Claude Sonnet 5 vs Kimi K2.7 Code
Cuál usar y cuándo — veredicto seleccionado, no una tabla de benchmarks
Elige claude-sonnet-5 cuando necesites espacio para trabajar: su contexto de 1000000 tokens es casi 4x los 256000 de kimi-k2.7-code, puede emitir hasta 128000 tokens frente a 32768, y su modo de pensamiento se puede desactivar, cosa que kimi-k2.7-code no permite. Elige kimi-k2.7-code para recortar el gasto por token en trabajo de texto e imagen — $0.95 de entrada y $4 de salida frente a $2 y $10, unas 2.1x y 2.5x más barato — y es el único de los dos que acepta entrada de vídeo. Las lecturas de caché quedan en tablas, $0.19 frente a $0.2.
Precios
| Claude Sonnet 5 | Kimi K2.7 Code | Δ | |
|---|---|---|---|
| Entrada / 1M tokens | $2 | $0.95 | 2.1× |
| Salida / 1M tokens | $10 | $4 | 2.5× |
| Lectura de caché / 1M tokens | $0.2 | $0.19 | 1.1× |
| Escritura en caché | 1.25x (5m) / 2x (1h) | — | — |
Tarifas del catálogo en vivo en el momento de la compilación; la página de cada modelo incluye la ficha actualizada.
Dónde se sitúan — precio de entrada por 1M de tokens en todos los 63 modelos de chat en esta unidad de facturación (escala logarítmica)
Capacidades
| Claude Sonnet 5 | Kimi K2.7 Code | |
|---|---|---|
| Uso de herramientas | sí | sí |
| Control de pensamiento | configurable | siempre activo |
| Salida estructurada | sí | — |
| Caché de prompt | explícito (marcas el prefijo) | implícito (automático) |
| Tiempo de vida de la caché | 5m default, 1h option | no publicado |
| Prefijo mínimo en caché | 1024 tokens | no publicado |
Especificaciones
| Claude Sonnet 5 | Kimi K2.7 Code | |
|---|---|---|
| Modalidades de entrada | texto imagen | texto imagen vídeo |
| Modalidades de salida | texto | texto |
| Lanzamiento | 2026-06-30 | 2026-06 |
| Límite de conocimiento | 2026-01 | — |
| Ventana de contexto | 1M | 256K |
| Salida máxima | 128K | 33K |
| Parámetro de pensamiento |
|
|
| Valores aceptados | thinking.type
effort
"enabled" returns 400 | type
keep
|
| Predeterminado | thinking on (adaptive) effort
| thinking on with Preserved Thinking on |
Las especificaciones se transcriben de la documentación de cada proveedor; si un proveedor no publica una fila, se omite en lugar de inferirse. Fuentes completas: Claude Sonnet 5 · Kimi K2.7 Code
Un prompt, ambos modelos — medido a través del gateway
Claude Sonnet 5 superado · 3 sentences
Reasoning mode works by having the model generate extended internal "thinking" steps before producing its final answer, and since we're billed on total tokens, that hidden reasoning text adds significantly to what we pay for—even though you never see it in the output. This is different from simply getting longer visible responses; the model is essentially "showing its work" behind the scenes, and complex prompts can trigger thousands of extra reasoning tokens per request. The upside is improved accuracy on complex tasks, but if we want to control costs, we should selectively enable reasoning only for queries that truly need it, rather than applying it universally.
salida 194 tok latencia 4.3 s
Kimi K2.7 Code superado · 3 sentences
Enabling reasoning mode causes the model to generate an internal chain-of-thought before producing each answer, and that hidden reasoning text is billed like any other output. Those extra reasoning tokens typically add up to much more than the final response itself, so every API call now costs significantly more than before. Because our overall usage volume did not change, the doubled bill reflects that each query consumes roughly twice as many billable tokens.
salida 259 tok (+174 pensamiento) latencia 5.4 s
Seguimiento de la instrucción (exactamente tres frases — contables), adaptación a la audiencia (registro de CFO), y la brecha de facturación por pensamiento oculto que los medidores de tokens de abajo dejan ver.
