Claude Opus 5.5 vs Claude Sonnet 5.5
Cuál usar y cuándo
Estos son dos niveles de la misma línea Claude, no gemelos: Anthropic posiciona a claude-opus-5-5 como el nivel superior, diseñado para programación agéntica de larga duración y trabajo de conocimiento, y a claude-sonnet-5-5 como el nivel inferior, al cual cataloga como más rápido. En papel, comparten un contexto de 1000000 tokens, 128000 de salida máxima, entrada de texto e imagen y lecturas de caché de $0.2, por lo que la lista de precios refleja la diferencia de nivel: claude-opus-5-5 cuesta $4 de entrada y $20 de salida, 2x claude-sonnet-5-5 a $2 y $10. Dirija el tráfico de alto volumen y sensible a la latencia a claude-sonnet-5-5, y envíe las tareas agénticas largas y difíciles a claude-opus-5-5.
Benchmarks
Claude Sonnet 5.5: el proveedor no ha publicado resultados de benchmarks.
Publicado por los proveedores: Alibaba (Qwen) Anthropic DeepSeek Google Moonshot OpenAI Z.ai
Precios
| Claude Opus 5.5 | Claude Sonnet 5.5 | Δ | |
|---|---|---|---|
| Entrada / 1M tokens | $4 | $2 | 2× |
| Salida / 1M tokens | $20 | $10 | 2× |
| Lectura de caché / 1M tokens | $0.2 | $0.2 | = |
| 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 76 modelos de chat en esta unidad de facturación (escala logarítmica)
Capacidades
| Claude Opus 5.5 | Claude Sonnet 5.5 | |
|---|---|---|
| Uso de herramientas | sí | sí |
| Control de pensamiento | siempre activo | siempre activo |
| 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 5.5 | Claude Sonnet 5.5 | |
|---|---|---|
| Modalidades de entrada | texto imagen | texto imagen |
| Modalidades de salida | texto | texto |
| Lanzamiento | 2026-09-22 | 2026-09-28 |
| Límite de conocimiento | 2026-06 | 2026-06 |
| Ventana de contexto | 1M | 1M |
| Salida máxima | 128K | 128K |
| Parámetro de pensamiento | output_config.effort | thinking.type |
| Valores aceptados | output_config.effort
| thinking.type
|
| Predeterminado | medium | adaptive, effort high |
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 5.5 · Claude Sonnet 5.5
Un prompt, ambos modelos - medido a través del gateway
Claude Opus 5.5 superado · 3 sentences
When we turned on reasoning, the model began "thinking out loud" before each answer, generating thousands of internal working tokens that users never see but that we are billed for just like the final response. Those hidden tokens are charged at the output rate, typically the most expensive rate, so the same question with the same visible answer now costs roughly twice as much or more. We can bring the bill back down without losing the quality gains by capping the reasoning budget and reserving reasoning mode for the complex tasks that actually benefit from it.
salida 263 tok (+102 pensamiento) latencia 5.7 s
Claude Sonnet 5.5 superado · 3 sentences
Enabling reasoning makes the model "think" before it answers, and that internal thinking is generated as extra text (reasoning tokens) that we are billed for at the same rate as output tokens, even though users never see it. Output tokens also cost several times more than input tokens, so a single request that used to produce a short answer can now generate many times more billable text behind the scenes. Because our request volume and prompts didn't change, the doubling comes almost entirely from this hidden thinking, and we can bring it down by limiting reasoning effort, turning it off for simple tasks, and reserving it for the workloads where the accuracy gain justifies the cost.
salida 205 tok latencia 3.6 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 5.5 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 ``` **Explanation:** The original code counted *matching adjacent pairs* rather than *items in a run*. Both `cur` and `best` started at 0, and `cur` reset to 0 on a mismatch, so a run of length *n* was reported as *n − 1*. For a single-element list the loop never executes, so it returned 0 instead of 1. For `[5, 5]` it returned 1 instead of 2. The fix treats every element as the start of a run of length 1: `best` and `cur` start at 1, and `cur` resets to 1 (not 0) when the value changes. An explicit check returns 0 for an empty list. The "misses runs at the last index" reports are the same off-by-one bug rather than a separate one. […]
salida 723 tok (+303 pensamiento) latencia 10.4 s
Claude Sonnet 5.5 superado · 8/8 cases
**The bug:** `cur` counts the number of *adjacent equal pairs* rather than the number of items in the run. A run of length k contains only k−1 such pairs, so every result is one too small. The counter also starts at 0 and the loop starts at index 1, so a single-element list never updates `best` and returns 0 instead of 1. (An empty list should return 0.) In addition, `cur` resets to 0 on a mismatch, when it should reset to 1, because the item at `xs[i]` begins a new run of length 1. The claim that it "misses runs ending at the last index" is really the same off-by-one: the final run is counted, but as one less than its true length. Because `best` is updated inside the loop on every extension, the last run isn't skipped. […]
salida 444 tok latencia 6.7 s
Si el arreglo es realmente correcto (ejecutable), la densidad de la explicación, y la eficiencia en tokens sobre una tarea acotada.
