Claude Opus 4.8 vs Claude Opus 5
Cuál usar y cuándo
Tarifa idéntica en cada línea: $5 por millón de tokens de entrada, $25 de salida, $0.5 en lecturas de caché, el mismo contexto de 1000000 tokens y los mismos 128000 tokens de salida, así que el coste no decide aquí. claude-opus-5 cachea desde 512 tokens donde claude-opus-4-8 necesita 1024, y llega con el pensamiento activado más los niveles de esfuerzo xhigh y max, mientras que claude-opus-4-8 lo deja desactivado por defecto. Actualizar cambia el comportamiento y la tasa de acierto de caché en prefijos cortos, no la factura: presupuesta lo mismo y revisa los ajustes de thinking y effort que hubieras fijado.
Benchmarks
14 medidos en ambos.
Publicado por los proveedores: Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
Precios
| Claude Opus 4.8 | Claude Opus 5 | Δ | |
|---|---|---|---|
| Entrada / 1M tokens | $5 | $5 | = |
| Salida / 1M tokens | $25 | $25 | = |
| Lectura de caché / 1M tokens | $0.5 | $0.5 | = |
| Escritura en caché | 1.25x (5m) / 2x (1h) | 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 64 modelos de chat en esta unidad de facturación (escala logarítmica)
Capacidades
| Claude Opus 4.8 | Claude Opus 5 | |
|---|---|---|
| Uso de herramientas | sí | sí |
| Control de pensamiento | configurable | configurable |
| Salida estructurada | sí | sí |
| Caché de prompt | explícito (marcas el prefijo) | explícito (marcas el prefijo) |
| Tiempo de vida de la caché | 5m default, 1h option | 5m default, 1h option |
| Prefijo mínimo en caché | 1024 tokens | 1024 tokens |
Especificaciones
| Claude Opus 4.8 | Claude Opus 5 | |
|---|---|---|
| Modalidades de entrada | texto imagen | texto imagen |
| Modalidades de salida | texto | texto |
| Lanzamiento | 2026-05-28 | 2026-07-24 |
| Límite de conocimiento | 2026-01 | 2026-05 |
| Ventana de contexto | 1M | 1M |
| Salida máxima | 128K | 128K |
| Parámetro de pensamiento |
|
|
| Valores aceptados | thinking.type
effort
"enabled" returns 400 | thinking.type
effort
|
| Predeterminado | thinking off effort
| thinking on effort
|
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 Opus 4.8 · Claude Opus 5
Un prompt, ambos modelos - medido a través del gateway
Claude Opus 4.8 superado · 3 sentences
Reasoning models generate a hidden "thinking" phase before producing their final answer, and we're billed for every one of those internal tokens even though they never appear in the user-facing output. Because this deliberation often runs several times longer than the answer itself, our token consumption per request grew substantially even though our request volume stayed flat. The upside is that this trade buys us materially higher accuracy on complex tasks, so the right question isn't just cost but whether the improved output quality justifies the per-query premium for each use case.
salida 162 tok latencia 4.2 s
Claude Opus 5 superado · 3 sentences
When reasoning is enabled, the model generates a hidden chain of "thinking" tokens before it writes the visible answer, and those internal tokens are billed at the output rate even though no one ever sees them — commonly 3–10x more tokens than the response itself. Because output tokens are priced several times higher than input tokens, that added volume hits us at our most expensive rate, which is why the bill roughly doubled while our request count stayed flat. The spend is controllable: we can cap the reasoning budget per call, route only genuinely hard requests to reasoning mode and cheaper models for the rest, and track cost per successfully completed task rather than cost per token, since a single correct answer usually beats three cheap retries plus human cleanup.
