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Claude Haiku 5.5 vs Claude Sonnet 5.5

vs

Quale scegliere e quando

Sono due livelli della stessa famiglia. claude-haiku-5-5 costa $0.1 in input e $0.5 in output contro i $2 e $10 di claude-sonnet-5-5, 20x in meno su entrambi, con letture in cache a $0.01 contro $0.1. Il perimetro è lo stesso: un contesto di 1000000 token, un output massimo di 128000, input di testo e immagini e un knowledge cutoff al 2026-06. Nella pratica contano due differenze: claude-haiku-5-5 fattura a 5x i prompt oltre i 100000 token e permette di disattivare il thinking, mentre claude-sonnet-5-5 mantiene un'unica tariffa e ha il thinking sempre attivo. Scegli claude-haiku-5-5 per classificazione, routing ed estrazione ad alto volume, e claude-sonnet-5-5 come livello superiore.

Prezzi

Claude Haiku 5.5 Claude Sonnet 5.5 Δ
Input / 1M token $0.1 $2 0.05×
Output / 1M token $0.5 $10 0.05×
Lettura cache / 1M token $0.01 $0.1 0.1×
Scrittura in cache 1.25x (5m) / 2x (1h) 1.25x (5m) / 2x (1h) -

Tariffe lette dal catalogo live al momento della build; il listino aggiornato è sulla pagina di ciascun modello.

Dove si collocano: prezzo di input per 1M di token tra tutti i modelli di chat con questa unità di fatturazione (78, scala logaritmica)

Funzionalità

Claude Haiku 5.5 Claude Sonnet 5.5
Tool use sì sì
Controllo del ragionamento configurabile sempre attivo
Output strutturato sì sì
Prompt caching esplicito (contrassegni tu il prefisso) esplicito (contrassegni tu il prefisso)
Durata della cache 5m default, 1h option 5m default, 1h option
Prefisso minimo in cache 1024 token 1024 token

Specifiche

Claude Haiku 5.5 Claude Sonnet 5.5
Modalità di input testo immagine testo immagine
Modalità di output testo testo
Rilascio 2026-10-07 2026-09-28
Knowledge cutoff 2026-06 2026-06
Finestra di contesto 1M 1M
Output massimo 128K 128K
Parametro di ragionamento output_config.effort thinking.type
Valori accettati
output_config.effort
  • low
  • medium
  • high
  • xhigh
  • max
thinking.type
  • adaptive (default)
  • between_tools
Valore di default medium adaptive, effort high

Le specifiche sono riprese dalla documentazione di ciascun provider; se un provider non pubblica un dato, la riga viene omessa e non dedotta. Fonti complete: Claude Haiku 5.5 · Claude Sonnet 5.5

Un solo prompt, entrambi i modelli, misurati attraverso il gateway

PROMPT Explain to a CFO, in exactly three sentences, why our LLM bill doubled after we enabled reasoning. VERIFICA esattamente 3 frasi

Claude Haiku 5.5 superato · 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.

out 332 tok (+159 ragionamento) latenza 2.9 s

Claude Sonnet 5.5 superato · 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.

out 205 tok latenza 3.6 s

Rispetto delle istruzioni (esattamente tre frasi: si contano), adattamento al destinatario (registro da CFO) e lo scarto di fatturazione dovuto al ragionamento nascosto, che i contatori di token qui sotto mettono in luce.

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. VERIFICA la correzione supera i test

Claude Haiku 5.5 superato · 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 `[]`. […]

out 1012 tok (+591 ragionamento) latenza 6.0 s

Claude Sonnet 5.5 superato · 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. […]

out 444 tok latenza 6.7 s

Se la correzione è davvero giusta (si può eseguire), quanto è densa la spiegazione e quanti token servono per un compito circoscritto.

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. VERIFICA JSON valido, schema esatto

Claude Haiku 5.5 superato · 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.

out 725 tok (+572 ragionamento) latenza 4.6 s

Claude Sonnet 5.5 superato · 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.

out 300 tok latenza 3.5 s

Aderenza allo schema (nessun campo inventato), tentazione di allucinare (il testo dice espressamente che la guidance non viene comunicata) e differenze tra i percorsi di output strutturato.

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. VERIFICA 120 parole, 0 parole vietate

Claude Haiku 5.5 superato · 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.

out 2164 tok (+1929 ragionamento) latenza 9.9 s

Claude Sonnet 5.5 superato · 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.

out 266 tok latenza 3.8 s

Rispetto dei vincoli (budget di parole, elenco di parole vietate, l'unica domanda), impronta stilistica e controllo della lunghezza.

Passa dall'uno all'altro cambiando una sola riga

In ogni scheda qui sotto ci sono entrambi gli id: le due righe evidenziate sono l'unica modifica. Stesso endpoint, stessa chiave, stessa struttura della richiesta.

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",  # decommenta questa riga, commenta quella sopra
    messages=[{"role": "user", "content": "Summarize this diff"}],
    reasoning_effort="medium",
)
print(resp.choices[0].message.content)

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FAQ

Qual è il più economico, Claude Haiku 5.5 o Claude Sonnet 5.5?

Claude Haiku 5.5 costa meno alla voce Input / 1M token ($0.1 contro $2, 20× di differenza). Altre voci potrebbero dire il contrario: la tabella qui sopra riporta il listino completo, e il costo reale dipende dal tuo mix di utilizzo.

Posso fare un A/B test di Claude Haiku 5.5 contro Claude Sonnet 5.5 senza due integrazioni?

Sì. Si chiamano entrambi dallo stesso endpoint compatibile con OpenAI, con una sola chiave API. Per passare dall'uno all'altro basta cambiare la stringa del modello in una riga, quindi puoi mandare una parte del traffico a ciascuno e confrontare direttamente i costi.

Claude Haiku 5.5 e Claude Sonnet 5.5 supportano il prompt caching?

Sì: entrambi fanno pagare le letture dalla cache meno della tariffa di input, quindi i carichi di lavoro con un prefisso già in cache costano meno di quanto facciano pensare le tariffe di listino. Le voci esatte per la lettura dalla cache sono nella tabella dei prezzi qui sopra.

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