LangMart: Meta: Llama 4 Maverick

Model Overview

Description

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.

Description

LangMart: Meta: Llama 4 Maverick is a language model provided by meta-llama. This model offers advanced capabilities for natural language processing tasks.

Provider

meta-llama

Specifications

Pricing

Capabilities

• Frequency penalty
• Logit bias
• Max tokens
• Min p
• Presence penalty
• Repetition penalty
• Response format
• Seed
• Stop
• Structured outputs
• Temperature
• Tool choice
• Tools
• Top k
• Top p

Detailed Analysis

Llama 4 Maverick on LangMart is Meta's flagship multimodal MoE model featuring 17B active parameters with 128 routed experts from a total pool of 400B parameters. This variant delivers 8x more expert granularity than Scout (128 vs 16 experts), enabling finer-grained specialization across knowledge domains for superior performance on complex reasoning tasks. Maverick achieves an ELO score of 1417 on LMSYS Chatbot Arena, outperforming GPT-4o (1343) and Gemini 2.0 Flash (1393), with 43.4% LiveCodeBench accuracy vs GPT-4o's 32.3%. In enterprise document analysis benchmarks, Maverick achieves 85-92% accuracy on complex conditional logic tasks compared to Scout's 45-70%, making it ideal for nuanced requirements and edge case detection. The model supports 1M token context window with early fusion of text and up to 8 image inputs, pretrained on 22 trillion multimodal tokens across 12 languages. OpenRouter provides flexible pricing at $0.50-0.59/M input tokens (varies by provider) with standard rate limits. Maverick uses Interleaved RoPE architecture for efficient long-context processing and activates only 17B parameters per forward pass despite its 400B total size. Choose OpenRouter's Maverick tier when you need advanced reasoning on complex tasks, precise multimodal understanding, creative writing, or sophisticated coding with competitive pricing and reliable availability.