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Favicon für meta-llama

Meta: Llama 4 Scout

meta-llama/llama-4-scout

Modellgewichte
Vergleich

Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input (text and image) and multilingual output (text and code) across 12 supported languages. Designed for assistant-style interaction and visual reasoning, Scout uses 16 experts per forward pass and features a context length of 10 million tokens, with a training corpus of ~40 trillion tokens.

Built for high efficiency and local or commercial deployment, Llama 4 Scout incorporates early fusion for seamless modality integration. It is instruction-tuned for use in multilingual chat, captioning, and image understanding tasks. Released under the Llama 4 Community License, it was last trained on data up to August 2024 and launched publicly on April 5, 2025.

Modalitäten
Eingabe- / Ausgabepreis
0,10 $ / 0,30 $pro 1 Mio.
Kontext
1,3M
Veröffentlicht
5. Apr. 2025
Wissensstand
Aug. 2024
Vergleich
AnbieterPreiseLeistungVerfügbarkeitLeistungsvergleicheAppsAktivitätFAQEntdecken

Anbieter

Verschiedene Unternehmen hosten dasselbe Modell. OpenRouter leitet Ihre Anfrage je nach gewähltem Routing-Modus an eines davon weiter — Balanced (Preis + Geschwindigkeit), Nitro (am schnellsten) oder Floor (am günstigsten).

Preise

Der durchschnittliche Preis, den Kunden tatsächlich für dieses Modell zahlen, neben den von Anbietern veröffentlichten Preisen. Durch Caching und Rabatte liegt der tatsächlich gezahlte Preis oft deutlich unter dem Listenpreis.

Leistung

Der Durchsatz gibt an, wie schnell das Modell schreibt (Token pro Sekunde – höher ist besser). Die Latenz ist die gesamte Umlaufzeit (niedriger ist besser). TTFT ist die Zeit bis zum ersten Token – also die Wartezeit, bis etwas angezeigt wird (niedriger ist besser).

Verfügbarkeit

Verfügbarkeit ist der prozentuale Anteil der letzten 3 Tage, in dem mindestens ein Anbieter auf Anfragen reagiert hat. Die Verfügbarkeit der Inferenz ist der prozentuale Anteil der Zeit, in der Inferenz erfolgreich bereitgestellt wurde. OpenRouter überwacht dies kontinuierlich und verwendet den nächstbesten Anbieter, wenn ein Anbieter einen Fehler zurückgibt.

Leistungsvergleiche

Ergebnisse standardisierter Bewertungen und eigene Ausgaben dieses Modells zu gemeinsamen Vergleichstest-Prompts. Höhere Prozentwerte sind besser – und jeder Rang zeigt, wo dieses Modell unter den anhand dieser Quelle bewerteten Modellen liegt.

Zusammenfassung der Benchmark-Ergebnisse für Meta: Llama 4 Scout (Artificial Analysis und Design Arena)
QuelleBenchmarkErgebnis
Artificial AnalysisLlama 4 Scout Coding Index8,2
Artificial AnalysisLlama 4 Scout Agentic Index0,5
Artificial AnalysisLlama 4 Scout GPQA Diamond58.7%
Artificial AnalysisLlama 4 Scout HLE3.8%
Artificial AnalysisLlama 4 Scout IFBench39.5%
Artificial AnalysisLlama 4 Scout τ²-Bench Telecom15.5%
Artificial AnalysisLlama 4 Scout AA-LCR27.7%
Artificial AnalysisLlama 4 Scout τ-Bench Banking3.3%
Artificial AnalysisLlama 4 Scout GDPval-AA0.0%
Artificial AnalysisLlama 4 Scout CritPt0.0%
Artificial AnalysisLlama 4 Scout LiveCodeBench29.9%
Artificial AnalysisLlama 4 Scout SciCode21.3%
Artificial AnalysisLlama 4 Scout Terminal-Bench Hard1.5%
Artificial AnalysisLlama 4 Scout Terminal-Bench 4.00.0%
Artificial AnalysisLlama 4 Scout Terminal-Bench 2.13.7%
Artificial AnalysisLlama 4 Scout MMLU-Pro75.2%
Artificial AnalysisLlama 4 Scout AIME 202514.0%
Artificial AnalysisLlama 4 Scout AIME28.3%
Artificial AnalysisLlama 4 Scout MATH-50084.4%
Design ArenaLlama 4 Scout Models Arena Code Categories Elo788
Design ArenaLlama 4 Scout Models Arena Data Visualization Elo896
Design ArenaLlama 4 Scout Models Arena Game Development Elo773
Design ArenaLlama 4 Scout Models Arena UI Component Elo762
Design ArenaLlama 4 Scout Models Arena Website Elo750

