Claude Opus 4.8 — Spezifikationen
Compiled by Mustafa Ihsan from the vendor’s published documentation · Last updated
| Entwickler | Anthropic |
|---|---|
| Typ | LLM (Reasoning) |
| Modality | Text, Vision → Text |
| Parameter | Nicht offengelegt |
| Kontextfenster | 1 Mio. |
| Maximale Ausgabe | 128 K |
| Lizenz | Proprietär |
| Offene Gewichte | Nein |
| Veröffentlicht | 2026 |
| Eingabepreis | $5.00 /1M |
| Ausgabepreis | $25.00 /1M |
| API-Anbieter | Anthropic, AWS, Vertex AI, Azure |
What is Claude Opus 4.8?
Claude Opus 4.8 is Anthropic’s Opus-tier flagship for state-of-the-art long-horizon
agentic work, coding and knowledge tasks. It runs adaptive thinking only, and carries a
1M-token context window at standard pricing — there is no long-context premium, which
distinguishes it from several rivals that step the input rate up once a prompt passes a
threshold. At $5 in / $25 out per million tokens it sits squarely in the frontier bracket.
The no-premium 1M context is the practical reason to choose it. Models that double their
input rate above 200K tokens make large-context work unpredictable to budget: the same
feature costs a different amount depending on how much a user pasted in. Opus 4.8 bills the
same rate from the first token to the millionth, so a retrieval-heavy application can size
its prompts around what produces the best answer rather than around a pricing cliff. It has
since been joined by Claude Opus 5, which ranks higher on the Artificial Analysis
Intelligence Index at identical pricing — so for new builds, check whether Opus 5 is
available in your region and provider before defaulting to 4.8.
Claude Opus 4.8 pricing: API cost per 1M tokens
| Eingabe (pro 1 Mio. Token) | $5.00 |
|---|---|
| Ausgabe (pro 1 Mio. Token) | $25.00 |
| Verhältnis Output/Input | 5× |
| Gemischt (4:1 Input:Output) | $9.00 pro 1 Mio. Tokens |
What Claude Opus 4.8 costs per month
Tatsächliche monatliche Ausgaben bei einem 4:1-Input-zu-Output-Mix – dem Verhältnis, das typische Chat- oder RAG-Arbeitslasten tatsächlich erzeugen.
| Workload | Tokens/Monat | Kosten pro Monat |
|---|---|---|
| Nebenprojekt | 1 Mio. Eingabe / 0,25 Mio. Ausgabe | $11 |
| Kleines Team | 20 Mio. Eingabe / 5 Mio. Ausgabe | $225 |
| Produktion | 200 Mio. Eingabe / 50 Mio. Ausgabe | $2,250 |
Stellen Sie Ihre eigenen Berechnungen im KI-API-Kostenrechner.
Cheaper alternatives to Claude Opus 4.8
| Modell | Gewichteter Preis pro Million US-Dollar | Sie sparen |
|---|---|---|
| Kimi K3 offenere | $5.40 | 40 % günstiger |
| GLM 5.2 offenere | $2.00 | 78 % günstiger |
| Gemini 3.5 Flash | $3.00 | 67 % günstiger |
Häufig gestellte Fragen
How much does Claude Opus 4.8 cost per 1M tokens?
Claude Opus 4.8 costs $5.00 per 1M input tokens and $25.00 per 1M output tokens. At a typical 4:1 input-to-output mix that blends to about $9.00 per 1M tokens.
How much does Claude Opus 4.8 cost per month?
A small-team workload of 20M input and 5M output tokens a month costs about $225 on Claude Opus 4.8. A side project (1M in / 0.25M out) costs roughly $11.25.
What is a cheaper alternative to Claude Opus 4.8?
Kimi K3 is the strongest cheaper option in our database at $5.40 per 1M blended — about 40% less than Claude Opus 4.8. It is also open-weight, so self-hosting is an option.
Can I run Claude Opus 4.8 locally?
No. Claude Opus 4.8 is a closed, API-only model — the weights are not released, so it cannot be self-hosted.
Why does Claude Opus 4.8 charge more for output than input?
Output tokens are generated one at a time and cannot be batched the way a prompt can, so they cost the provider more to serve. Claude Opus 4.8 charges 5× more for output, which is why prompt-heavy workloads are far cheaper to run than generation-heavy ones.
Alle Claude-Modelle im direkten Preisvergleich: Claude-API-Preise.
Die Preise entsprechen den offiziell veröffentlichten Listenpreisen für die primäre API des jeweiligen Modells und werden regelmäßig aktualisiert, sobald Anbieter diese ändern. Volumen-, Batch- und Cached-Input-Rabatte sind nicht enthalten. Vergleichen Sie alle Modelle nebeneinander im Datenbank für KI-Modelle oder das LLM-Leaderboard.