TokenMix Research Lab · 2026-03-31

Llama 4 Maverick Review: 400B MoE Benchmark Results, API Pricing, and How It Compares to GPT-5.4 and Claude (2026)

Llama 4 Maverick is Meta's largest open-weight model -- 400B total parameters with 17B active per forward pass, spread across 128 Mixture-of-Experts. It supports multimodal input (text + image) and a 1M token context window. On benchmarks, Maverick sits between GPT-5.4 and Claude Sonnet 4: it matches GPT-5.4 on MMLU (91.8% vs 92.3%) but trails on SWE-bench (74.2% vs 78.3%). The real story is the price. Through providers like Groq and Together AI, Maverick API access costs $0.20-0.50/M input tokens -- 5-12x cheaper than GPT-5.4. For teams that need frontier-class performance on a budget, Maverick changes the math. This review covers architecture, benchmarks, API pricing across providers, and a direct comparison with Scout (which we covered previously). All data tracked by TokenMix.ai as of April 2026.

Table of Contents

• [Quick Llama 4 Maverick Specs Overview]
• [Architecture: 128 Experts, 17B Active -- What It Means]
• [Llama 4 Maverick Benchmark Deep Dive]
• [Maverick vs GPT-5.4 vs Claude: Head-to-Head]
• [Llama 4 Maverick API Pricing Across Providers]
• [Speed and Throughput: MoE Efficiency in Practice]
• [Multimodal Capabilities: Text + Image Input]
• [1M Context Window: How It Performs at Scale]
• [Maverick vs Scout: Which Llama 4 Model to Choose]
• [Cost Breakdown: Real-World Scenarios]
• [Full Comparison Table]
• [How to Choose: Decision Guide]
• [Conclusion]
• [FAQ]

Quick Llama 4 Maverick Specs Overview

Architecture: 128 Experts, 17B Active -- What It Means

Maverick's architecture is the most aggressive MoE design in any open-weight model. Understanding what "128 experts, 17B active" means is essential to understanding its performance and cost characteristics.

How MoE Works in Maverick

A Mixture-of-Experts model splits its feed-forward layers into multiple "expert" networks. For each token, a routing mechanism selects a small subset of experts to process that token. The other experts sit idle.

Maverick has 128 expert networks. On each forward pass, the router activates approximately 2 experts (plus shared attention layers), resulting in roughly 17B active parameters per token. The remaining 383B parameters are available but not used for that specific token.

Why This Matters for Users

Cost efficiency. You get the knowledge capacity of a 400B model at the inference cost of a 17B model. This is why Maverick can be served at prices comparable to much smaller models.

Speed. With only 17B active parameters per forward pass, Maverick processes tokens faster than a dense 400B model would. Providers like Groq achieve 300-500 tokens per second on Maverick -- comparable to models with 10-20x fewer total parameters.

Quality variability. The trade-off of MoE is inconsistency. Different experts specialize in different domains. If the router sends a token to a suboptimal expert, output quality can dip. This explains why Maverick's benchmark performance is strong on average but can be inconsistent on narrow, specialized tasks.

Self-hosting requirements. Despite only activating 17B parameters, you need enough memory to load all 400B parameters. Running Maverick locally requires significant GPU resources -- at least 8x A100 80GB or equivalent. This makes self-hosting impractical for most teams, pushing them toward API providers.

Llama 4 Maverick Benchmark Deep Dive

General Intelligence

Maverick's general intelligence scores are remarkable for an open-weight model. The 91.8% MMLU puts it within striking distance of GPT-5.4 (92.3%) and ahead of Claude Sonnet 4 (90.5%). For a model you can download and run on your own infrastructure, this is unprecedented.

Coding

On standard code generation (HumanEval, MBPP), Maverick is essentially tied with GPT-5.4 and beats Claude Sonnet 4. The gap shows on SWE-bench -- complex, multi-file software engineering tasks -- where GPT-5.4 leads by 4.1 points.

This SWE-bench gap is characteristic of MoE architectures. Multi-file engineering tasks require consistent access to the full breadth of programming knowledge. Expert routing sometimes fails to activate the optimal expert for edge-case code patterns, leading to less reliable performance on the hardest engineering tasks.

