Llama 4 Maverick Review 2026: Meta's 400B Open-Weight MoE Model
Llama 4 Maverick Review 2026
TL;DR
- Llama 4 Maverick is Meta's 400B MoE open-weight model (17B active) — delivers frontier-level MMLU (91.8%) at API pricing as low as $0.20/M input on Groq, with 300-500 tok/s throughput.
- Strongest selling points: price-performance ratio (5-12× cheaper than GPT-5.4), native multimodal (text+image early fusion), and Scout variant's 10M-token context window.
- Restrictive Llama 4 Community License (not OSI-approved), SWE-bench coding gap (4+ pts behind GPT-5.4), and MoE quality inconsistency. Self-hosting requires 8× A100 80GB.
What Is Llama 4 Maverick?
Llama 4 is the open-weight model family that forced every other AI lab to ship a serious mixture-of-experts checkpoint. It's also the model family with the longest context window in existence (Scout at 10M tokens), the loudest licensing controversy, and the widest gap between benchmark hype and real-world performance.
Meta released Llama 4 in April 2025 with three variants — Scout (long context), Maverick (frontier performance), and Behemoth (preview only). By May 2026, Maverick is the one that matters for most developers.
The Llama 4 Family
| Variant | Total Params | Active Params | Experts | Context | Multimodal | Status |
|---|---|---|---|---|---|---|
| Scout | 109B | 17B | 16 | 10M tokens | Text + image | Released |
| Maverick | ~400B | 17B | 128 | 1M tokens | Text + image | Released |
| Behemoth | ~2T | 288B | 16 | — | Text + image | Preview only |
All variants were pretrained on roughly 22 trillion tokens of mixed text, image, and video data with native multimodality from day one — early fusion of text and vision tokens into a unified backbone, not a vision encoder bolted onto a frozen LLM.
📊 Quick Specs
Architecture
Mixture-of-Experts Design
The biggest architectural change from Llama 3 is the shift to MoE. Instead of running every parameter on every token, each token is routed to a small subset of expert sub-networks. Maverick activates 2 of 128 experts per token (~17B active params), while the remaining 383B sit idle. This means you get 400B knowledge capacity at roughly 17B inference cost.
The trade-off is quality variability. If the router sends a token to a suboptimal expert, output quality can dip. This explains why Maverick's benchmark scores are strong on average but inconsistent on narrow, specialized tasks.
iRoPE for Long Context
Llama 4 interleaves NoPE layers (no positional encoding, full causal attention over the entire context) every fourth layer with three RoPE layers using chunked attention, plus inference-time temperature scaling. Meta calls this iRoPE.
The result is Scout's 10M-token context window — the largest of any open-weight model — with strong needle-in-the-haystack retrieval across the full range. Maverick tops out at 1M tokens, which is still competitive with Gemini 3 Flash.
🔬 Detailed Analysis
Performance Benchmarks
| Benchmark | Score | Notes |
|---|---|---|
| MMLU | 91.8% | Within 0.5 pts of GPT-5.4 — remarkable for open-weight |
| MMLU-Pro | 77.1% | On par with Claude Sonnet 4 and DeepSeek V4 Pro |
| HumanEval | 91.5% | Within 0.3 pts of GPT-5.4 |
| SWE-bench Verified | 74.2% | 4.1 pts behind GPT-5.4 — real coding gap |
| MATH-500 | 85.3% | Mid-tier vs DeepSeek (87.2%) and Claude (86.7%) |
| GPQA Diamond | 65.8% | Trails GPT-5.4 (68.2%) and DeepSeek V4 Pro (66.1%) |
Key takeaways: Maverick matches GPT-5.4 within 0.5 points on MMLU and HumanEval — remarkable for an open-weight model. The real gap is on SWE-bench: 4.1 points behind GPT-5.4 and 2.3 behind DeepSeek V4 Pro on multi-file engineering tasks. Math is mid-tier compared to DeepSeek and Claude. Maverick excels on ARC-Challenge (97.0%) and HellaSwag (96.1%) — top among all open-weight models.
