# Llama 4 (Scout / Maverick): pricing, crowd verdict, pros and cons (2026)

> Meta's open-weight MoE duo: 17B active params, native image input, 10M-token context on paper

Source: [GLAD-AI-TOR](https://glad-ia-tor.com) · Full page: https://glad-ia-tor.com/tool/llama-4
Arena: llm-models · Crowd score: 50% recommend (0 votes, Bayesian-smoothed) · Price: $0.60/1M out (Maverick, hosted)

Meta's first natively multimodal mixture-of-experts open-weight models, released April 5, 2025. Scout (109B total params, 16 experts) targets single-GPU deployment with a nominal 10M-token context; Maverick (400B total, 128 experts) is the flagship chat model with a 1M-token window.

## Key facts

- provider: Meta
- contextWindow: 10M tokens (Scout) / 1M (Maverick)
- priceIn: $0.15/1M in (Maverick, hosted)
- priceOut: $0.60/1M out (Maverick, hosted)
- modalities: text, vision (image input)
- openWeights: yes

## Ratings (editorial, 1-5)

- reasoning: 2.5/5
- coding: 2/5
- writing: 2.5/5
- speed: 4.5/5
- valueForMoney: 3.5/5

## Pros

- MoE efficiency: only 17B active parameters per token (Scout 109B/16 experts, Maverick 400B/128 experts), giving near GPT-4o-class chat at a fraction of the compute
- Very cheap hosted inference: Maverick from about $0.15/1M in and $0.60/1M out; Scout from about $0.10/$0.30 on DeepInfra or $0.11/$0.34 on Groq
- Native early-fusion multimodality (text plus images, tested up to 8 images) in an open-weight model
- Largest nominal context of any open-weight model at release: 10M tokens on Scout, 1M on Maverick
- Scout fits on a single H100 GPU with Int4 quantization; pretrained on 200 languages
- High throughput: 17B active params reach 500+ tokens/s on fast providers (Groq lists Scout at 594 TPS)

## Cons

- Benchmark trust damaged: Meta submitted an unreleased chat-optimized Maverick variant to LMArena (ELO 1417), which devs called misleading since public weights score lower
- Long-context claims collapse in practice: Scout scored ~15.6% at 128K on Fiction.LiveBench vs 90.6% for Gemini 2.5 Pro, and hosted providers cap Scout far below 10M (e.g. ~320K on DeepInfra)
- Coding widely panned on r/LocalLlama and HN, losing to the similarly-priced DeepSeek V3 on real dev tasks
- Not OSI open source: license excludes EU-domiciled companies, requires a special license above 700M MAU, and mandates Llama branding
- 109B/400B total params are too big for consumer GPUs, and the lineage stalled: no reasoning variant shipped and Behemoth was never released

## Best for

- high-volume cheap inference
- self-hosted multimodal apps
- multilingual chat at scale
- long-document summarization

## Verdict

Pick Llama 4 if you need a cheap, fast, self-hostable multimodal model for high-volume chat, extraction, or multilingual workloads outside the EU; Maverick lands near GPT-4o quality at roughly a tenth of the price. Avoid it for coding, hard reasoning, or genuine million-token retrieval, where DeepSeek, Qwen 3, and Gemini clearly outperform it. Versus Llama 3.3 70B it adds native vision and a longer window, but many developers found the older dense model or Qwen more reliable for pure text quality, and the 10M context is mostly a paper number.

## Pricing note

No first-party paid Meta API; typical third-party hosted rates shown for Maverick (Scout from ~$0.10/$0.30 per 1M on DeepInfra, $0.11/$0.34 on Groq). Hosted Scout context is capped well below the 10M nominal spec (e.g. ~320K on DeepInfra).

## Alternatives and head-to-heads

- [Claude Haiku 4.5](https://glad-ia-tor.com/tool/claude-haiku-4-5) (crowd score 67%) · [Llama 4 (Scout / Maverick) vs Claude Haiku 4.5](https://glad-ia-tor.com/vs/llama-4-vs-claude-haiku-4-5)
- [Claude Fable 5](https://glad-ia-tor.com/tool/claude-fable-5) (crowd score 63%) · [Llama 4 (Scout / Maverick) vs Claude Fable 5](https://glad-ia-tor.com/vs/llama-4-vs-claude-fable-5)
- [Claude Opus 4.7](https://glad-ia-tor.com/tool/claude-opus-4-7) (crowd score 57%) · [Llama 4 (Scout / Maverick) vs Claude Opus 4.7](https://glad-ia-tor.com/vs/llama-4-vs-claude-opus-4-7)
- [Claude Sonnet 5](https://glad-ia-tor.com/tool/claude-sonnet-5) (crowd score 57%) · [Llama 4 (Scout / Maverick) vs Claude Sonnet 5](https://glad-ia-tor.com/vs/llama-4-vs-claude-sonnet-5)

Full ranking: https://glad-ia-tor.com/hall-of-fame/llm-models

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