Cost by workload size
Price comparison
| Feature |  Llama 3.1 405B |  GPT-4o |
|---|---|---|
| Provider | Meta / Llama | OpenAI |
| Input / M tokens | $3.50 | $2.50 |
| Output / M tokens | $3.50 | $10 |
| Cached input read | — | $1.25 |
| Context window | 128K | 128K |
| Batch discount | — | −50% |
| Vision | — | Yes |
| Audio | — | Yes |
| Reasoning tokens | — | — |
What your workload actually costs
Prices per token hide the real picture. Here are three common workloads computed with AITokenCalculator's own engine (medium reasoning effort, no batch, no cache):
| Workload | Llama 3.1 405B | GPT-4o | Cheaper |
|---|---|---|---|
| 1,000 in / 300 out | $0.00455 | $0.0055 | Llama 3.1 405B |
| 10,000 in / 2,000 out | $0.042 | $0.045 | Llama 3.1 405B |
| 100,000 in / 20,000 out | $0.42 | $0.45 | Llama 3.1 405B |
| Monthly @ 1,000 req/day (standard) | $1,278.38 | $1,369.69 | Llama 3.1 405B |
Analysis
On input price, GPT-4o wins at $2.50/M — roughly 1.4× cheaper than its rival. Output tells a sharper story: Llama 3.1 405B charges $3.50/M , about 2.9× less than GPT-4o — and output is where chatty and agentic workloads bleed money.
Context capacity differs too: Llama 3.1 405B fits 128K tokens (~96,000 words) versus 128K. If you feed whole documents or codebases, that gap decides feasibility before price even matters.
Open weights vs closed API. Hosted Llama 405B undercuts GPT-4o on price and removes vendor lock-in — you can even self-host. Expect a gap on multimodal polish and tool-calling reliability; strong pick for text-heavy, cost-sensitive pipelines.
Numbers shift as providers reprice — check the full AI model pricing table, then run your own prompt through the calculator preloaded with Llama 3.1 405B or with GPT-4o.