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Claude Opus 5 vs Claude Sonnet 5

vs

Cuál usar y cuándo — veredicto seleccionado, no una tabla de benchmarks

La clásica decisión de escalar o reducir nivel, ahora con una diferencia de precio de 2.5× ($5/$25 vs $2/$10) y contextos idénticos de 1M. Sonnet 5 se posicionó como una mejora directa de capacidad respecto a 4.6 — cubre por completo la mayor parte del tráfico de producto; reserve Opus 5 para la cola de tareas de agentes a largo plazo, donde su profundidad es el objetivo fundamental.

Precios

Claude Opus 5 Claude Sonnet 5 Δ
Entrada / 1M tokens $5 $2 2.5×
Salida / 1M tokens $25 $10 2.5×
Lectura de caché / 1M tokens $0.5 $0.2 2.5×
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 63 modelos de chat en esta unidad de facturación (escala logarítmica)

Capacidades

Claude Opus 5 Claude Sonnet 5
Uso de herramientas
Control de pensamiento configurable configurable
Salida estructurada
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 Claude Sonnet 5
Modalidades de entrada texto imagen texto imagen
Modalidades de salida texto texto
Lanzamiento 2026-07-24 2026-06-30
Límite de conocimiento 2026-05 2026-01
Ventana de contexto 1M 1M
Salida máxima 128K 128K
Parámetro de pensamiento
  • thinking.type
  • output_config.effort
  • thinking.type "adaptive"
  • output_config.effort
Valores aceptados
thinking.type
  • adaptive
  • disabled
effort
  • low
  • medium
  • high
  • xhigh
  • max
thinking.type
  • adaptive
  • disabled
effort
  • low
  • medium
  • high
  • xhigh
  • max

"enabled" returns 400

Predeterminado

thinking on

effort
  • high (Claude API and Claude Code)

thinking on (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 · Claude Sonnet 5

Un prompt, ambos modelos — medido a través del gateway

PROMPT Explain to a CFO, in exactly three sentences, why our LLM bill doubled after we enabled reasoning. COMPROBAR exactamente 3 frases

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

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

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.

PROMPT This function is supposed to return the longest run of consecutive equal items, but callers report it is off by one on single-element inputs and misses runs that end at the last index. Fix it and explain the bug in one paragraph. COMPROBAR el arreglo pasa las pruebas

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

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

Si el arreglo es realmente correcto (ejecutable), la densidad de la explicación, y la eficiencia en tokens sobre una tarea acotada.

PROMPT Extract a JSON object with fields {company, quarter, revenue_usd_m, growth_pct, guidance} from this text. Use null for anything not stated; add no other fields. COMPROBAR JSON válido, esquema exacto

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

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

Obediencia al esquema (sin campos inventados), presión de alucinación (guidance se retiene explícitamente), y diferencias en la ruta de salida estructurada.

PROMPT Write a 120-word product announcement for an API gateway feature that caches prompts across providers. Forbidden words: "seamless", "unlock", "game-changer", "revolutionize", "empower". Exactly one sentence must be a question. COMPROBAR 120 palabras, 0 palabras prohibidas

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

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

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",
    # model="claude-sonnet-5",  # descomenta esta línea, comenta la de arriba
    messages=[{"role": "user", "content": "Summarize this diff"}],
    reasoning_effort="medium",
)
print(resp.choices[0].message.content)

Obtén una clave de API →

Preguntas frecuentes

¿Cuál es más barato, Claude Opus 5 o Claude Sonnet 5?

Claude Sonnet 5 es más barato en entrada / 1m tokens ($2 vs $5, 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 Opus 5 frente a Claude Sonnet 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 y Claude Sonnet 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.

Comparaciones relacionadas

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