Apps

Öffentliche Apps, die den meisten Datenverkehr an dieses Modell senden. Ein guter Anhaltspunkt dafür, wie echte Produktionsworkloads aussehen – und ein Hinweis darauf, für welche Anwendungsfälle dieses Modell am besten geeignet ist.

Aktivität

Token-Volumen und Anfrageaufkommen für dieses Modell im Zeitverlauf.

API

Direkt einsetzbarer Code zum Aufrufen dieses Modells. Die API von OpenRouter ist OpenAI-kompatibel – bei den meisten SDKs genügt es, die Basis-URL auszutauschen. Zwischen den Modellen ändert sich nur der unten stehende Modell-Slug.

Derzeit nicht über EU-regionales Routing verfügbar. Dokumentation zum regionsinternen Routing

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0,15 $0,45 $14,15s9 tps
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Nicht beim Routing im Modus Standard verwendet:Warum diese Endpunkte nicht verwendet werden
Flex
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Durchsatz
9Token/s

P50, bester Wert über alle Anbieter hinweg

Latenz
14,15s

P50, bester Anbieter

Verfügbarkeit (3 Tage)Das Modell war erreichbar. Die Anfrage wurde an einen Anbieter weitergeleitet.
100,00 %
Verfügbarkeit (3 Tage)Das Modell hat über einen beliebigen Anbieter ein Inferenzergebnis zurückgegeben. Fehler und leere Antworten wirken sich negativ darauf aus.
99,95 %

Verfügbarkeit in den letzten 3 Tagen

Letzte 72 Stunden
Verfügbarkeit 99,95 %
Vor 3 TagenVor 2 TagenGesternJetzt

Verfügbarkeit in den letzten 24 Stunden

OpenRouter Availability
99,95 %
Without Routing
84,99 %

Wenn bei einem vorgelagerten Anbieter ein Fehler auftritt, können wir diesen durch die Weiterleitung an einen anderen funktionsfähigen Anbieter beheben, sofern Ihre Anfragefilter dies zulassen. Sie können programmgesteuert auf die Verfügbarkeitsdaten pro Anbieter zugreifen, und zwar über die Endpunkt-API. Erfahren Sie mehr über unsere Optionen für Lastverteilung und Anbieter-Routing.

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186MToken

Häufig gestellte Fragen

Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input (text and image) and multilingual output (text and code) across 12 supported languages.

Llama 4 Scout kostet 0,10 $/M Eingabe-Token und 0,30 $/M Ausgabe-Token.

Llama 4 Scout hat ein Kontextfenster von 1.310.720 Token. Es unterstützt bis zu 16.384 Completion-Token.

Ja. Llama 4 Scout akzeptiert tools und tool_choice für Funktionsaufrufe. Es unterstützt auch strukturierte Ausgaben über ein JSON-Schema in response_format.

Llama 4 Scout akzeptiert Text und Bilder als Eingabe und gibt Text zurück.

Llama 4 Scout wird auf OpenRouter von 2 Anbietern bereitgestellt: DeepInfra und Google Vertex. Anfragen werden an den besten verfügbaren Anbieter weitergeleitet, bei Ausfällen wird automatisch zu den anderen gewechselt, und Sie können Anbieter über das Anbieter-Routing festlegen oder ausschließen.

Llama 4 Scout wurde am 5. April 2025 veröffentlicht. Der Wissensstand reicht bis zum 31. August 2024.

