Llama 3.1 405B vs GPT-4o
| Spec | Llama 3.1 405B | GPT-4o |
|---|---|---|
| Provider | Meta | OpenAI |
| Input price (per 1M) | $3.50 | $2.50 |
| Output price (per 1M) | $3.50 | $10.00 |
| Context window | 128,000 | 128,000 |
| Tokenizer accuracy | exact (uses official tokenizer) | exact (uses official tokenizer) |
Cost per 1,000 calls across common workloads
| Workload | Llama 3.1 405B | GPT-4o | Winner |
|---|---|---|---|
| Short chat (200 in / 100 out) |
$1,050.00 | $1,500.00 | Llama 3.1 405B 30% cheaper |
| Medium chat (1,000 in / 500 out) |
$5,250.00 | $7,500.00 | Llama 3.1 405B 30% cheaper |
| Heavy generation (1,000 in / 2,000 out) |
$10,500.00 | $22,500.00 | Llama 3.1 405B 53% cheaper |
| Long context (8,000 in / 500 out) |
$29,750.00 | $25,000.00 | GPT-4o 16% cheaper |
| Code review (3,000 in / 600 out) |
$12,600.00 | $13,500.00 | Llama 3.1 405B 7% cheaper |
Costs are per 1,000 API calls. Multiply by 1,000 for per-million-calls.
Verdict
Llama 3.1 405B is the open-weight option that gets closest to GPT-4o's quality at roughly half the price on most hosted-API providers (Together AI, Fireworks, DeepInfra, Groq). It loses on ecosystem and tool-use reliability; it wins on cost and on the strategic benefits of using an open-weight model (no vendor lock-in, self-hostable, customizable).
Cost example
For a 1,000-token prompt with a 200-token reply, using Together AI pricing:
Llama 3.1 405B: 1000 × $3.50/M + 200 × $3.50/M = $0.00420 per call
GPT-4o: 1000 × $2.50/M + 200 × $10/M = $0.00450 per callRoughly tied at this prompt/output ratio. As output length grows, Llama becomes significantly cheaper because most providers charge the same rate for input and output, while OpenAI charges 4× more for output than input.
For a 1,000-token prompt with a 4,000-token reply:
Llama 3.1 405B: 1000 × $3.50/M + 4000 × $3.50/M = $0.01750 per call
GPT-4o: 1000 × $2.50/M + 4000 × $10/M = $0.04250 per callLlama 405B costs ~59% less on output-heavy workloads.
Context windows
- Llama 3.1 405B: 128,000 tokens (most providers)
- GPT-4o: 128,000 tokens
Equivalent. Both more than enough for typical work.
Quality differences
Where GPT-4o leads:
- Function calling and tool use. Llama's tool-use is improving but less reliable
- Native multimodal (vision input), Llama 3.1 is text-only; you'd need Llama 3.2 or a separate vision model
- Ecosystem (SDK maturity, framework support)
- Lower-latency cold starts on most hosted platforms
Where Llama 3.1 405B leads:
- Per-output-token cost (significant on long outputs)
- Open weights, you can self-host, fine-tune, run on your own GPUs
- No vendor lock-in for strategic AI deployments
- Latency on Groq's LPU hardware (Groq runs Llama at ~600 tokens/sec, much faster than GPT-4o)
On standard benchmarks (MMLU, HumanEval, MATH), Llama 405B is within 2-4 points of GPT-4o. On open-ended writing and instruction-following, GPT-4o still has a noticeable edge.
Hosting tradeoffs
The "right" Llama provider depends on what you optimize for:
| Provider | Price (input/output per M) | Best for |
|---|---|---|
| Together AI | $3.50 / $3.50 | Reliability, US data residency |
| Fireworks AI | $3.00 / $3.00 | Slightly cheaper, similar reliability |
| DeepInfra | $2.70 / $2.70 | Cheapest hosted option |
| Groq | $3.50 / $3.50 | Fastest inference (~600 tok/sec) |
| Self-hosted (H100/A100) | Hardware cost / no per-token fee | Highest volume, full control |
If you're sending >100M tokens/month, self-hosting becomes economically competitive even after hardware amortization. Below that, hosted APIs are simpler.
When to choose each
Use Llama 3.1 405B when:
- Output length per call is meaningful (4k+ tokens), cost advantage compounds
- You want vendor diversification or open-weight strategic posture
- You're using Groq for ultra-low-latency interactive applications
- Your workload is text-only and doesn't need vision
Use GPT-4o when:
- Tool use and function calling are central to your workflow
- You need native multimodal in one model
- Ecosystem maturity and SDK reliability matter more than the cost gap
- Your outputs are short and the cost gap is small
Count tokens on Llama 3.1 405B → · Count tokens on GPT-4o →