Claude Fable 5.1 vs Claude Opus 5.5
Claude Fable 5.1 se ofrece por invitación. Las cifras mostradas a continuación son las tarifas actuales, pero las llamadas requieren primero un permiso para el espacio de trabajo; solicítanos acceso antes de desarrollar en base a esta comparación.
Cuál usar y cuándo
Estos dos modelos de Anthropic coinciden casi exactamente en sus características: ambos admiten texto e imagen de entrada y devuelven texto, ambos ofrecen un contexto de 1000000 tokens con un máximo de 128000 de salida, ambos incluyen chat, código, thinking, herramientas y razonamiento, y ninguno permite desactivar el thinking. La diferencia está en la tarifa, donde claude-fable-5-1 cuesta $10 de entrada y $50 de salida frente a los $4 y $20 de claude-opus-5-5, 2.5x más en ambos tramos, además de $0.25 frente a $0.2 en lecturas de caché. Elige claude-opus-5-5, lanzado el 2026-09-22, para obtener las mismas prestaciones a menor costo, y elige claude-fable-5-1 solo si ya estás estandarizado en él.
Benchmarks
9 medidos en ambos.
Publicado por los proveedores: Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
Precios
| Claude Fable 5.1 | Claude Opus 5.5 | Δ | |
|---|---|---|---|
| Entrada / 1M tokens | $10 | $4 | 2.5× |
| Salida / 1M tokens | $50 | $20 | 2.5× |
| Lectura de caché / 1M tokens | $0.25 | $0.2 | 1.3× |
| 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 74 modelos de chat en esta unidad de facturación (escala logarítmica)
Capacidades
| Claude Fable 5.1 | Claude Opus 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 Fable 5.1 | Claude Opus 5.5 | |
|---|---|---|
| Modalidades de entrada | texto imagen | texto imagen |
| Modalidades de salida | texto | texto |
| Lanzamiento | 2026-09-01 | 2026-09-22 |
| Límite de conocimiento | 2026-06 | 2026-06 |
| Ventana de contexto | 1M | 1M |
| Salida máxima | 128K | 128K |
| Parámetro de pensamiento | output_config.effort (thinking is adaptive-only and always on) | output_config.effort |
| Valores aceptados | effort
| output_config.effort
|
| Predeterminado | thinking always on (adaptive) effort
| medium |
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 Fable 5.1 · Claude Opus 5.5
Un prompt, ambos modelos - medido a través del gateway
Claude Fable 5.1 superado · 3 sentences
When we enabled reasoning, the model started generating a long chain of internal "thinking" tokens before producing each answer, and we're billed for those tokens at the same output rate even though users never see them. In practice, that hidden reasoning often runs several times longer than the visible response, so the total tokens per request roughly doubled while the number of requests stayed flat. The upside is measurably better accuracy on complex tasks, so the right conversation is whether that quality gain justifies the cost—and if not, we can restrict reasoning to the workflows that actually need it rather than turning it on globally.
salida 186 tok latencia 3.2 s
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
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 Fable 5.1 superado · 8/8 cases
```python def longest_run(xs): if not xs: return 0 best = cur = 1 for i in range(1, len(xs)): cur = cur + 1 if xs[i] == xs[i - 1] else 1 best = max(best, cur) return best ``` The bug is that `cur` counts *matches between adjacent elements* rather than *elements in the run*: it starts at 0 and only increments when `xs[i] == xs[i-1]`, so a run of length k produces a count of k−1. That's why a single element returns 0 instead of 1, and why every other run also comes out one short — the "misses runs at the end" reports are the same off-by-one seen on inputs like `[1, 2, 2]`, where the trailing run of length 2 is reported as 1 (the original code does update `best` inside the loop, so end-of-list runs aren't actually skipped, just undercounted). […]
salida 882 tok (+507 pensamiento) latencia 3.4 s
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
Si el arreglo es realmente correcto (ejecutable), la densidad de la explicación, y la eficiencia en tokens sobre una tarea acotada.
Claude Fable 5.1 superado · 5/5 fields, guidance null
```json { "company": "Meridian Robotics", "quarter": "Q3 (September quarter)", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null } ``` Notes on judgment calls (not part of the object): - **revenue_usd_m**: The text says "412 for the topline" but never states the currency or that the figure is in millions; it's also on a constant-currency basis and prefaced with "call it," so it's approximate. If you want strict literalism, this should be `null`. - **growth_pct**: "Eleven and change" means slightly above 11%; the exact figure isn't given. - **quarter**: "September quarter" is Q3 on a calendar basis, consistent with the reference to a pending Q4 close. - **guidance**: Explicitly withheld, so `null`.
salida 633 tok (+349 pensamiento) latencia 3.8 s
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
Obediencia al esquema (sin campos inventados), presión de alucinación (guidance se retiene explícitamente), y diferencias en la ruta de salida estructurada.
Claude Fable 5.1 superado · 121 words, 0 banned, 1 question
**Introducing Cross-Provider Prompt Caching for the Gateway.** Starting today, your API gateway can cache prompts once and reuse them across OpenAI, Anthropic, Google, and any other provider you route to. Long system instructions, retrieval context, and few-shot examples are stored at the gateway layer, so switching models no longer means paying to resend the same tokens. Why send the same 8,000-token preamble to three different vendors when you could send it once? Our cache normalizes prompt segments, fingerprints them, and serves matching prefixes from memory before the request ever reaches an upstream API. Early customers report token costs falling by 30 to 60 percent on high-volume workloads, with median latency dropping alongside. Available today on all Team and Enterprise plans.
salida 1722 tok (+1463 pensamiento) latencia 2.9 s
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
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-fable-5-1",
# model="claude-opus-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-fable-5-1",
// model: "claude-opus-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-fable-5-1",
# "model": "claude-opus-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-fable-5-1",
// Model: "claude-opus-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-fable-5-1")
// .model("claude-opus-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 Fable 5.1 o Claude Opus 5.5?
Claude Opus 5.5 es más barato en entrada / 1m tokens ($4 vs $10, con una diferencia de 2.5×). 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 Fable 5.1 frente a Claude Opus 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 Fable 5.1 y Claude Opus 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.