💰 Pricing & Cost Analysis
- ✅ Output: $0.80 per 1M tokens
- ✅ 300-500 tok/s inference speed
- ✅ Available on Groq, Together, DeepInfra, Replicate
- ✅ Self-hostable (requires 8x A100 80GB)
5-12× cheaper than GPT-5.4 ($2.50/M input). Self-hosting ~$20-30/hr in GPU rental.
| Provider | Input (1M) | Output (1M) | Speed |
|---|---|---|---|
| Groq | $0.20 | $0.80 | 300-500 tok/s |
| Together AI | $0.50 | $1.50 | 200-300 tok/s |
| DeepInfra | $0.30 | $1.00 | 250-400 tok/s |
| Replicate | $0.40 | $1.20 | 150-250 tok/s |
| Self-hosted (8x A100) | ~$2.50/hr GPU | — | Variable |
Despite activating only 17B parameters per token, you need enough VRAM to load all 400B parameters. Running Maverick locally requires at least 8x A100 80GB or equivalent — roughly $20-30/hour in GPU rental. Scout (109B total) is more self-hosting friendly, fitting on 4x A100 80GB.
🔓 The Licensing Situation
Llama 4 uses the Llama 4 Community License, not Apache 2.0 or MIT. Key restrictions:
- Commercial use allowed below 700M monthly active users
- EU multimodal restrictions — image input is restricted in EU deployments
- Acceptable Use Policy applies — no competitive benchmarking without permission
- Not OSI-approved open source
This is the most restrictive license Meta has shipped. If your project needs a clean Apache/MIT license for compliance, look at DeepSeek V4 or Qwen 3.5 instead.
✅ What It Does Best
Best Price-Performance Ratio
At $0.20/M input on Groq, Maverick delivers frontier-class MMLU scores for 5-12× less than GPT-5.4. For high-volume text generation on a budget, this is the best deal in AI.
1M Token Context (Maverick)
Full codebase analysis in one prompt. Combined with Groq's 300-500 tok/s speed, long-context processing feels fast and responsive.
10M Token Context (Scout)
The longest context window of any open-weight model. Unmatched for document retrieval, contract analysis, and codebase-wide search tasks.
Native Multimodal (Text + Image)
Early fusion architecture means vision is built in from day one, not added as an afterthought. Handles images at the same high quality as text.
Open Weights Are Downloadable
Full weights available for self-hosting, fine-tuning, and customization — unlike GPT or Gemini. Scout is particularly practical for custom deployments.
❌ Where It Falls Short
Restrictive License
Llama 4 Community License is not open source by OSI standards. EU multimodal restrictions and 700M MAU caps limit commercial use. MIT/Apache alternatives like DeepSeek V4 and Qwen 3.5 are cleaner.
SWE-Bench Coding Gap
4+ points behind GPT-5.4 and 2+ behind DeepSeek V4 Pro. For complex multi-file coding agents, Claude Code or GPT-5.5 with agent frameworks still perform better.
MoE Quality Inconsistency
Router-dependent output quality means Maverick is less predictable than dense models of equivalent capability. One query gets a brilliant answer, the next a mediocre one.
Self-Hosting Is Expensive
400B total parameters means you need 8x A100 80GB to run it — roughly $20-30/hour in GPU rental. Only Scout (109B, 4x A100) is practical for self-hosting.
