
GLM-5.3-Flash superado · 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.
salida 2095 tok (+1937 razonamiento) latencia 20.2 s
MiniMax M3 superado · 121 words, 0 banned, 1 question
Introducing PromptCache, our intelligent caching layer for the API gateway. Tired of paying twice for identical requests? The feature stores prompt-response pairs across every connected provider, returning cached results instead of hitting costly endpoints. Deployment requires only a single configuration flag, and semantic similarity detection happens automatically. Latency drops up to 70% on repeated traffic while costs follow the same downward curve. Developers access detailed cache hit metrics, eviction logs, and provider breakdowns through the dashboard. Whether routing to OpenAI, Anthropic, or local models, every cached token saves time and money. The cache respects TTL policies, supports manual purging, and encrypts stored content. Available today for Pro and Enterprise tiers—enable PromptCache on existing endpoints and watch inference bills shrink immediately.
salida 2808 tok (+2656 razonamiento) latencia 6.0 s
Respeto de las restricciones (límite de palabras, lista de palabras prohibidas, una única pregunta), sello de estilo y control de la longitud.
Cambia de uno a otro con una sola línea
Los dos ids aparecen en todas las pestañas de abajo; lo único que cambia es el par de líneas resaltadas. El endpoint, la clave y la estructura de la solicitud son los mismos.
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="minimax-m3", # descomenta esta línea, comenta la de arriba
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: "minimax-m3", // descomenta esta línea, comenta la de arriba
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": "minimax-m3", # descomenta esta línea, comenta la de arriba
"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: "minimax-m3", // descomenta esta línea, comenta la de arriba
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("minimax-m3") // descomenta esta línea, comenta la de arriba
.addUserMessage("Summarize this diff")
.reasoningEffort(ReasoningEffort.MEDIUM)
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));Preguntas frecuentes
¿Cuál es más barato, GLM-5.3-Flash o MiniMax M3?
GLM-5.3-Flash es más barato en Entrada / 1M tokens ($0.15 frente a $0.3, una diferencia de 2.0×). En otras filas puede ser al revés: la tabla de arriba recoge todas las tarifas, y el coste real depende de tu combinación de uso.
¿Puedo hacer pruebas A/B de GLM-5.3-Flash frente a MiniMax M3 sin dos integraciones?
Sí. Los dos se sirven desde el mismo endpoint compatible con OpenAI y con una sola clave API. Para cambiar de uno a otro basta con tocar una línea, el nombre del modelo, así que puedes mandar una parte del tráfico a cada uno y comparar directamente las facturas.
¿Admiten GLM-5.3-Flash y MiniMax M3 caché de prompts?
Sí. Los dos cobran las lecturas de caché por debajo de su tarifa de entrada, así que las cargas de trabajo que reutilizan un prefijo ya cacheado cuestan menos de lo que sugieren los precios de lista. Las tarifas exactas de lectura de caché están en la tabla de precios de arriba.