Meta’s Llama models are now serious API pricing contenders, not just open-model alternatives for teams that want optional self-hosting later. Via Together AI, the 2026 Llama lineup gives you two clear front-runners: Llama 4 Scout for extremely cheap long-context work, and Llama 4 Maverick for stronger general-purpose and vision workloads at budget-model pricing.
The most important number is Scout’s context window. Llama 4 Scout supports 10,000,000 tokens at $0.08 per 1M input tokens and $0.30 per 1M output tokens. That is the pricing profile you want when the workload is mostly reading: contracts, codebases, knowledge bases, transcripts, support histories, and retrieval-heavy agent loops.
This guide compares Scout, Llama 4 Maverick, and the older Llama API lineup against GPT-5, Claude, Gemini, DeepSeek, and Mistral. You’ll get real per-million-token pricing, long-context cost math, practical monthly estimates, and clear recommendations for when Meta’s open models beat proprietary APIs on total spend.
[stat] 10,000,000 tokens Llama 4 Scout’s context window is 10x larger than GPT-5’s 1,000,000 tokens and 50x larger than Claude Sonnet 4.5’s 200,000 tokens.
Meta Llama API pricing in 2026
The current Llama API lineup on AI Cost Check includes six Meta models served via Together AI. The two models that matter most for new builds are Llama 4 Scout and Llama 4 Maverick. The older Llama 3.x models still matter for compatibility, but their pricing is no longer the obvious bargain.
| Model | Input / 1M tokens | Output / 1M tokens | Context window | Best use case |
|---|---|---|---|---|
| Llama 4 Scout | $0.08 | $0.30 | 10,000,000 | Long-context search, RAG, document review |
| Llama 4 Maverick | $0.27 | $0.85 | 1,000,000 | Production assistants, vision, stronger responses |
| Llama 3.1 405B | $3.50 | $3.50 | 128,000 | Legacy high-capability Llama workloads |
| Llama 3.3 70B | $0.88 | $0.88 | 131,072 | Legacy 70B compatibility |
| Llama 3.1 70B | $0.88 | $0.88 | 128,000 | Existing 70B deployments |
| Llama 3.1 8B | $0.18 | $0.18 | 128,000 | Lightweight extraction and classification |
Scout is the cheapest Llama model on input tokens, even compared with Llama 3.1 8B. That matters because many production AI systems are input-heavy: they load retrieved documents, customer history, product records, tool outputs, and prior conversation state before generating a short answer.
Maverick costs more than Scout, but it is still priced like a budget model. At $0.27 input / $0.85 output per 1M tokens, Maverick sits close to DeepSeek V3.2 on input price while giving a much larger 1,000,000-token context window.
💡 Key Takeaway: Use Llama 4 Scout as the default for long-context and high-volume reads. Use Llama 4 Maverick when output quality, vision behavior, or customer-facing reliability matters more.
Llama 4 Scout: the long-context cost winner
Llama 4 Scout is built for workloads where the model needs to read a lot and respond briefly. Its $0.08 per 1M input tokens price is the main advantage. The $0.30 per 1M output tokens price is also low, but Scout’s biggest savings show up when your input context is huge.
A typical long-context analysis task might use:
- 500,000 input tokens
- 2,000 output tokens
Cost on Llama 4 Scout:
- Input: 0.5M × $0.08 = $0.040
- Output: 0.002M × $0.30 = $0.0006
- Total: $0.0406 per task
The same token profile on GPT-5 costs:
- Input: 0.5M × $1.25 = $0.625
- Output: 0.002M × $10 = $0.020
- Total: $0.645 per task
Scout is about 15.9x cheaper than GPT-5 for that task. Against Claude Opus 4.6, priced at $5 input / $25 output, the same task costs $2.55, making Scout about 62.8x cheaper.
Scout also beats Gemini 3 Pro on long-context price. Gemini 3 Pro has a strong 2,000,000-token context window, but costs $2 input / $12 output per 1M tokens. Scout has a larger context window and much lower input pricing, so it is the better choice for document-heavy systems where cost per scan matters.