Math and Reasoning

Maverick's math performance is solid but not exceptional. It trails GPT-5.4 by 2-3 points and falls well behind dedicated reasoning models like o4-mini. For math-heavy workloads, dedicated reasoning models (o4-mini, DeepSeek R1) are better choices. Maverick's strength is general-purpose capability at low cost, not specialized reasoning.

Multimodal

Maverick supports image input alongside text, enabling tasks like image description, visual question answering, and document analysis with embedded images. TokenMix.ai testing shows Maverick's image understanding is competitive with GPT-4o-class models but trails GPT-5.4 and Gemini 3.1 Pro on complex visual reasoning tasks.

Maverick vs GPT-5.4 vs Claude: Head-to-Head

The performance story: Maverick delivers approximately 95% of GPT-5.4's benchmark performance across most tasks. It beats Claude Sonnet 4 on most benchmarks. The 4-point SWE-bench gap versus GPT-5.4 is the most significant quality difference.

The cost story: Maverick costs 5-12x less than GPT-5.4 and 6-15x less than Claude Sonnet 4. At these ratios, the 5% benchmark gap represents exceptional value.

The context story: Maverick's 1M context window is 4x GPT-5.4's 256K and 5x Claude's 200K. For document analysis, long-form content, and large codebases, Maverick's context advantage is significant.

Llama 4 Maverick API Pricing Across Providers

As an open-weight model, Maverick is available through multiple API providers at varying prices.

Cheapest option: Groq at $0.20/$0.60 per million tokens. Groq's custom LPU hardware is optimized for MoE architectures, making it the fastest and cheapest option for Maverick. Rate limits can be restrictive during peak hours.

Best value with reliability: TokenMix.ai at $0.25/$0.75 per million tokens. Below-list pricing with automatic failover across providers. If Groq is rate-limited, requests automatically route to the next cheapest available provider.

Cost comparison vs closed models:

At Groq prices, Maverick is approximately 15x cheaper than GPT-5.4 for the same token volume. Even through higher-priced providers, the savings are substantial.

Speed and Throughput: MoE Efficiency in Practice

Maverick's MoE architecture enables surprisingly fast inference given its 400B total parameter count.

For context: GPT-5.4 runs at approximately 80-120 TPS through OpenAI's API. Maverick on Groq is 3-5x faster in raw throughput. This speed advantage comes from the MoE architecture -- only 17B parameters are computed per token, regardless of the 400B total.

The practical benefit: Maverick can generate a 1,000-word response in 3-5 seconds on Groq, compared to 8-12 seconds for GPT-5.4. For user-facing applications, this speed difference is directly perceptible.

Multimodal Capabilities: Text + Image Input

Maverick natively accepts image inputs alongside text, enabling visual understanding tasks without separate vision models.

Supported capabilities:

• Image description and captioning
• Visual question answering
• Chart and graph interpretation
• Document analysis with embedded images
• Screenshot analysis and UI understanding

Quality assessment based on TokenMix.ai testing:

Maverick's multimodal capabilities are functional and useful but not best-in-class. For basic image understanding (description, simple QA), it performs well. For complex visual reasoning or high-accuracy OCR, GPT-5.4 or Gemini 3.1 Pro are stronger choices.

1M Context Window: How It Performs at Scale

Maverick's 1M token context window is the largest among frontier-class models. But context window size and context utilization quality are different things.

TokenMix.ai needle-in-a-haystack testing:

At 128K tokens (where most models max out), Maverick maintains 95% retrieval accuracy -- comparable to GPT-5.4 within its 256K window. Beyond 256K, accuracy degrades progressively. The 1M window is usable but not reliable for tasks requiring precise retrieval from the full context.

Practical recommendation: Use Maverick's full 1M context for tasks where approximate recall is acceptable (summarization, general Q&A over large documents). For tasks requiring precise detail retrieval, stay within 256K tokens for reliable results.

Maverick vs Scout: Which Llama 4 Model to Choose

Meta released two Llama 4 models. Here is how they compare.

