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Claude Haiku 5.5 vs Claude Sonnet 5.5

vs

Cuál usar y cuándo

Son dos niveles de la misma familia. claude-haiku-5-5 cuesta $0.1 por entrada y $0.5 por salida frente a los $2 y $10 de claude-sonnet-5-5, 20x menos en ambos casos, con lecturas de caché a $0.01 frente a $0.1. Los límites son los mismos: un contexto de 1000000 tokens, 128000 de salida máxima, entrada de texto e imagen y un límite de conocimiento de 2026-06. En la práctica importan dos diferencias: claude-haiku-5-5 factura a 5x los prompts de más de 100000 tokens y permite desactivar el pensamiento, mientras que claude-sonnet-5-5 mantiene una sola tarifa y siempre piensa. Elija claude-haiku-5-5 para clasificación, enrutamiento y extracción en gran volumen, y claude-sonnet-5-5 como el nivel superior.

Precios

Claude Haiku 5.5 Claude Sonnet 5.5 Δ
Entrada / 1M tokens $0.1 $2 0.05×
Salida / 1M tokens $0.5 $10 0.05×
Lectura de caché / 1M tokens $0.01 $0.1 0.1×
Escritura en caché 1.25x (5m) / 2x (1h) 1.25x (5m) / 2x (1h) -

Tarifas tomadas del catálogo en vivo al generar el sitio; la página de cada modelo tiene la ficha de precios actualizada.

Dónde queda cada uno: precio de entrada por 1M de tokens entre los 78 modelos de chat que se facturan en esta unidad (escala logarítmica)

Capacidades

Claude Haiku 5.5 Claude Sonnet 5.5
Uso de herramientas sí sí
Control del razonamiento configurable siempre activo
Salida estructurada sí sí
Caché de prompts explícito (tú marcas el prefijo) explícito (tú marcas el prefijo)
Duración de la caché 5m default, 1h option 5m default, 1h option
Prefijo mínimo en caché 1024 tokens 1024 tokens

Especificaciones

Claude Haiku 5.5 Claude Sonnet 5.5
Modalidades de entrada texto imagen texto imagen
Modalidades de salida texto texto
Lanzamiento 2026-10-07 2026-09-28
Corte de conocimiento 2026-06 2026-06
Ventana de contexto 1M 1M
Salida máxima 128K 128K
Parámetro de razonamiento output_config.effort thinking.type
Valores aceptados
output_config.effort
  • low
  • medium
  • high
  • xhigh
  • max
thinking.type
  • adaptive (default)
  • between_tools
Valor por defecto medium adaptive, effort high

Las especificaciones se transcriben de la documentación de cada proveedor; si un proveedor no publica un dato, la fila se omite en vez de deducirlo. Fuentes completas: Claude Haiku 5.5 · Claude Sonnet 5.5

Un mismo prompt en los dos modelos, medido a través de la pasarela

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

Claude Haiku 5.5 superado · 3 sentences

Enabling reasoning makes the model generate a long internal chain of thought before its answer, and those thinking tokens are billed as output tokens, which cost several times more than input tokens. The model often produces many times more output per request than it did without reasoning, so the per-call cost rises even when the visible answer looks about the same length. Because that extra spend scales with every request, the bill roughly doubled, and we can control it by setting reasoning budgets, using reasoning only for tasks that need it, or routing simpler requests to a non-reasoning model.

salida 332 tok (+159 razonamiento) latencia 2.9 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

Cumplimiento de la instrucción (exactamente tres frases, se pueden contar), adaptación al público (registro de CFO) y la diferencia de facturación por razonamiento oculto que dejan ver los contadores de tokens de abajo.