Weitere Modelle von Meta Llama

Llama 3.3 8B Instruct

A lightweight and ultra-fast variant of Llama 3.3 70B, for use when quick response times are needed most.

Text128K Kontext
Llama Guard 4 12B

Llama Guard 4 is a Llama 4 Scout-derived multimodal pretrained model, fine-tuned for content safety classification. Similar to previous versions, it can be used to classify content in both LLM inputs (prompt classification) and in LLM responses (response classification). It acts as an LLM—generating text in its output that indicates whether a given prompt or response is safe or unsafe, and if unsafe, it also lists the content categories violated.

Llama Guard 4 was aligned to safeguard against the standardized MLCommons hazards taxonomy and designed to support multimodal Llama 4 capabilities. Specifically, it combines features from previous Llama Guard models, providing content moderation for English and multiple supported languages, along with enhanced capabilities to handle mixed text-and-image prompts, including multiple images. Additionally, Llama Guard 4 is integrated into the Llama Moderations API, extending robust safety classification to text and images.

Text164K Kontext0,18 $ / 0,18 $
Llama 4 Maverick

Llama 4 Maverick 17B Instruct (128E) is a high-capacity multimodal language model from Meta, built on a mixture-of-experts (MoE) architecture with 128 experts and 17 billion active parameters per forward pass (400B total). It supports multilingual text and image input, and produces multilingual text and code output across 12 supported languages. Optimized for vision-language tasks, Maverick is instruction-tuned for assistant-like behavior, image reasoning, and general-purpose multimodal interaction.

Maverick features early fusion for native multimodality and a 1 million token context window. It was trained on a curated mixture of public, licensed, and Meta-platform data, covering ~22 trillion tokens, with a knowledge cutoff in August 2024. Released on April 5, 2025 under the Llama 4 Community License, Maverick is suited for research and commercial applications requiring advanced multimodal understanding and high model throughput.

Text524K Kontext0,1875 $ / 0,6525 $
Llama 4 Scout

Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input (text and image) and multilingual output (text and code) across 12 supported languages. Designed for assistant-style interaction and visual reasoning, Scout uses 16 experts per forward pass and features a context length of 10 million tokens, with a training corpus of ~40 trillion tokens.

Built for high efficiency and local or commercial deployment, Llama 4 Scout incorporates early fusion for seamless modality integration. It is instruction-tuned for use in multilingual chat, captioning, and image understanding tasks. Released under the Llama 4 Community License, it was last trained on data up to August 2024 and launched publicly on April 5, 2025.

Text1,3M Kontext0,10 $ / 0,30 $
Llama Guard 3 8B

Llama Guard 3 is a Llama-3.1-8B pretrained model, fine-tuned for content safety classification. Similar to previous versions, it can be used to classify content in both LLM inputs (prompt classification) and in LLM responses (response classification). It acts as an LLM – it generates text in its output that indicates whether a given prompt or response is safe or unsafe, and if unsafe, it also lists the content categories violated.

Llama Guard 3 was aligned to safeguard against the MLCommons standardized hazards taxonomy and designed to support Llama 3.1 capabilities. Specifically, it provides content moderation in 8 languages, and was optimized to support safety and security for search and code interpreter tool calls.

Text
Llama 3.3 70B Instruct

The Meta Llama 3.3 multilingual large language model (LLM) is a pretrained and instruction tuned generative model in 70B (text in/text out). The Llama 3.3 instruction tuned text only model is optimized for multilingual dialogue use cases and outperforms many of the available open source and closed chat models on common industry benchmarks.

Supported languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.

Model Card

Text131K Kontext0,10 $ / 0,32 $
Llama 3.2 90B Vision Instruct

The Llama 90B Vision model is a top-tier, 90-billion-parameter multimodal model designed for the most challenging visual reasoning and language tasks. It offers unparalleled accuracy in image captioning, visual question answering, and advanced image-text comprehension. Pre-trained on vast multimodal datasets and fine-tuned with human feedback, the Llama 90B Vision is engineered to handle the most demanding image-based AI tasks.