Llama 4 Maverick: the production-quality budget option
Llama 4 Maverick is the practical upgrade when Scout is too cheap to be good enough for the specific task. Maverick costs $0.27 per 1M input tokens and $0.85 per 1M output tokens, with a 1,000,000-token context window.
For a moderate assistant request using 20,000 input tokens and 2,000 output tokens, Maverick costs:
- Input: 0.02M × $0.27 = $0.0054
- Output: 0.002M × $0.85 = $0.0017
- Total: $0.0071 per task
GPT-5 costs $0.045 for the same request. Claude Sonnet 4.5, at $3 input / $15 output, costs $0.09. That puts Maverick at roughly 6.3x cheaper than GPT-5 and 12.7x cheaper than Claude Sonnet 4.5 for this assistant-style workload.
Maverick is the right Llama model for customer-facing chat, visual Q&A, product assistants, content workflows, and internal copilots where answer quality matters. Scout should still be tested first for back-office extraction and summarization, but Maverick is the better public-facing default.
✅ TL;DR: Scout is the cost baseline. Maverick is the quality upgrade. Older Llama models are compatibility choices, not first-choice defaults for new API builds.
Llama pricing compared with GPT-5, Claude, Gemini, DeepSeek, and Mistral
Here is the direct pricing comparison against common alternatives.
| Model | Input / 1M | Output / 1M | Context | Pricing position |
|---|---|---|---|---|
| Llama 4 Scout | $0.08 | $0.30 | 10M | Cheapest long-context model here |
| Llama 4 Maverick | $0.27 | $0.85 | 1M | Strong open-model budget default |
| DeepSeek V3.2 | $0.28 | $0.42 | 128K | Cheap output-heavy alternative |
| Gemini 2.5 Flash | $0.30 | $2.50 | 1M | Good Google budget model |
| GPT-5 mini | $0.25 | $2.00 | 500K | Cheap OpenAI option |
| Mistral Large 3 | $0.50 | $1.50 | 256K | Strong Mistral model, higher input cost |
| GPT-5 | $1.25 | $10.00 | 1M | Premium general model |
| Gemini 3 Pro | $2.00 | $12.00 | 2M | Premium Google model |
| Claude Sonnet 4.5 | $3.00 | $15.00 | 200K | Premium coding and writing model |
| Claude Opus 4.6 | $5.00 | $25.00 | 1M | High-end reasoning model |
Scout wins when the workload has large input context. Maverick wins when you need stronger results without paying premium-model output prices. DeepSeek V3.2 remains a serious competitor because its output is only $0.42 per 1M tokens, cheaper than Maverick’s $0.85. For long generated answers, DeepSeek can beat Maverick. For long-context reads, Scout beats DeepSeek.
The clean recommendation is to use Scout for context-heavy steps, Maverick for quality-sensitive open-model steps, DeepSeek for cheap output-heavy generation, and GPT-5 or Claude only for premium fallback paths. You can model that routing strategy in AI Cost Check before committing to one provider.
Scenario 1: customer support assistant
Assume a support assistant handles 100,000 conversations per month. Each conversation uses:
- 6,000 input tokens
- 800 output tokens
Monthly usage:
- Input: 100,000 × 6,000 = 600M input tokens
- Output: 100,000 × 800 = 80M output tokens
| Model | Monthly input cost | Monthly output cost | Total |
|---|---|---|---|
| Llama 4 Scout | 600 × $0.08 = $48 | 80 × $0.30 = $24 | $72 |
| Llama 4 Maverick | 600 × $0.27 = $162 | 80 × $0.85 = $68 | $230 |
| DeepSeek V3.2 | 600 × $0.28 = $168 | 80 × $0.42 = $33.60 | $201.60 |
| GPT-5 mini | 600 × $0.25 = $150 | 80 × $2 = $160 | $310 |
| GPT-5 | 600 × $1.25 = $750 | 80 × $10 = $800 | $1,550 |
| Claude Sonnet 4.5 | 600 × $3 = $1,800 | 80 × $15 = $1,200 | $3,000 |
For support automation, Scout is the cheapest at $72/month. DeepSeek V3.2 is cheaper than Maverick in this scenario because output tokens are meaningful and DeepSeek output pricing is lower. Maverick still beats GPT-5 mini, GPT-5, and Claude Sonnet 4.5 by a wide margin.