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. COMPROBACIÓN la corrección pasa las pruebas

Claude Haiku 5.5 superado · 8/8 cases

```python def longest_run(xs): if not xs: return 0 best = 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 ``` The bug is that `cur` counts adjacent equal *pairs*, not run *elements*. A run of k equal items produces k−1 matching pairs, so the function returns one less than the true length, and a single-element input produces no pairs at all, so it returns 0 instead of 1. Resetting `cur` to 0 on a mismatch compounds this by starting every run one step behind. The fix starts both `cur` and `best` at 1 (a non-empty list always has a run of at least one element), resets `cur` to 1 on a mismatch, and updates `best` after every element rather than only inside the equal-branch. The empty-list guard keeps the function from returning 1 for `[]`. […]

salida 1012 tok (+591 razonamiento) latencia 6.0 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 la corrección funciona de verdad (se puede ejecutar), lo densa que es la explicación y la eficiencia en tokens en 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. COMPROBACIÓN JSON válido, esquema exacto

Claude Haiku 5.5 superado · 5/5 fields, guidance "Formal guidance withheld pending the Q4 "

```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": null, "growth_pct": 11, "guidance": "Formal guidance withheld pending the Q4 close" } ``` Notes: The text gives revenue as 412 but doesn't state the currency, so I left `revenue_usd_m` as null. Growth is "eleven and change," so 11 is an approximation.

salida 725 tok (+572 razonamiento) latencia 4.6 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

Respeto del esquema (sin campos inventados), tentación de alucinar (la guidance se omite a propósito) y diferencias entre las vías 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. COMPROBACIÓN 120 palabras, 0 palabras prohibidas

Claude Haiku 5.5 superado · 120 words, 0 banned, 1 question

**Introducing Prompt Cache Sync, the newest feature in GatewayOne.** Teams routing requests across OpenAI, Anthropic, and Google have often paid to process the same long system prompts again and again. Now the gateway stores those prompts once and reuses them wherever they appear, regardless of provider. Repeated context is matched by content hash, so cached prefixes return faster and cost less, with no changes to your application code. How much could this save your team? Enable Prompt Cache Sync from the dashboard, and your next billing report will show the difference. Cache entries follow your existing retention settings and can be purged per project at any time. Availability begins this week for all Scale and Enterprise plans worldwide. Start today.

salida 2164 tok (+1929 razonamiento) latencia 9.9 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

Respeto de las restricciones (límite de palabras, lista de palabras prohibidas, una única pregunta), sello de estilo y control de la longitud.

Cambia de uno a otro con una sola línea

Los dos ids aparecen en todas las pestañas de abajo; lo único que cambia es el par de líneas resaltadas. El endpoint, la clave y la estructura de la solicitud son los mismos.

from openai import OpenAI

client = OpenAI(
    base_url="https://synthorai.io/v1",
    api_key="sk-syn-...",
)

resp = client.chat.completions.create(
    model="claude-haiku-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)

Obtén tu clave API →

Preguntas frecuentes

¿Cuál es más barato, Claude Haiku 5.5 o Claude Sonnet 5.5?

Claude Haiku 5.5 es más barato en Entrada / 1M tokens ($0.1 frente a $2, una diferencia de 20×). En otras filas puede ser al revés: la tabla de arriba recoge todas las tarifas, y el coste real depende de tu combinación de uso.

¿Puedo hacer pruebas A/B de Claude Haiku 5.5 frente a Claude Sonnet 5.5 sin dos integraciones?

Sí. Los dos se sirven desde el mismo endpoint compatible con OpenAI y con una sola clave API. Para cambiar de uno a otro basta con tocar una línea, el nombre del modelo, así que puedes mandar una parte del tráfico a cada uno y comparar directamente las facturas.

¿Admiten Claude Haiku 5.5 y Claude Sonnet 5.5 caché de prompts?

Sí. Los dos cobran las lecturas de caché por debajo de su tarifa de entrada, así que las cargas de trabajo que reutilizan un prefijo ya cacheado cuestan menos de lo que sugieren los precios de lista. Las tarifas exactas de lectura de caché están en la tabla de precios de arriba.

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