Claude Sonnet 5 superado · 8/8 cases
## The Bug The function has two related off-by-one issues rooted in the same cause: `cur` and `best` are initialized to `0` instead of `1`, treating run lengths as the number of *matches* between adjacent elements rather than the number of *elements* in the run. This means a single-element input never enters the loop (since `range(1, 1)` is empty) and returns `0` instead of the correct answer of `1`. It also means that for any input, the run length is undercounted by exactly one — a run of 3 equal elements only produces 2 "match" increments, so `best` ends up as 2 instead of 3. The claim that it "misses runs that end at the last index" is actually a symptom of this same undercount: the logic does update `best` correctly on every iteration (including the last), but because it's counting matches instead of elements, the final reported value is always one less than the true longest run. […]
salida 536 tok latencia 7.1 s
Kimi K2.7 Code superado · 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 that the function counts *transitions* between equal neighbors instead of counting the items themselves, so a single-element list never enters the loop and returns `0` instead of `1`. Likewise, resetting `cur` to `0` on a mismatch forgets the current item that starts a new run, which causes runs that end at the last index to be undercounted. Initializing both `best` and `cur` to `1` and resetting `cur` to `1` fixes both issues.
salida 410 tok (+222 pensamiento) latencia 9.4 s
Si el arreglo es realmente correcto (ejecutable), la densidad de la explicación, y la eficiencia en tokens sobre una tarea acotada.
Claude Sonnet 5 superado · 5/5 fields, guidance null
```json { "company": "Meridian Robotics", "quarter": "Q3", "revenue_usd_m": 412, "growth_pct": 11.5, "guidance": null } ```
salida 348 tok latencia 5.3 s
Kimi K2.7 Code 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" } ```
salida 721 tok (+663 pensamiento) latencia 13.2 s
Obediencia al esquema (sin campos inventados), presión de alucinación (guidance se retiene explícitamente), y diferencias en la ruta de salida estructurada.
Claude Sonnet 5 superado · 123 words, 0 banned, 1 question
## Introducing Cross-Provider Prompt Caching We're rolling out a new caching layer for our API gateway that works across every major LLM provider you connect through it. Instead of resending identical prompts and paying full price each time, the gateway recognizes repeated requests and serves cached responses in milliseconds—regardless of whether you're routing to OpenAI, Anthropic, or Google. What does this mean for your bill and your latency? Both drop, often significantly, especially for teams running high-volume, repetitive workloads like customer support bots or batch content generation. The cache is configurable per route, with adjustable TTLs and invalidation rules, so you stay in control of freshness versus cost. Available now for all Pro and Enterprise plans. Check your dashboard to enable it today.
salida 259 tok latencia 4.8 s
Kimi K2.7 Code superado · 120 words, 0 banned, 1 question
We're introducing Prompt Cache for our API Gateway, a new capability that stores prompt responses across multiple AI providers. By caching replies, you reduce redundant calls, lower latency, and cut costs without changing how you build. Teams using several model providers can now reuse identical prompt results instantly, keeping traffic consistent and budgets predictable. Responses are keyed by provider, model, and exact prompt, so you always get the right result. Configure TTL, hit thresholds, and eviction rules from a single dashboard. It fits into your existing routing and requires no code changes. Setup takes minutes and works with your current endpoints. Want to see how much latency and spend you can trim? Check the docs to enable Prompt Cache today.
salida 2375 tok (+2235 pensamiento) latencia 38.6 s
Obediencia a las restricciones (presupuesto de palabras, lista de palabras prohibidas, la única pregunta), huella de estilo, y control de la longitud.
Cambia entre ellos con una línea
Ambos IDs están en cada pestaña a continuación — el par de líneas resaltadas es la única edición. Mismo endpoint, misma clave, misma estructura de solicitud.
from openai import OpenAI
client = OpenAI(
base_url="https://synthorai.io/v1",
api_key="sk-syn-...",
)
resp = client.chat.completions.create(
model="claude-sonnet-5",
# model="kimi-k2.7-code", # 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: "claude-sonnet-5",
// model: "kimi-k2.7-code", // 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": "claude-sonnet-5",
# "model": "kimi-k2.7-code", # 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: "claude-sonnet-5",
// Model: "kimi-k2.7-code", // 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("claude-sonnet-5")
// .model("kimi-k2.7-code") // 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, Claude Sonnet 5 o Kimi K2.7 Code?
Kimi K2.7 Code es más barato en entrada / 1m tokens ($0.95 vs $2, con una diferencia de 2.1×). Otras filas pueden indicar lo contrario — la tabla anterior muestra la ficha completa, y el costo real depende de su combinación.
¿Puedo hacer pruebas A/B de Claude Sonnet 5 frente a Kimi K2.7 Code sin dos integraciones?
Sí. Ambos se sirven a través del mismo endpoint compatible con OpenAI con una clave API — el cambio es una modificación de una línea en la cadena del modelo, por lo que puede enrutar una fracción del tráfico a cada uno y comparar las facturas directamente.
¿Admiten Claude Sonnet 5 y Kimi K2.7 Code caché de prompts?
Sí — ambos cobran las lecturas en caché por debajo de su tarifa de entrada, por lo que las cargas de trabajo con prefijo caliente cuestan menos de lo que sugieren las tarifas de lista. Las filas exactas de lectura en caché están en la tabla de precios de arriba.