Recommended routing: Scout for low-risk FAQ answers and internal summaries, Maverick for customer-facing responses that need better instruction following, and GPT-5 or Claude only for escalations.
📊 Quick Math: At 100,000 support conversations per month, Llama 4 Scout saves $1,478/month versus GPT-5 and $2,928/month versus Claude Sonnet 4.5.
Scenario 2: long-context document analysis
Now assume a document analysis product runs 10,000 reviews per month. Each review loads a large bundle of contracts, transcripts, or filings:
- 250,000 input tokens
- 3,000 output tokens
Monthly usage:
- Input: 2.5B input tokens
- Output: 30M output tokens
| Model | Monthly cost |
|---|---|
| Llama 4 Scout | 2,500 × $0.08 + 30 × $0.30 = $209 |
| Llama 4 Maverick | 2,500 × $0.27 + 30 × $0.85 = $700.50 |
| DeepSeek V3.2 | 2,500 × $0.28 + 30 × $0.42 = $712.60 |
| GPT-5 | 2,500 × $1.25 + 30 × $10 = $3,425 |
| Gemini 3 Pro | 2,500 × $2 + 30 × $12 = $5,360 |
| Claude Opus 4.6 | 2,500 × $5 + 30 × $25 = $13,250 |
This is Scout’s strongest use case. A workload that costs $13,250/month on Claude Opus 4.6 costs $209/month on Scout with the same token assumptions. The difference is $13,041/month, or $156,492/year.
Scout also removes engineering pressure from chunking. Its 10M-token context window means a product can load far more source material into a single call than GPT-5, Gemini 3 Pro, Claude Sonnet 4.5, or DeepSeek V3.2.
Scenario 3: vision and multimodal product features
Llama 4 Scout and Maverick are relevant for vision-style workloads, but they should not be used the same way. Scout is best for low-risk extraction, labels, metadata, and internal triage. Maverick is the stronger default for user-visible visual Q&A and richer multimodal responses.
Assume a product runs 500,000 multimodal requests per month. Each request uses:
- 2,000 input tokens
- 500 output tokens
Monthly usage:
- Input: 1B input tokens
- Output: 250M output tokens
| Model | Monthly cost |
|---|---|
| Llama 4 Scout | 1,000 × $0.08 + 250 × $0.30 = $155 |
| Llama 4 Maverick | 1,000 × $0.27 + 250 × $0.85 = $482.50 |
| GPT-5 mini | 1,000 × $0.25 + 250 × $2 = $750 |
| Gemini 2.5 Flash | 1,000 × $0.30 + 250 × $2.50 = $925 |
| GPT-5 | 1,000 × $1.25 + 250 × $10 = $3,750 |
For vision use cases, the best architecture is a two-step router. Scout handles cheap extraction and classification. Maverick handles final user-facing descriptions. GPT-5 or Gemini should be reserved for premium fallbacks, not every request.
⚠️ Warning: Do not send every image request to a premium model by default. In this scenario, GPT-5 costs 24.2x more than Scout and 7.8x more than Maverick.
