This model is deprecated. We only continue performance benchmarking for the default 10k input token workload. Results for other workloads are historical and no longer updated.

Anthropic has launched a newer model, Claude Opus 5 (low). We suggest considering it instead.

For more information, see comparison of Claude Opus 5 (low) to other models and API provider benchmarks for Claude Opus 5 (low).

Claude Opus 4.7 (Non-reasoning, High Effort) logo
Anthropic
•

Proprietary model

•

Released April 2026

Claude Opus 4.7 (Non-reasoning, High Effort): Analyse von Intelligenz, Leistung und Preis

Benchmarks der API-Anbieter

Modellübersicht

Intelligenz

43
Artificial Analysis Intelligence Index
4 von 4 Einheiten für Intelligenz.

Geschwindigkeit

45.9
Ausgabetokens pro Sekunde
1 von 4 Einheiten für Geschwindigkeit.

Preis

Eingabe
5,00 $
pro 1 Mio. Tokens
Ausgabe
25,00 $
pro 1 Mio. Tokens
4 von 4 Einheiten für Preis.

Cache-Preis

Schreiben
6,25 $
pro 1 Mio. Tokens
Treffer
0,50 $
pro 1 Mio. Tokens
3 von 4 Einheiten für Cache-Preis.

Ausführlichkeit

k. A.
Ausgabetokens im Intelligence Index
Unbekannter Wert von 4 Einheiten für Ausführlichkeit.

Claude Opus 4.7 (Non-reasoning, High Effort) zählt bei der Intelligenz zu den führenden Modellen, ist aber besonders teuer im Vergleich zu anderen Modellen ohne Reasoning mit ähnlichem Preis. Das Modell unterstützt Text und Bilder als Eingabe und Text als Ausgabe. Das Kontextfenster umfasst 1M Tokens.

Claude Opus 4.7 (Non-reasoning, High Effort) erzielt im Artificial Analysis Intelligence Index einen Wert von 43 und liegt damit deutlich über dem Durchschnitt der vergleichbaren Modelle (Median: 19).

Der Preis für Claude Opus 4.7 (Non-reasoning, High Effort) beträgt $5.00 pro 1 Mio. Eingabetokens (teuer; Median: $2.00) und $25.00 pro 1 Mio. Ausgabetokens (teuer; Median: $8.00).

Mit 46 Tokens pro Sekunde ist Claude Opus 4.7 (Non-reasoning, High Effort) auffallend langsam (Median: 63).

ReasoningNein

Diese Seite zeigt die Version dieses Modells ohne Reasoning.

Möglicherweise gibt es auch eine Reasoning-Variante.

Eingabemodalität

Unterstützt: Text und Bilder

Ausgabemodalität

Unterstützt: Text

Kontextfenster1M
~1500 A4-Seiten in Arial mit Schriftgröße 12

Metriken werden mit Modellen derselben Klasse verglichen:

  • Modelle ohne Reasoning → Vergleich nur mit anderen Modellen ohne Reasoning
  • Reasoning-Modelle → Vergleich mit Reasoning-Modellen und Modellen ohne Reasoning
  • Modelle mit offenen Gewichten → Vergleich nur mit anderen Modellen mit offenen Gewichten derselben Größenklasse:
    • Sehr klein: ≤4B Parameter
    • Klein: 4B–40B Parameter
    • Mittel: 40B–150B Parameter
    • Groß: >150B Parameter
  • Proprietäre Modelle → Vergleich mit proprietären Modellen und Modellen mit offenen Gewichten derselben Preisklasse anhand eines Mischpreisverhältnisses von 3:1 für Eingabe/Ausgabe:
    • <$0.15 pro 1 Mio. Tokens
    • $0.15–$1 pro 1 Mio. Tokens
    • >$1 pro 1 Mio. Tokens

Wichtigste Ergebnisse

Intelligenz

Artificial Analysis Intelligence Index · Higher is better

Geschwindigkeit

Output tokens per second · Higher is better

Kosten pro Aufgabe

Weighted average cost (USD) per Intelligence Index task · Lower is better

Intelligenz

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR
Estimate (independent evaluation forthcoming)
Reasoning models are indicated by a lightbulb icon

Artificial Analysis Intelligence Index v4.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Artificial Analysis Intelligence Index by Open Weights / Proprietary

Artificial Analysis Intelligence Index v4.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR
Estimate (independent evaluation forthcoming)
Reasoning models are indicated by a lightbulb icon

Artificial Analysis Intelligence Index v4.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Indicates whether the model weights are available. Models are labelled as 'Commercial Use Restricted' if the weights are available but commercial use is limited (typically requires obtaining a paid license).