Claude Opus 5.5 superado · 5/5 fields, guidance "Withheld pending Q4 close"
```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": "Withheld pending Q4 close" } ``` **Judgment calls to check:** - **quarter**: Kept as "September quarter" rather than "Q3", since the fiscal calendar isn't stated. The text mentions an upcoming "Q4 close," so it may be fiscal Q3, but that's not certain. - **revenue_usd_m**: The text says only "412 for the topline." It doesn't state currency or units, and the figure is on a constant-currency basis, not reported USD. USD millions is an assumption. Use `null` if you need strictly stated values. - **growth_pct**: The source says "eleven and change," so the true figure is slightly above 11. 11 is an approximation. - **guidance**: Guidance was explicitly withheld. I recorded that rather than `null`, since the text addresses it directly.
salida 829 tok (+488 pensamiento) latencia 10.9 s
Claude Sonnet 5.5 superado · 5/5 fields, guidance null
Here is the extracted JSON: ```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null } ``` Notes on the choices: - **quarter**: The text says "September quarter" without specifying a fiscal year or Q-number, so I kept the wording as stated. - **revenue_usd_m**: The text gives "412" for the topline, with no currency or unit stated explicitly. I've taken it as 412 million USD per the field definition, but the text does not confirm that, and the figure is constant-currency. - **growth_pct**: "Eleven and change" means slightly above 11, so 11 is an approximation. The exact figure isn't given. - **guidance**: Guidance is withheld pending the Q4 close, so no guidance is stated, hence null.
salida 300 tok latencia 3.5 s
Obediencia al esquema (sin campos inventados), presión de alucinación (guidance se retiene explícitamente), y diferencias en la ruta de salida estructurada.
Claude Opus 5.5 superado · 120 words, 0 banned, 1 question
Today we're launching Cross-Provider Prompt Caching for our API gateway. Why pay twice for the same context? Our gateway now stores repeated prompt prefixes, system instructions, and document chunks, then reuses them whether your request goes to OpenAI, Anthropic, Google, or an open-source model. Teams running retrieval pipelines or long agent loops can cut token costs by up to 60 percent and trim latency on every cached call. Setup takes one configuration flag; no code changes are required. Cache rules respect tenant boundaries, expire on your schedule, and log every hit for audit review. Dashboards show savings by provider, route, and application in real time. Cross-Provider Prompt Caching is available on all Pro and Enterprise plans. Turn it on today.
salida 1263 tok (+1007 pensamiento) latencia 13.5 s
Claude Sonnet 5.5 superado · 122 words, 0 banned, 1 question
**Introducing Cross-Provider Prompt Caching** Your API gateway now caches prompts across every connected LLM provider, whether you route traffic to OpenAI, Anthropic, Google, or a self-hosted model. Here's how it works: the gateway fingerprints each prompt prefix, stores it centrally, and applies the right provider-specific caching mechanism automatically. When you switch providers or fail over during an outage, your cached context follows you. Early testing shows input token costs dropping by up to 70% on repeated system prompts and long documents, with latency improvements of 40% or more on cache hits. Tired of paying to send the same instructions thousands of times a day? Enable it with one configuration flag. Cache rules, TTLs, and per-team isolation are available in the dashboard today.
salida 266 tok latencia 3.8 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-5-5",
# model="claude-sonnet-5-5", # 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-5-5",
// model: "claude-sonnet-5-5", // 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-5-5",
# "model": "claude-sonnet-5-5", # 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-5-5",
// Model: "claude-sonnet-5-5", // 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-5-5")
// .model("claude-sonnet-5-5") // 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 5.5 o Claude Sonnet 5.5?
Claude Sonnet 5.5 es más barato en entrada / 1m tokens ($2 vs $4, con una diferencia de 2.0×). 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 Opus 5.5 frente a Claude Sonnet 5.5 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 5.5 y Claude Sonnet 5.5 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.