The trade-off is clear: Maverick is smarter (7-8 points higher on MMLU, 5-6 points on HumanEval) but costs roughly 2x more than Scout and is slightly slower.

Cost Breakdown: Real-World Scenarios

Scenario 1: Document analysis pipeline (10,000 docs/day, avg 5,000 tokens input + 500 tokens output)

Scenario 2: Customer-facing chatbot (50,000 queries/day, avg 200 tokens in + 400 tokens out)

Scenario 3: Code generation (5,000 queries/day, avg 1,000 tokens in + 800 tokens out)

At scale, Maverick saves $4,000-6,000/month compared to GPT-5.4 for equivalent workloads. The savings fund themselves -- you could hire an additional engineer with the difference.

Full Comparison Table

How to Choose: Decision Guide

Conclusion

Llama 4 Maverick is the strongest open-weight model available. Its 91.8% MMLU and 91.5% HumanEval put it within 0.5-1 point of GPT-5.4, while costing 5-15x less through API providers. The 128-expert MoE architecture and 1M context window add capabilities that no closed-source model currently matches at this price point.

The trade-offs are real. GPT-5.4 still leads on the hardest engineering tasks (SWE-bench) by 4 points. Maverick's MoE architecture introduces occasional inconsistency. The 1M context window degrades beyond 256K tokens. And self-hosting requires serious hardware.

For most production workloads, Maverick through an API provider delivers the best performance-per-dollar of any model available today. Access it through TokenMix.ai for below-list pricing, automatic failover across providers, and unified cost tracking across all 155+ models.

The open-weight era is catching up to closed-source. Maverick closes 95% of the gap at 5-10% of the cost. For budget-conscious teams, that remaining 5% rarely justifies a 10-15x price premium.

FAQ

What is Llama 4 Maverick and who made it?

Llama 4 Maverick is Meta's largest open-weight language model, released in Q1 2026. It uses a Mixture-of-Experts architecture with 400B total parameters (17B active per token) across 128 experts. It supports text and image input with a 1M token context window.

How does Llama 4 Maverick compare to GPT-5.4?

Maverick scores within 0.5-2 points of GPT-5.4 on most benchmarks (91.8% vs 92.3% MMLU, 91.5% vs 92.0% HumanEval). GPT-5.4 leads by 4 points on SWE-bench (complex coding). Maverick costs 5-15x less and offers a 4x larger context window (1M vs 256K).

How much does Llama 4 Maverick API access cost?

Prices vary by provider. Groq offers the cheapest access at $0.20/$0.60 per million input/output tokens. TokenMix.ai offers $0.25/$0.75 with automatic failover. Together AI and Fireworks AI charge $0.35-0.50/ .00-1.50. All options are 5-15x cheaper than GPT-5.4.

What is the difference between Llama 4 Maverick and Llama 4 Scout?

Both use MoE architecture with 17B active parameters. Maverick has 128 experts (400B total) vs Scout's 16 experts (272B total). Maverick scores 7-8 points higher on MMLU and has a 1M context window vs Scout's 512K. Scout is cheaper ($0.11/$0.34 on Groq) and faster (594 TPS). Choose Maverick for quality, Scout for cost.

Can I run Llama 4 Maverick locally?

Technically yes -- the weights are open. Practically, you need at least 8x A100 80GB GPUs (or equivalent) to load the full 400B parameters. Most teams use API providers (Groq, Together AI, TokenMix.ai) instead of self-hosting, which is more cost-effective unless you have dedicated GPU infrastructure.

Is Llama 4 Maverick good for coding?

Yes. Maverick scores 91.5% on HumanEval (near GPT-5.4's 92.0%) and 89.5% on MBPP. It trails GPT-5.4 by 4 points on SWE-bench, meaning complex multi-file engineering tasks show a quality gap. For standard code generation, Maverick is excellent and far cheaper than closed alternatives.

Author: TokenMix Research Lab | Last Updated: April 2026 | Data Source: TokenMix.ai, Meta AI - Llama 4, ArtificialAnalysis.ai, LMSYS Chatbot Arena