Meta Llama 4 Maverick: 400B MoE, 1M Context, $0.19/M Tokens — The Open-Weight Model That Changes the Value Equation
Meta's Llama 4 Maverick matches GPT-5.3 on major benchmarks at roughly 1/9th the cost — with a 1-million-token context window and free commercial use for most companies. Here's everything you need to know to decide if it belongs in your stack.
TL;DR
- 400B total params, 40B active per token (128-expert MoE architecture)
- 1M token context window; native multimodal (text + images)
- Trained on ~22 trillion tokens
- Matches GPT-5.3 on MMLU-Pro, GPQA, MATH within 1–2 points
- API cost: $0.19–$0.49/M tokens (vs ~$30 for GPT-5.4 Pro)
- Llama 4 Community License — free commercial use under 700M MAU
The Llama 4 Family: Maverick vs Scout
Meta released two primary Llama 4 models for production use. Understanding the distinction matters before choosing which to deploy:
| Spec | Llama 4 Maverick | Llama 4 Scout |
|---|---|---|
| Total parameters | 400B | 109B |
| Active params per token | 40B | 17B |
| MoE experts | 128 | 16 |
| Context window | 1M tokens | 10M tokens |
| Multimodal | Text + images | Text + images |
| Best use case | Complex reasoning, production workloads | Long-document processing, daily use |
| API pricing (est.) | $0.19–$0.49/M tokens | $0.08–$0.15/M tokens |
| Local VRAM (Q4) | ~22–24 GB | ~8–10 GB |
| License | Llama 4 Community License | Llama 4 Community License |
Benchmark Results: How Maverick Stacks Up
| Model | MMLU-Pro | GPQA Diamond | MATH | SWE-bench | $/M tokens |
|---|---|---|---|---|---|
| Claude Opus-4.6 | 84.2% | 82.1% | 91.4% | 65.8% | ~$22 |
| GPT-5.4 Pro | 82.9% | 80.8% | 90.2% | 61.5% | ~$30 |
| GPT-5.3 | 81.7% | 79.4% | 88.9% | 58.1% | ~$15 |
| Llama 4 Maverick | 80.3% | 78.1% | 87.6% | 52.4% | $0.19–0.49 |
| Gemini 3.1 Pro | 82.1% | 80.2% | 89.1% | 60.3% | ~$15 |
| DeepSeek V3.2 | 79.8% | 76.4% | 86.2% | 54.8% | ~$0.28 |
| Llama 4 Scout | 74.2% | 70.8% | 82.4% | 44.1% | $0.08–0.15 |
Maverick matches GPT-5.3 within 1–2 points on knowledge and reasoning while costing roughly 1/30th to 1/9th the price. Trails on coding/agentic tasks (SWE-bench).
Hardware Requirements: Running Maverick Locally
| Setup | Hardware | Quantization | Speed | Notes |
|---|---|---|---|---|
| Consumer GPU (min) | RTX 4090 (24GB) | Q4 | ~10 tok/s | Tight; context limited to 2–4K |
| Consumer GPU (rec) | RTX 5090 (32GB) | Q4 | 15–20 tok/s | Best single-GPU consumer option |
| Mac (best consumer) | Mac Studio M4 Max (128GB) | FP16 unquantized | ~8 tok/s | Only consumer option for full FP16 |
| Enterprise single GPU | A100 80GB | FP16 | ~25 tok/s | Production-grade; full precision |
| Enterprise multi-GPU | 2x A100 80GB | FP16 | ~50 tok/s | Recommended for production inference |
| Cloud API | Any (via Meta/providers) | N/A | Fast | $0.19–$0.49/M tokens, no HW cost |
Access Options: API vs Self-Hosted
| Provider | Price (input/output) | Best for |
|---|---|---|
| Meta AI API (direct) | $0.19/M in, $0.49/M out | Direct access, fastest new features |
| Groq | $0.20/M in, $0.60/M out | Highest throughput, sub-100ms latency |
| Together AI | $0.27/M in, $0.27/M out | Batch processing, fine-tuning support |
| Fireworks AI | $0.22/M in, $0.88/M out | Low latency production inference |
| Hugging Face Inference | Free (rate limited) / Pro | Prototyping, research |
| Self-hosted (vLLM) | GPU cost only | Data sovereignty, custom fine-tunes |
| HappyCapy | Included in plan | No-code agent workflows |
When to Use Maverick (vs Scout vs Proprietary Models)
Use Maverick when:
- You need near-frontier intelligence at 1/15th the cost of Opus or GPT-5.4 Pro
- Your context fits within 1M tokens (most use cases do)
- You need multimodal — images + text — in the same model
- You want commercial freedom with no MAU restrictions under 700M
- You need to self-host for data privacy (on-premise or private VPC)
Use Scout instead when:
- You need 10M+ token context for very long documents (entire codebases, legal archives)
- Cost efficiency matters more than peak intelligence
- You're processing large volumes of simpler tasks at scale
Stick with Claude Opus-4.6 or GPT-5.4 Pro when:
- Agentic coding tasks where SWE-bench performance gap (65% vs 52%) is critical
- Complex multi-step reasoning chains where the ~4-point MMLU gap matters
- You need RLHF-tuned safety behaviors and don't have resources to fine-tune Llama
Frequently Asked Questions
What is Llama 4 Maverick?
Meta's flagship open-weight model: 400B total params, 40B active per token, 128-expert MoE, 1M token context, native multimodal. Matches GPT-5.3 on benchmarks at ~1/30th the cost.
How does it compare to GPT-5.3 and Claude Opus?
Within 1–2 points of GPT-5.3 on MMLU-Pro, GPQA, MATH. Falls 13 points below Claude Opus-4.6 on SWE-bench (coding/agentic). Cost: $0.19–0.49/M vs $15–22/M for those models.
What GPU do I need for local inference?
RTX 5090 (32GB) for Q4 quantized at 15–20 tok/s. A100 80GB for FP16 production inference. Mac Studio M4 Max (128GB unified memory) for consumer FP16 unquantized.
What's the difference between Maverick and Scout?
Maverick (400B total, 40B active, 1M context): higher intelligence, complex reasoning. Scout (109B total, 17B active, 10M context): lighter, faster, cheaper, best for long documents.
Is Llama 4 Maverick free for commercial use?
Yes, under Llama 4 Community License for platforms with under 700M monthly active users. Weights are free to download from Meta and Hugging Face.
Deploy Llama 4 Maverick in Production — Without the DevOps
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