Meta's Llama 4 Maverick is available through API providers like Together AI at $0.27/$0.85 per million tokens — making it one of the cheapest models with a 1 million token context window and a frequent contender in cheapest API rankings. That combination of low price and massive context doesn't exist anywhere else in the proprietary model world.
But "cheap" is relative. DeepSeek V3.2 undercuts Maverick on raw per-token pricing. GPT-5 mini offers a larger ecosystem. And self-hosting Llama 4 opens up even lower costs at scale. The right choice depends on your workload profile, volume, and whether you plan to stay on APIs or eventually run your own infrastructure.
This analysis covers every angle: API pricing comparisons, real workload costs, self-hosting economics, and the specific scenarios where Maverick wins — or loses — to the competition in the wider AI API pricing landscape.
[stat] $0.27/M Llama 4 Maverick's input price — among the lowest for any model with a 1M context window
Pricing via Together AI
Llama 4 Maverick is an open-weight model, so pricing varies by provider. Together AI offers competitive rates that we'll use as the baseline:
| Model | Input / 1M tokens | Output / 1M tokens | Context window | Max output |
|---|---|---|---|---|
| Llama 4 Maverick | $0.27 | $0.85 | 1,000,000 | 65,536 |
| Llama 3.1 8B | $0.18 | $0.18 | 128,000 | 32,768 |
| Llama 3.1 70B | $0.88 | $0.88 | 128,000 | 32,768 |
| Llama 3.1 405B | $3.50 | $3.50 | 128,000 | 32,768 |
Within Meta's own model family, Maverick hits a sweet spot. It's cheaper than the 70B and 405B predecessors while offering a dramatically larger context window. Only the tiny 8B model is cheaper, and it can't match Maverick's capabilities.
The output multiplier of 3.1× (output costs 3.1× more than input) is moderate — better than GPT-5 mini's 8× but higher than DeepSeek V3.2's 1.5×.
How Maverick compares to closed models
Here's where Maverick sits in the broader market. We'll use a medium workload — 2,000 input / 1,000 output tokens, 100K requests/month — to normalize the comparison:
| Model | Cost per request | Monthly cost (100K) | vs Maverick |
|---|---|---|---|
| Mistral Small 3.2 | $0.00030 | $30 | 57% cheaper |
| DeepSeek V3.2 | $0.00098 | $98 | 29% cheaper |
| Llama 4 Maverick | $0.00139 | $139 | baseline |
| GPT-5 mini | $0.00250 | $250 | 80% more |
| Mistral Large 3 | $0.00250 | $250 | 80% more |
| Gemini 3 Flash | $0.00400 | $400 | 188% more |
| GPT-5 | $0.01250 | $1,250 | 799% more |
| Claude Sonnet 4.6 | $0.02100 | $2,100 | 1,411% more |
💡 Key Takeaway: Maverick isn't the absolute cheapest model — Mistral Small 3.2 and DeepSeek V3.2 undercut it on pure per-token pricing. But neither of those models offers a 1M context window. Maverick's value proposition is the combination of low cost AND massive context.
Real-world cost scenarios
Scenario 1: High-volume chatbot (1,000 in / 400 out, 500K requests/month)
A large-scale customer service deployment:
| Model | Monthly cost |
|---|---|
| Mistral Small 3.2 | $66 |
| DeepSeek V3.2 | $224 |
| Llama 4 Maverick | $305 |
| GPT-5 mini | $525 |
| GPT-5 | $2,625 |
For simple chatbot workloads, Maverick isn't the cheapest. Mistral Small 3.2 wins by a huge margin. But Maverick's advantage shows when conversations get long and need the full context window.