salida 529 tok latencia 9.0 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 Opus 4.8 superado · 8/8 cases
## Fixed Function ```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 else: cur = 1 best = max(best, cur) return best ``` ## Explanation of the Bug The original code counted **transitions between equal adjacent elements** rather than the **length of the run** itself. It initialized `best` and `cur` to `0` and incremented `cur` only when a match was found, so a run of length *N* recorded a count of *N-1* (e.g., a single element `[5]` never enters the loop and returns `0` instead of `1`, and `[5, 5]` returns `1` instead of `2`)—this is the off-by-one. […]
salida 495 tok latencia 9.2 s
Claude Opus 5 superado · 8/8 cases
## Fixed version ```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 else: cur = 1 if cur > best: best = cur return best ``` ## The bug The counters were tracking the wrong quantity: `cur` counted *matching adjacent pairs* rather than *elements in the current run*, and both counters started at `0` instead of `1`. Since a run of length *n* contains only *n − 1* adjacent equal pairs, every result came back one too small — `[1, 1]` returned 1, `[3, 3, 3]` returned 2 — and a single-element list returned 0 because the loop body never executed and `best` kept its initial value of `0`. […]
salida 1094 tok latencia 16.1 s
Si el arreglo es realmente correcto (ejecutable), la densidad de la explicación, y la eficiencia en tokens sobre una tarea acotada.
Claude Opus 4.8 superado · 5/5 fields, guidance null
```json { "company": "Meridian Robotics", "quarter": "Q3", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null } ```
salida 72 tok latencia 1.8 s
Claude Opus 5 superado · 5/5 fields, guidance "Withheld pending Q4 close"
```json { "company": "Meridian Robotics", "quarter": "Q3", "revenue_usd_m": 412, "growth_pct": 11, "guidance": "Withheld pending Q4 close" } ``` Notes on judgment calls: - **quarter**: "September quarter" is labeled Q3 because the text refers to an upcoming "Q4 close," implying a calendar-aligned fiscal year. No fiscal year is given. - **revenue_usd_m**: 412 is stated as constant-currency and hedged ("call it"), so it's approximate; the currency is not explicitly stated as USD. - **growth_pct**: "up eleven and change" means slightly more than 11, so 11 is a floor rather than an exact figure.
salida 539 tok latencia 7.6 s
Obediencia al esquema (sin campos inventados), presión de alucinación (guidance se retiene explícitamente), y diferencias en la ruta de salida estructurada.
Claude Opus 4.8 superado · 121 words, 0 banned, 1 question
**Introducing Cross-Provider Prompt Caching** Today we're launching prompt caching directly within our API gateway, designed to cut latency and reduce your inference costs. When identical or overlapping prompts hit the gateway, cached responses are served instantly instead of being routed to the underlying model provider again. What makes this different from provider-specific caching? Our system works across OpenAI, Anthropic, Google, and any custom endpoint you configure, so you maintain one consistent cache regardless of which model handles a request. You control cache duration, invalidation rules, and scope through simple configuration settings. Teams running high-volume workloads can expect meaningful savings on repeated queries and faster response times for end users. Cross-provider prompt caching is available now for all customers on paid plans.
salida 258 tok latencia 5.3 s
Claude Opus 5 superado · 123 words, 0 banned, 1 question
**Cross-Provider Prompt Caching** We're introducing Cross-Provider Prompt Caching in the API Gateway. Repeated system prompts, long documents, and few-shot examples are stored once at the gateway layer and reused across OpenAI, Anthropic, Google, and self-hosted models. Instead of paying full input token costs on every request, your application sends a cache reference, and the gateway rehydrates the context before forwarding it downstream. Why does that matter? Teams running high-volume agents and retrieval pipelines typically see input token spend fall 40 to 70 percent, with median latency dropping by several hundred milliseconds. Caches are scoped per project, encrypted at rest, and invalidated automatically when a prompt template changes. Enable it with a single header, and see the docs for TTL tuning and per-route controls.
salida 1593 tok latencia 19.1 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-opus-4-8",
# model="claude-opus-5", # 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-opus-4-8",
// model: "claude-opus-5", // 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-opus-4-8",
# "model": "claude-opus-5", # 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-opus-4-8",
// Model: "claude-opus-5", // 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-opus-4-8")
// .model("claude-opus-5") // 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 Opus 4.8 o Claude Opus 5?
Muestran el mismo entrada / 1m tokens ($5), por lo que el precio no es el factor decisivo en este caso - vea las especificaciones y capacidades a continuación.
¿Puedo hacer pruebas A/B de Claude Opus 4.8 frente a Claude Opus 5 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 Opus 4.8 y Claude Opus 5 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.