Llama 4 Scout vs Maverick: Which Open-Source Model Should You Use?
Meta released two Llama 4 models: Scout ($0.11/$0.34, 10M context) and Maverick ($0.20/$0.60, 1M context). Both are open weights, both run on Together.ai — but they serve very different use cases. Here's how to pick the right one.
Quick Comparison
10M context window · 109B params (MoE)
1M context window · 400B params (MoE)
Maverick wins on raw quality
Full Model Specs
Both models use Mixture-of-Experts (MoE) architecture, which means they activate only a fraction of their parameters per request — keeping inference fast and cheap despite large total parameter counts.
| Spec | Llama 4 Scout | Llama 4 Maverick |
|---|---|---|
| Total parameters | 109B | 400B |
| Active parameters | ~17B | ~17B |
| Context window | 10M tokens | 1M tokens |
| Input price / 1M | $0.11 | $0.20 |
| Output price / 1M | $0.34 | $0.60 |
| Blended cost* | $0.18 | $0.30 |
| Provider | Together.ai | Together.ai |
| Open weights | Yes (Meta) | Yes (Meta) |
| Self-hostable | Yes | Yes |
| Multimodal | Text + Vision | Text + Vision |
*Blended cost assumes a 3:1 input-to-output ratio, typical for chat workloads.
Same active parameters, very different total capacity
Despite both models activating ~17B parameters per request (meaning similar per-request latency), Maverick's 400B total gives it access to 3.7x more knowledge. Think of it like a library: Scout has a smaller catalog but a massive reading room (10M context). Maverick has a much larger catalog but a standard reading room (1M context).
Cost Comparison: Head to Head
Scout is consistently cheaper across every workload type. The question is whether Maverick's quality advantage justifies the premium.
Cost Scenario 1: Chatbot (1M tokens/day, 60/40 split)
Production chatbot with 18M input + 12M output tokens per month:
| Model | Input/mo | Output/mo | Total/mo | Savings vs Maverick |
|---|---|---|---|---|
| Llama 4 Scout | $1.98 | $4.08 | $6.06 | 40% cheaper |
| Llama 4 Maverick | $3.60 | $7.20 | $10.80 | — |
Scout saves $4.74/month — that's $56.88/year for a basic chatbot. At this workload, Scout is the clear choice unless you need Maverick's superior quality for specific tasks.
Cost Scenario 2: Long-Context Document Processing (500 requests/day, 50K input + 2K output)
Legal contracts, research papers, codebases — 750M input + 30M output per month:
| Model | Input/mo | Output/mo | Total/mo | Savings vs Maverick |
|---|---|---|---|---|
| Llama 4 Scout | $82.50 | $10.20 | $92.70 | 45% cheaper |
| Llama 4 Maverick | $150.00 | $18.00 | $168.00 | — |
Scout saves $75.30/month at this scale. And Scout's 10M context means you can process massive documents in a single call — Maverick's 1M limit requires chunking at ~750K words.
Cost Scenario 3: High-Volume Classification (50K requests/day, 200 input + 50 output)
Sentiment analysis, content moderation, intent classification — 300M input + 75M output per month:
| Model | Input/mo | Output/mo | Total/mo | Savings vs Maverick |
|---|---|---|---|---|
| Llama 4 Scout | $33.00 | $25.50 | $58.50 | 45% cheaper |
| Llama 4 Maverick | $60.00 | $45.00 | $105.00 | — |
Scout saves $46.50/month at high volume. Classification tasks rarely need Maverick's extra capacity — Scout's quality is more than sufficient.
Quality Comparison: Where Each Model Excels
Llama 4 Scout: The efficient generalist
With 109B total parameters and 10M context, Scout is optimized for throughput and efficiency. It handles general-purpose tasks, multilingual content, and long-context workloads exceptionally well. The 10M context window is its killer feature — no other model at this price point comes close.