Scenario 2: Document analysis (8,000 in / 2,000 out, 50K requests/month)
Processing reports, contracts, or research papers with long inputs:
| Model | Monthly cost |
|---|---|
| DeepSeek V3.2 | $154 |
| Llama 4 Maverick | $193 |
| Mistral Large 3 | $350 |
| GPT-5 mini | $300 |
| GPT-5 | $1,500 |
| Claude Sonnet 4.6 | $2,700 |
📊 Quick Math: For document analysis at 50K requests/month, Maverick costs $193 — just 25% more than the budget king DeepSeek V3.2, but with a 1M context window that's 8× larger. For documents exceeding 128K tokens, Maverick is the only budget option that works in a single request.
Scenario 3: Code review over large repos (20,000 in / 5,000 out, 10K requests/month)
This is where Maverick's 1M context window shines — processing entire files or module collections:
| Model | Monthly cost |
|---|---|
| DeepSeek V3.2 | $77 |
| Llama 4 Maverick | $97 |
| Mistral Large 3 | $175 |
| GPT-5 | $750 |
| Claude Sonnet 4.6 | $1,350 |
Maverick handles 20K-token inputs easily within its 1M window. Models with 128K context windows can also handle this, but Maverick leaves massive headroom for even larger reviews.
The open-source advantage: beyond API pricing
Because Llama 4 is open-weight, the API price is just one option. Open-source models unlock cost optimizations that proprietary models can't match:
1. Self-hosting eliminates per-token costs. At high volume, running Maverick on your own GPU infrastructure can drop costs to a fraction of API pricing. The break-even point depends on your hardware costs and utilization rate, but teams processing millions of requests per month often save 50-80% by self-hosting, as shown in our local vs cloud cost comparison.
2. Multiple providers create price competition. Together AI, Fireworks, Replicate, Anyscale, and others all host Llama models. You can shop for the cheapest provider or switch instantly if pricing changes. Try comparing providers on our calculator.
3. Fine-tuning for your domain. Customize Maverick for your specific use case without paying for fine-tuning API fees. A fine-tuned model that handles 90% of your queries eliminates the need for expensive flagship models on most requests.
4. No vendor lock-in. Your prompts, workflows, and integrations work with any Llama-compatible provider. If Together AI raises prices tomorrow, you migrate in hours, not weeks.
⚠️ Warning: Self-hosting sounds attractive but has hidden costs: GPU rental/purchase, infrastructure management, model updates, and monitoring. It only makes financial sense above approximately 1-2 million requests per month. Below that threshold, API providers are almost always cheaper when you factor in engineering time.
Self-hosting economics: when does it make sense?
Let's do the math on self-hosting Maverick versus using Together AI's API:
API cost at 2M requests/month (2,000 in / 1,000 out):
- Together AI: 2M × $0.00139 = $2,780/month
Self-hosting cost estimate (using cloud GPUs):
- A100 80GB instance: ~$2.00/hour on most cloud providers
- Maverick requires multi-GPU serving (estimated 2-4 A100s)
- 4 × A100 at $2.00/hr × 720 hours = $5,760/month for raw compute
- Plus engineering time, monitoring, and infrastructure overhead
At 2M requests/month, the API is still cheaper. The break-even point is roughly:
| Monthly requests | API cost | Self-host cost | Winner |
|---|---|---|---|
| 500K | $695 | $5,760+ | API |
| 2M | $2,780 | $5,760+ | API |
| 5M | $6,950 | $5,760+ | Self-host |
| 10M+ | $13,900 | $5,760+ | Self-host (clearly) |
📊 Quick Math: Self-hosting Llama 4 Maverick breaks even around 5 million requests/month compared to Together AI's API pricing. Below that, stick with the API.
When to choose Maverick
Maverick is the right choice when:
- You need a 1M context window on a budget. No other model at this price point offers 1M tokens of context. DeepSeek V3.2 maxes out at 128K, and Mistral models top out at 256K.
- You're processing long documents. Legal briefs, research papers, codebases, and transcripts that exceed 128K tokens need Maverick's context window.
- You plan to self-host eventually. Starting with the API and migrating to self-hosted infrastructure later gives you the best of both worlds — low startup cost with a clear path to lower marginal costs at scale.
- You want provider flexibility. Being open-weight means you're never locked into one vendor's pricing decisions.