This model is perfect for industries requiring cutting-edge multimodal AI capabilities, particularly those dealing with complex, real-time visual and textual analysis.

Click here for the original model card.

Usage of this model is subject to Meta's Acceptable Use Policy.

Text131K Kontext
Llama 3.2 3B Instruct

Llama 3.2 3B is a 3-billion-parameter multilingual large language model, optimized for advanced natural language processing tasks like dialogue generation, reasoning, and summarization. Designed with the latest transformer architecture, it supports eight languages, including English, Spanish, and Hindi, and is adaptable for additional languages.

Trained on 9 trillion tokens, the Llama 3.2 3B model excels in instruction-following, complex reasoning, and tool use. Its balanced performance makes it ideal for applications needing accuracy and efficiency in text generation across multilingual settings.

Click here for the original model card.

Usage of this model is subject to Meta's Acceptable Use Policy.

Text131K Kontext0,05 $ / 0,33 $
Llama 3.2 1B Instruct

Llama 3.2 1B is a 1-billion-parameter language model focused on efficiently performing natural language tasks, such as summarization, dialogue, and multilingual text analysis. Its smaller size allows it to operate efficiently in low-resource environments while maintaining strong task performance.

Supporting eight core languages and fine-tunable for more, Llama 1.3B is ideal for businesses or developers seeking lightweight yet powerful AI solutions that can operate in diverse multilingual settings without the high computational demand of larger models.

Click here for the original model card.

Usage of this model is subject to Meta's Acceptable Use Policy.

Text60K Kontext0,027 $ / 0,201 $
Llama 3.2 11B Vision Instruct

Llama 3.2 11B Vision is a multimodal model with 11 billion parameters, designed to handle tasks combining visual and textual data. It excels in tasks such as image captioning and visual question answering, bridging the gap between language generation and visual reasoning. Pre-trained on a massive dataset of image-text pairs, it performs well in complex, high-accuracy image analysis.

Its ability to integrate visual understanding with language processing makes it an ideal solution for industries requiring comprehensive visual-linguistic AI applications, such as content creation, AI-driven customer service, and research.

Click here for the original model card.

Usage of this model is subject to Meta's Acceptable Use Policy.

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Llama 3.1 405B

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It has demonstrated strong performance compared to leading closed-source models in human evaluations.

To read more about the model release, click here. Usage of this model is subject to Meta's Acceptable Use Policy.

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Llama 3.1 405B Instruct

The highly anticipated 400B class of Llama3 is here! Clocking in at 128k context with impressive eval scores, the Meta AI team continues to push the frontier of open-source LLMs.

Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 405B instruct-tuned version is optimized for high quality dialogue usecases.

It has demonstrated strong performance compared to leading closed-source models including GPT-4o and Claude 3.5 Sonnet in evaluations.

To read more about the model release, click here. Usage of this model is subject to Meta's Acceptable Use Policy.

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Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 70B instruct-tuned version is optimized for high quality dialogue usecases.

It has demonstrated strong performance compared to leading closed-source models in human evaluations.

To read more about the model release, click here. Usage of this model is subject to Meta's Acceptable Use Policy.

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It has demonstrated strong performance compared to leading closed-source models in human evaluations.

To read more about the model release, click here. Usage of this model is subject to Meta's Acceptable Use Policy.

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LlamaGuard 2 8B

This safeguard model has 8B parameters and is based on the Llama 3 family. Just like is predecessor, LlamaGuard 1, it can do both prompt and response classification.

LlamaGuard 2 acts as a normal LLM would, generating text that indicates whether the given input/output is safe/unsafe. If deemed unsafe, it will also share the content categories violated.

For best results, please use raw prompt input or the /completions endpoint, instead of the chat API.

It has demonstrated strong performance compared to leading closed-source models in human evaluations.

To read more about the model release, click here. Usage of this model is subject to Meta's Acceptable Use Policy.

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It has demonstrated strong performance compared to leading closed-source models in human evaluations.

To read more about the model release, click here. Usage of this model is subject to Meta's Acceptable Use Policy.

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It has demonstrated strong performance compared to leading closed-source models in human evaluations.

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