Llama 4 Maverick: The quality leader
With 400B total parameters, Maverick has 3.7x more knowledge capacity than Scout. This shows in complex reasoning, nuanced analysis, and tasks that benefit from deeper world knowledge. It's Meta's answer to GPT-5 and Claude 4 — at a fraction of the cost.
| Capability | Llama 4 Scout | Llama 4 Maverick |
|---|---|---|
| General reasoning | Very Good | Excellent |
| Code generation | Good | Excellent |
| Math & logic | Good | Excellent |
| Natural conversation | Excellent | Excellent |
| Instruction following | Excellent | Excellent |
| Multilingual support | Excellent | Excellent |
| Long context (1M+) | Excellent (10M) | Good (1M max) |
| Complex analysis | Good | Excellent |
| Creative writing | Good | Excellent |
| Structured output | Excellent | Excellent |
| Vision / image understanding | Good | Excellent |
| Cost efficiency | 45% cheaper | More expensive |
Self-Hosting: The Real Price Comparison
Both models are open weights — you can self-host them on your own GPU cluster. This changes the cost equation dramatically:
| Hosting | Llama 4 Scout | Llama 4 Maverick |
|---|---|---|
| GPU requirement | 2x H100 (80GB) | 8x H100 (80GB) |
| Estimated GPU cost/mo | ~$2,500 | ~$10,000 |
| Break-even vs API | ~27M tokens/mo | ~17M tokens/mo |
| Best for | High-volume, cost-sensitive | Quality-critical, privacy-sensitive |
Self-hosting math: When does it pay off?
Scout: If you're processing more than ~27M tokens/month via API, self-hosting starts saving money. At 100M tokens/month, self-hosting Scout saves ~$8,000/month vs the API.
Maverick: The break-even is lower (~17M tokens/month) because the API is more expensive. But you need 4x the GPUs. If you're already running an 8-GPU cluster for other workloads, adding Maverick is essentially free.
When to Choose Llama 4 Scout
- Long-context workloads: When you need to process documents, codebases, or conversations exceeding 1M tokens
- Cost-sensitive applications: 45% cheaper than Maverick across all workload types
- High-volume classification: Sentiment analysis, content moderation, tagging at scale
- General-purpose chat: Excellent conversational quality at the lowest price point
- Multilingual applications: Broader language support at lower cost
- Self-hosting on a budget: Runs on 2x H100 vs Maverick's 8x requirement
- RAG pipelines: 10M context fits massive retrieval sets, improving answer quality
When to Choose Llama 4 Maverick
- Complex reasoning: Multi-step analysis, research synthesis, strategic planning
- Code generation: More knowledge capacity means better code quality and fewer bugs
- Creative writing: More nuanced, varied, and engaging output
- Quality-critical tasks: When wrong answers are costly (legal, medical, financial)
- Mixed workloads: If your app handles both simple and complex tasks, Maverick handles the complex ones
- Vision tasks: Better image understanding for multimodal applications
- When you already have 8x H100s: Marginal cost of adding Maverick is near zero
The Multi-Model Strategy
Use both for maximum value
The smartest approach: route simple tasks to Scout, complex tasks to Maverick. A typical split:
- 80% of requests → Scout: Classification, simple Q&A, formatting, basic chat
- 20% of requests → Maverick: Complex reasoning, code generation, creative tasks
This hybrid approach gives you Maverick-level quality on hard tasks while keeping costs closer to Scout's budget tier. At a 80/20 split on 1M tokens/day, your blended cost is ~$7.50/month — still under $10.
The Bottom Line
Two models, one decision
Choose Llama 4 Scout if cost and context window matter most. At $0.11 input and 10M context, it's the cheapest way to process enormous amounts of text. It handles 80% of workloads brilliantly.
Choose Llama 4 Maverick if quality matters most. At $0.20 input, it's still 95% cheaper than GPT-5 ($1.25) or Claude 4 Opus ($5.00), but delivers flagship-level reasoning and code generation.
The best move? Use Scout as your default, route complex tasks to Maverick. Both are open weights, both run on Together.ai, and both cost under $0.30 blended. You get GPT-5-class quality for a fraction of the price.
Calculate your exact costs: Plug your real workload into our free calculator and see exactly what each model would cost — down to the penny.
Try the APIpulse Calculator