Scenario 4: agent tool loops and research workflows
AI agents are expensive because they repeatedly read tool outputs, revise plans, and generate intermediate reasoning. Assume an internal research agent runs 50,000 tasks per month with:
- 30,000 input tokens
- 4,000 output tokens
Monthly usage:
- Input: 1.5B input tokens
- Output: 200M output tokens
| Model | Monthly cost |
|---|---|
| Llama 4 Scout | 1,500 × $0.08 + 200 × $0.30 = $180 |
| Llama 4 Maverick | 1,500 × $0.27 + 200 × $0.85 = $575 |
| DeepSeek V3.2 | 1,500 × $0.28 + 200 × $0.42 = $504 |
| GPT-5 mini | 1,500 × $0.25 + 200 × $2 = $775 |
| GPT-5 | 1,500 × $1.25 + 200 × $10 = $3,875 |
| Claude Opus 4.6 | 1,500 × $5 + 200 × $25 = $12,500 |
Scout is the cheapest agent model in this scenario, but the best production setup is model routing. Use Scout for context compression, search result digestion, and cheap planning. Use Maverick or DeepSeek for answer generation. Use GPT-5, Gemini 3 Pro, or Claude Opus only when the task requires premium reasoning.
For deeper premium-model comparisons, start with GPT-5 vs DeepSeek V3.2 and Claude Opus 4.6 vs DeepSeek V3.2.
Which Llama model should you use?
Use Llama 4 Scout for long-context workloads, support retrieval, document review, call transcript analysis, log analysis, internal search, and cheap agent steps. Its combination of $0.08 input pricing and 10M context is the main reason to choose Llama in 2026.
Use Llama 4 Maverick for production assistants, vision features, customer-facing chat, and workflows where Scout’s answer quality is not strong enough. Maverick’s $0.27/$0.85 pricing is still cheap enough for scale and much lower than GPT-5, Claude, and Gemini Pro output pricing.
Use Llama 3.1 8B only for simple legacy extraction or classification. At $0.18/$0.18, it no longer beats Scout on input price, but it can still work for narrow, stable tasks.
Use Llama 3.3 70B and Llama 3.1 70B mainly for compatibility. Both cost $0.88/$0.88, which makes Maverick a better default for most new builds.
Use Llama 3.1 405B only when that exact legacy model is required. At $3.50/$3.50, it is no longer the obvious open-model bargain.
Frequently asked questions
How much does Llama 4 Scout cost?
Llama 4 Scout costs $0.08 per 1M input tokens and $0.30 per 1M output tokens via Together AI. It has a 10,000,000-token context window, making it the best Llama option for long-context document analysis and retrieval-heavy workloads.
How much does Llama 4 Maverick cost?
Llama 4 Maverick costs $0.27 per 1M input tokens and $0.85 per 1M output tokens via Together AI. It has a 1,000,000-token context window and is the best Llama default for customer-facing assistants, multimodal features, and higher-quality open-model responses.
Is Llama cheaper than GPT-5?
Yes. Llama 4 Scout is much cheaper than GPT-5 at $0.08/$0.30 per 1M tokens versus GPT-5 at $1.25/$10 per 1M tokens. Llama 4 Maverick is also cheaper than GPT-5 at $0.27/$0.85, especially for support, agent, and multimodal workloads.
Is Llama cheaper than DeepSeek?
Llama 4 Scout is cheaper than DeepSeek V3.2 on input tokens: $0.08 vs $0.28 per 1M input tokens. DeepSeek V3.2 is cheaper than Llama 4 Maverick on output tokens at $0.42 vs $0.85, so use DeepSeek for output-heavy generation and Scout for long-context reads.
Which Llama model should I use in 2026?
Use Llama 4 Scout for long-context and high-volume workloads. Use Llama 4 Maverick for production assistants, vision features, and customer-facing answers. Keep older Llama 3.x models only for compatibility with existing workflows.
Calculate your own Llama API costs
The safest way to choose a model is to price your real token profile. A workload with 500,000 input tokens behaves very differently from one with 5,000 output tokens, and the cheapest model changes when the input-output ratio changes.
Use AI Cost Check to compare Llama 4 Scout, Llama 4 Maverick, GPT-5, Claude, Gemini, DeepSeek, and Mistral using your own monthly volume. Start with the Llama 4 Scout model page, compare it with Llama 4 Maverick, then benchmark against GPT-5 and DeepSeek V3.2.
The recommendation is simple: make Scout your long-context cost baseline, make Maverick your open-model quality upgrade, and only pay GPT-5, Claude, or Gemini prices when the task proves it needs premium-model quality.