Benchmarks

Intelligence Evaluations

Intelligence evaluations measured independently by Artificial Analysis · Higher is better
GDPval-AA v2

Agentic real-world work tasks, (Elo-500)/2000

𝜏³-Banking

Agentic tool use

Terminal-Bench v2.1

Agentic coding & terminal use

SciCode

Coding

Humanity's Last Exam

Reasoning & knowledge

GPQA Diamond

Scientific reasoning

CritPt

Physics reasoning

AA-Omniscience Accuracy

Knowledge

AA-Omniscience Non-Hallucination Rate

1 - hallucination rate

AA-LCR

Long context reasoning

AA-Briefcase

Agentic knowledge work, Elo

AutomationBench-AA

Agentic SaaS workflows

Harvey LAB-AA

Legal agentic work, criterion pass rate

EnterpriseOps-Gym-AANew

Agentic business operations

IFBench

Instruction following

APEX-Agents-AA

Long-horizon agentic tasks

ITBench-AA

Kubernetes incident root-cause analysis

MMMU-Pro

Visual reasoning

Reasoning models are indicated by a lightbulb icon

While model intelligence generally translates across use cases, specific evaluations may be more relevant for certain use cases.

Artificial Analysis Intelligence Index v4.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

AA-Briefcase

AA-Briefcase Elo

AA-Briefcase is an agentic knowledge work benchmark developed by Artificial Analysis. AA-Briefcase Elo is a combined metric that aggregates rubric pass rate, analytical quality Elo and presentation Elo · Higher is better
Reasoning models are indicated by a lightbulb icon

AA-Briefcase Elo is a combined metric that aggregates analytical quality Elo, presentation Elo, and rubric pass rate, with rubric performance converted into Elo via synthetic head-to-head matches. Elo and 95% confidence interval bounds are clamped at 0.

AA-Omniscience

AA-Omniscience Index

AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct.
Reasoning models are indicated by a lightbulb icon

AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct.

Vergleiche des Intelligence Index

Intelligence Index vs. Cost per Intelligence Index Task

Artificial Analysis Intelligence Index · Weighted average cost (USD) per Artificial Analysis Intelligence Index task
Most attractive quadrant
Pareto line
Reasoning models are indicated by a lightbulb icon

Weighted average cost per Intelligence Index task. Each evaluation’s cost is calculated from input, cache hit, cache write, reasoning, and answer token prices, divided by task count, and weighted by its Intelligence Index weight.

Artificial Analysis Intelligence Index v4.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Kosten

Cost per Intelligence Index Task

Weighted average cost (USD) per Artificial Analysis Intelligence Index task, segmented by token type. Lower is better
Reasoning models are indicated by a lightbulb icon

Weighted average cost per Intelligence Index task. Each evaluation’s cost is calculated from input, cache hit, cache write, reasoning, and answer token prices, divided by task count, and weighted by its Intelligence Index weight.

Cost to Run Artificial Analysis Intelligence Index

Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index
Reasoning models are indicated by a lightbulb icon

The cost to run the evaluations in the Artificial Analysis Intelligence Index, calculated using the model's input, cache hit, cache write, reasoning, and answer token prices and the number of tokens used across evaluations (excluding repeats).

Pricing: Cache Hit, Input, and Output

Price (USD per M Tokens)
Reasoning models are indicated by a lightbulb icon

Price per token for cached prompts (previously processed), typically offering a significant discount compared to regular input price, represented as USD per million tokens. The values shown here are the cache hit price; cache write and cache storage are billed separately and vary by provider — see "Cache pricing by provider" for detail.

Kontextfenster

Context Window

Context window: tokens limit · Higher is better
Reasoning models are indicated by a lightbulb icon

Larger context windows are relevant to RAG (Retrieval Augmented Generation) LLM workflows which typically involve reasoning and information retrieval of large amounts of data.

Maximum number of combined input & output tokens. Output tokens commonly have a significantly lower limit (varied by model).

Geschwindigkeit

Gemessen anhand der Ausgabegeschwindigkeit (Tokens pro Sekunde)

Output Speed

Output tokens per second · Higher is better
Reasoning models are indicated by a lightbulb icon

Tokens per second received while the model is generating tokens (ie. after first chunk has been received from the API for models which support streaming).

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Time per Intelligence Index Task

Weighted average decode time (minutes) per task; excludes TTFT and overhead time · Lower is better
Reasoning models are indicated by a lightbulb icon

The weighted average time (seconds) per Artificial Analysis Intelligence Index task. This is calculated by dividing output tokens per task by output speed, weighted by the relative weights of each benchmark in the Intelligence Index.

Latenz

Gemessen anhand der Zeit (in Sekunden) bis zum ersten Token

Latency: Time To First Answer Token

Seconds to first answer token received · Accounts for reasoning model 'thinking' time
Reasoning models are indicated by a lightbulb icon

Time to first answer token received, in seconds, after API request sent. For reasoning models, this includes the 'thinking' time of the model before providing an answer. For models which do not support streaming, this represents time to receive the completion.