🎯 Who Should Use Llama 4 Maverick?
| User Type | Verdict |
|---|---|
| Hobbyist developers (hosted APIs) | Great choice. Groq's cheap pricing makes Maverick accessible for personal projects. |
| High-volume text generation | Best in class. $0.20/M input + 300-500 tok/s is unmatched. |
| Document / contract analysis | Use Scout. 10M context window is the killer feature for retrieval-heavy workloads. |
| AI coding agents | Skip Maverick. Use Claude Code, GPT-5.5, or DeepSeek V4 Pro for multi-file engineering tasks. |
| EU-based deployments | Caution. Llama 4 Community License restricts multimodal in EU. Consider Qwen 3.5 or DeepSeek V4. |
| Fine-tuning / custom models | Good option. Open weights + strong base for fine-tuning. Scout is particularly practical for custom deployments. |
📋 Score Breakdown
Verdict
Llama 4 Maverick is the best value proposition in open-weight AI for text generation and vision understanding at scale. The combination of 91.8% MMLU, $0.20/M input pricing, and 300-500 tok/s throughput through Groq creates a price-performance ratio that no other open-weight model matches.
It is not the best model for coding agents or complex reasoning tasks. The SWE-bench gap to GPT-5.4 and DeepSeek V4 Pro is real, and the MoE router inconsistency can be frustrating for precision work.
And it is not truly open source. The Llama 4 Community License is Meta's most restrictive yet. If licensing matters to your project, DeepSeek V4 (MIT) or Qwen 3.5 (Apache 2.0) are cleaner alternatives.
ToolBrain Verdict: Buy / Deploy (for text generation, skip for coding agents).
❓ FAQ
How does Llama 4 compare to DeepSeek V4?
Maverick matches DeepSeek V4 Pro on general knowledge benchmarks but trails on coding (SWE-bench 74.2% vs 76.5%). DeepSeek V4 uses MIT license (more permissive), offers cheaper API pricing ($0.10/M input), and has stronger math reasoning. Llama 4 wins on multimodal nativity and Groq's inference speed.
Can I use Llama 4 commercially?
Yes, if your product has fewer than 700 million monthly active users and you comply with the Acceptable Use Policy. EU deployments face additional restrictions on multimodal features. Review the full license terms before building a product on it.
Can I run Llama 4 on my own hardware?
Scout (109B) runs on 4x A100 80GB. Maverick (400B) needs 8x A100 80GB or equivalent. Scout is practical for self-hosting; Maverick is better accessed through API providers unless you have serious GPU infrastructure.
Should I use Scout or Maverick?
Choose Maverick for general-purpose text generation and vision understanding. Choose Scout if your primary need is long-context retrieval (contract analysis, codebase search, document QA). Scout's 10M context window is the best in open-weight AI.
What's the best API provider for Llama 4?
Groq offers the fastest speeds (300-500 tok/s) and lowest prices ($0.20/M input). Together AI and DeepInfra are solid alternatives with more model variants. For EU users, check region availability before choosing a provider.
📖 Related Reads
🔗 DeepSeek V4 Flash Review — 9.1/10 — Best value LLM
🔗 Gemini 3 Flash Review — 8.0/10 — Google's speed champion
🔗 v0 by Vercel Review — 8.0/10 — AI UI generation
🔗 Bolt.new Review — 8.3/10 — AI full-stack coding
| Review | Summary |
|---|
📚 Citations
- Llama Official Website — Model info, variants, and documentation
- Meta Llama GitHub Repository — Model weights and source code
- Llama Documentation — API reference and integration guide
- Groq Cloud Console — Fastest inference provider for Llama 4
- Artificial Analysis — Intelligence Index and model benchmarks
📝 Change Log
- May 28, 2026 — v4 template upgrade: Added TL;DR, Quick Specs (tb-quick-specs), structured strengths/weaknesses (tb-strengths), benchmark table (tb-benchmarks with MMLU highlight), Pricing card (tb-pricing-recommended with Groq hero), 6-dimension Score Breakdown with emoji, Related Reads, Citations, and Change Log. Converted FAQ to collapsible. Wrapped Verdict in tb-verdict. Fixed broken Pros/Cons HTML.
- Original — Initial published review with family breakdown, benchmarks, pricing analysis, and licensing deep-dive.