When to choose an alternative
- Maximum quality for complex reasoning: GPT-5.2 ($1.75/$14.00) or Claude Opus 4.6 ($5.00/$25.00) outperform Maverick on the hardest tasks.
- Absolute lowest cost, context window doesn't matter: Mistral Small 3.2 at $0.06/$0.18 or DeepSeek V3.2 at $0.28/$0.42 are cheaper per token and often lead the best budget model lists.
- EU data residency requirements: Mistral offers EU-hosted APIs that simplify GDPR compliance.
- You need the OpenAI ecosystem: Function calling, Assistants API, fine-tuning tools, and broad SDK support are OpenAI advantages that Llama providers don't fully replicate.
✅ TL;DR: Llama 4 Maverick offers the best combination of budget pricing and massive context window available today. It's not the absolute cheapest model (Mistral Small and DeepSeek beat it on per-token cost), but nothing else gives you 1M context at $0.27/$0.85. For long-document workloads on a budget, Maverick is the clear winner.
Self-hosting economics: when does it pay off?
One of Maverick's biggest advantages is being open-weight — you can self-host it. But self-hosting only makes sense at certain scale thresholds.
The breakeven calculation
Running Maverick on cloud GPU infrastructure (e.g., AWS p5.48xlarge with 8× H100 GPUs) costs roughly $98/hour or $70,560/month for a dedicated instance. At Together AI's API pricing of $0.27/$0.85 per million tokens, you'd need to process approximately 83 million output tokens per month before self-hosting becomes cheaper.
📊 Quick Math: At 1,000 output tokens per request, that's 83,000 requests per day — the breakeven point where self-hosting starts saving money.
Below that volume, the API is cheaper and simpler. Above it, self-hosting can cut your per-token costs by 60-80%, especially as you optimize inference with techniques like quantization, batching, and speculative decoding.
The hidden costs of self-hosting
Don't forget the operational overhead: DevOps engineering time, GPU availability risks, model update management, and monitoring infrastructure. For most teams processing under 50K requests/day, the API route through Together AI or Fireworks is the pragmatic choice. Reserve self-hosting for when your AI costs exceed $5,000/month and you have engineering capacity to manage infrastructure.
Frequently asked questions
How much does Llama 4 Maverick cost per request?
For a typical request with 2,000 input and 1,000 output tokens, Maverick costs approximately $0.0014 via Together AI. That's about $139 per 100K requests/month. Pricing varies by provider — check Together AI, Fireworks, and Replicate for current rates, or use our calculator to compare.
Is Llama 4 Maverick cheaper than GPT-5?
Yes, significantly. Maverick costs $0.27/$0.85 per million tokens versus GPT-5's $1.25/$10.00. For a medium workload at 100K requests/month, Maverick costs $139 while GPT-5 costs $1,250 — an 89% savings. However, GPT-5 may deliver higher quality on complex reasoning tasks.
Can I self-host Llama 4 Maverick to save money?
Yes, but only at very high volume. Self-hosting on cloud GPUs costs approximately $5,760+/month for infrastructure. The break-even point versus Together AI's API pricing is around 5 million requests per month. Below that, the API is cheaper. Factor in engineering time for infrastructure management before deciding.
How does Llama 4 Maverick compare to DeepSeek V3.2?
DeepSeek V3.2 is 29% cheaper per request ($0.28/$0.42 vs $0.27/$0.85), mainly due to its lower output pricing. However, Maverick offers a 1M token context window versus DeepSeek's 128K — that's 8× more context. For workloads involving long documents or extensive code, Maverick's context advantage outweighs the per-token difference.
What's the best budget model with a large context window?
Llama 4 Maverick at $0.27/$0.85 with a 1M context window is the best budget option for long-context work. The only cheaper large-context option is Gemini 2.5 Flash at $0.15/$0.60 with a 1M window, but availability and rate limits differ. For 2M context, Grok 4.1 Fast offers $0.20/$0.50 but through xAI's API only. Compare all options on our calculator.