Llama 3.3 Cost Calculator
Estimate inference cost for Meta's Llama 3.3 70B. Open weights — prices depend on provider. This calculator uses ~$0.20 input / $0.60 output per million tokens, the rough average across Together AI, Groq, Fireworks, and similar hosted endpoints.
Llama 3.3 hosted pricing reference
Open weights — actual pricing varies by provider (Together, Groq, Fireworks, Replicate, DeepInfra). Self-hosting on your own GPUs has a different cost model entirely.
| Model | Input / 1M | Output / 1M | Context |
|---|---|---|---|
| Llama 3.3 Meta | $0.20 | $0.60 | 128K |
What you'll use this for
LLM costs scale fast. A quick estimate before you ship saves real money in production.
Budget planning
Project monthly and annual API spend before you ship. Catch surprises before billing does.
Comparing models
Pick the right model for your use case. Cheapest that passes your evals usually wins.
Cost optimization
Estimate caching impact, identify the cheapest viable model, find the breakeven for a smaller tier.
Pricing transparency
Show stakeholders concrete numbers for "what does AI cost us?" — no vendor pitch deck required.
How to estimate LLM cost
Paste a representative prompt
Use something real from your app, not a toy example. Token count scales with input length.
Set expected output tokens
How long is the model's reply? 500 tokens is a typical chat response; classification might be 5; agents can run thousands.
Set calls per day
Conservative is fine. Multiply by users × actions/user/day. The yearly figure is usually the eye-opener.
Read totals
Per call, per day, per month, per year. Toggle caching to see the discount impact.
Frequently asked questions
This calculator uses a rough heuristic of ~4 characters per token. Real tokenizers vary: code is denser, languages other than English are sparser, and each provider has its own tokenizer. Expect ±20% accuracy. For exact counts use the model-specific token counters.
From each provider's public pricing page as of 2026. Rates are subject to change; always verify on the provider site before relying on these numbers for procurement decisions.
Yes. No signup, no limits, no ads, no data leaves your browser.
Toggle "Input is cached" for an approximate 90% input-side discount. The exact discount varies by provider (Anthropic: 90%; OpenAI/Google: 50-75%). This calculator uses the Anthropic figure as a useful upper bound.
A few reasons. (1) Token counts on the provider may differ slightly from our ~4-char estimate. (2) System prompts, tool definitions, and retrieved context all count toward input. (3) Provider rates may have changed since this tool was last updated. (4) Some providers add surcharges for premium tiers, regional endpoints, or long-context overflow. Use this for ballpark estimates, not for final accounting.
About Llama 3.3 pricing
Llama 3.3 is open-weights — Meta releases the model and anyone can run it. That means there's no single "Llama price"; cost depends entirely on where you run it. Hosted providers like Together AI, Groq, Fireworks, Replicate, and DeepInfra all serve Llama 3.3 70B at slightly different rates, and self-hosting on your own GPUs has its own economics.
Typical hosted rates
For the 70B Instruct model, expect roughly $0.20 input / $0.60 output per 1M tokens at major providers. The 8B variant is often closer to $0.05 / $0.10. Groq tends to be cheaper on small models thanks to LPU inference speeds; Together has aggressive batched-completion pricing for high-volume customers.
Self-hosting
If you have spare GPU capacity, the marginal cost of Llama 3.3 is electricity plus depreciation — often well under $0.10 per 1M tokens at decent utilization. The challenge is keeping utilization high. Most teams find hosted inference cheaper until they're burning >$5k/month, at which point self-hosting starts to pay off.
When Llama makes sense
Privacy / data-residency constraints, fine-tuning needs, very high volume, latency-sensitive edge deployments, or cost-sensitive production workloads where Haiku/4o-mini quality isn't a meaningful step up.