Ende-zu-Ende-Antwortzeit

Seconds to output 500 tokens, calculated based on time to first token, 'thinking' time for reasoning models, and output speed

End-to-End Response Time

Seconds to output 500 tokens, including reasoning model 'thinking' time · Lower is better
Reasoning models are indicated by a lightbulb icon

Seconds to receive a 500 token response. Key components:

  • Input time: Time to receive the first response token
  • Thinking time (only for reasoning models): Time reasoning models spend outputting tokens to reason prior to providing an answer. Amount of tokens based on the average reasoning tokens across a diverse set of 60 prompts (methodology details).
  • Answer time: Time to generate 500 output tokens, based on output speed

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Häufig gestellte Fragen

Häufige Fragen zu Claude Opus 4.7 (Non-reasoning, High Effort)

Claude Opus 4.7 (Non-reasoning, High Effort) wurde am 16. April 2026 veröffentlicht.

Claude Opus 4.7 (Non-reasoning, High Effort) wurde von Anthropic entwickelt.

Claude Opus 4.7 (Non-reasoning, High Effort) erzielt im Artificial Analysis Intelligence Index einen geschätzten Wert von 43 und liegt damit im Vergleich zu anderen Modellen ohne Reasoning einer ähnlichen Preisklasse deutlich über dem Durchschnitt (Median: 19).

Claude Opus 4.7 (Non-reasoning, High Effort) erzeugt Ausgaben mit 45.9 Tokens pro Sekunde (auf Grundlage der API von Anthropic). Dies ist am unteren Ende im Vergleich zu anderen Modellen ohne Reasoning einer ähnlichen Preisklasse (Median: 62.9 Tokens/s).

Claude Opus 4.7 (Non-reasoning, High Effort) hat eine Zeit bis zum ersten Token (TTFT) von 1.98 s (auf Grundlage der API von Anthropic). Dies ist etwas höher als der Durchschnitt im Vergleich zu anderen Modellen ohne Reasoning einer ähnlichen Preisklasse (Median: 1.77 s).

Claude Opus 4.7 (Non-reasoning, High Effort) kostet $5.00 pro 1 Mio. Eingabetokens (am oberen Ende; Median: $2.00) und $25.00 pro 1 Mio. Ausgabetokens (am oberen Ende; Median: $8.00). Die Angaben basieren auf der API von Anthropic.

Claude Opus 4.7 (Non-reasoning, High Effort) kostet $5.00 pro 1 Mio. Eingabetokens und $25.00 pro 1 Mio. Ausgabetokens (auf Grundlage der API von Anthropic). Bei einem Mischpreis mit einem Verhältnis von 7:2:1 für Cache-Treffer, Eingabe und Ausgabe entspricht dies $3.85 pro 1 Mio. Tokens. Der Preis kann je nach Anbieter variieren. Preise der Anbieter vergleichen

Nein, Claude Opus 4.7 (Non-reasoning, High Effort) ist kein Reasoning-Modell. Es antwortet direkt, ohne längeres Chain-of-Thought-Reasoning.

Claude Opus 4.7 (Non-reasoning, High Effort) unterstützt Text und Bilder als Eingabe.

Claude Opus 4.7 (Non-reasoning, High Effort) unterstützt Text als Ausgabe.

Ja, Claude Opus 4.7 (Non-reasoning, High Effort) unterstützt Bildeingaben und kann Bilder analysieren, beschreiben und Fragen dazu beantworten.

Ja, Claude Opus 4.7 (Non-reasoning, High Effort) ist multimodal. Es kann Text und Bilder als Eingabe verarbeiten und Text als Ausgabe erzeugen.

Claude Opus 4.7 (Non-reasoning, High Effort) hat ein Kontextfenster von 1.0M Tokens. Es bestimmt, wie viel Text und Gesprächsverlauf das Modell in einer einzelnen Anfrage verarbeiten kann.

Nein, Claude Opus 4.7 (Non-reasoning, High Effort) ist proprietär. Die Modellgewichte sind nicht öffentlich verfügbar.

Claude Opus 4.7 (Non-reasoning, High Effort) ist ein proprietäres Modell und Anthropic hat weder die Modellgröße noch die Parameterzahl offengelegt.

Claude Opus 4.7 (Non-reasoning, High Effort) erzielt im Artificial Analysis Intelligence Index einen Wert von 43. Dieser zusammengesetzte Benchmark bewertet Modelle in den Bereichen Schlussfolgern, Wissen, Mathematik und Programmierung.

Ja, Claude Opus 4.7 (Non-reasoning, High Effort) ist über 2 Anbieter per API verfügbar. API-Anbieter vergleichen

Claude Opus 4.7 (Non-reasoning, High Effort) ist über 2 API-Anbieter verfügbar. Anbieter vergleichen