Claude Opus 5 vs Claude Sonnet 5
Quale scegliere e quando — verdetto curato, non una tabella di benchmark
La classica alternativa verso l'alto o verso il basso, ora con un divario di prezzo di 2.5× ($5/$25 contro $2/$10) e contesti identici da 1M. Sonnet 5 è stato posizionato come un aggiornamento di capacità drop-in da 4.6 — per la maggior parte del traffico di prodotto è sufficiente; riserva Opus 5 per la coda agentica a lungo orizzonte in cui la sua profondità è ciò che conta.
Prezzi
| Claude Opus 5 | Claude Sonnet 5 | Δ | |
|---|---|---|---|
| Input / 1M token | $5 | $2 | 2.5× |
| Output / 1M token | $25 | $10 | 2.5× |
| Lettura cache / 1M token | $0.5 | $0.2 | 2.5× |
| Scrittura in cache | 1.25x (5m) / 2x (1h) | 1.25x (5m) / 2x (1h) | — |
Le tariffe provengono dal catalogo live al momento della build; la pagina di ciascun modello riporta la scheda attuale.
Dove si posizionano — prezzo di input per 1M di token rispetto a tutti gli 63 modelli chat con questa unità di fatturazione (scala logaritmica)
Capacità
| Claude Opus 5 | Claude Sonnet 5 | |
|---|---|---|
| Utilizzo dei tool | sì | sì |
| Controllo del ragionamento | configurabile | configurabile |
| Output strutturato | sì | sì |
| Prompt caching | esplicito (contrassegni il prefisso) | esplicito (contrassegni il prefisso) |
| Durata della cache | 5m default, 1h option | 5m default, 1h option |
| Prefisso minimo in cache | 1024 token | 1024 token |
Specifiche
| Claude Opus 5 | Claude Sonnet 5 | |
|---|---|---|
| Modalità di input | testo immagine | testo immagine |
| Modalità di output | testo | testo |
| Rilascio | 2026-07-24 | 2026-06-30 |
| Cutoff di conoscenza | 2026-05 | 2026-01 |
| Finestra di contesto | 1M | 1M |
| Output massimo | 128K | 128K |
| Parametro thinking |
|
|
| Valori accettati | thinking.type
effort
| thinking.type
effort
"enabled" returns 400 |
| Predefinito | thinking on effort
| thinking on (adaptive) effort
|
Le specifiche sono trascritte dalla documentazione di ciascun fornitore; una riga che un fornitore non pubblica viene omessa anziché essere dedotta. Fonti complete: Claude Opus 5 · Claude Sonnet 5
Un solo prompt, entrambi i modelli — misurati attraverso il gateway
Claude Opus 5 superato · 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.
out 529 tok latenza 9.0 s
Claude Sonnet 5 superato · 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.
out 194 tok latenza 4.3 s
Rispetto dell'istruzione (esattamente tre frasi — contabili), adattamento al pubblico (registro da CFO), e il divario di fatturazione del pensiero nascosto che i contatori di token qui sotto rivelano.
Claude Opus 5 superato · 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`. […]
out 1094 tok latenza 16.1 s
Claude Sonnet 5 superato · 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. […]
out 536 tok latenza 7.1 s
Se la correzione è davvero giusta (eseguibile), la densità della spiegazione, e l'efficienza in token su un compito delimitato.
Claude Opus 5 superato · 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.
out 539 tok latenza 7.6 s
Claude Sonnet 5 superato · 5/5 fields, guidance null
```json { "company": "Meridian Robotics", "quarter": "Q3", "revenue_usd_m": 412, "growth_pct": 11.5, "guidance": null } ```
out 348 tok latenza 5.3 s
Aderenza allo schema (nessun campo inventato), pressione di allucinazione (guidance è esplicitamente sospesa), e differenze nel percorso di output strutturato.
Claude Opus 5 superato · 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.
out 1593 tok latenza 19.1 s
Claude Sonnet 5 superato · 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.
out 259 tok latenza 4.8 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 con una sola riga
Entrambi gli id sono presenti in ogni scheda qui sotto — la coppia di righe evidenziata è 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-opus-5",
# model="claude-sonnet-5", # decommenta questa riga, commenta quella sopra
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",
// model: "claude-sonnet-5", // decommenta questa riga, commenta quella sopra
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",
# "model": "claude-sonnet-5", # decommenta questa riga, commenta quella sopra
"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",
// Model: "claude-sonnet-5", // decommenta questa riga, commenta quella sopra
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")
// .model("claude-sonnet-5") // decommenta questa riga, commenta quella sopra
.addUserMessage("Summarize this diff")
.reasoningEffort(ReasoningEffort.MEDIUM)
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));FAQ
Qual è più economico, Claude Opus 5 o Claude Sonnet 5?
Claude Sonnet 5 è più economico per input / 1m token ($2 contro $5, 2.5× di differenza). Altre righe potrebbero indicare il contrario — la tabella sopra riporta la scheda completa, e il costo reale dipende dal tuo mix.
Posso fare un A/B test di Claude Opus 5 contro Claude Sonnet 5 senza due integrazioni?
Sì. Entrambi sono serviti tramite lo stesso endpoint compatibile con OpenAI con una singola chiave API — il passaggio richiede la modifica della stringa del modello in una sola riga, quindi puoi instradare una frazione del traffico verso ciascuno e confrontare direttamente le fatture.
Claude Opus 5 e Claude Sonnet 5 supportano il prompt caching?
Sì — entrambi fatturano le letture in cache a un prezzo inferiore rispetto alla loro tariffa di input, quindi i carichi di lavoro con warm-prefix costano meno di quanto suggeriscano le tariffe di listino. Le righe esatte per la lettura in cache si trovano nella tabella dei prezzi qui sopra.