OpenAI vs Meta Llama Pricing
Meta Llama is cheaper than OpenAI at the entry level: Llama 3.2 3B Instruct costs $0.020 per 1M output tokens against $0.140 for gpt-oss-20b — 7.0× the price. On current flagships, OpenAI's GPT-5.6 costs $30.00/1M output against $4.00/1M for Meta Llama's Llama 3.1 405B (base). Prices are USD, current as of August 11, 2026.
Per-million-token pricing for OpenAI and Meta Llama, with side-by-side flagship models, cheapest tiers, and context windows. Pricing data syncs weekly from a continuously-updated model catalog — last updated August 11, 2026.
Who wins on what
Cheapest input tokens
$0.02/1MMeta Llama
Llama 3.1 8B Instruct — $0.02/1M input
Cheapest output tokens
$0.02/1MMeta Llama
Llama 3.2 3B Instruct — $0.02/1M output
Longest context window
2.0MOpenAI
gpt-5.4 (>272K context length) — 2.0M input tokens
Lowest average output cost
$0.67/1MMeta Llama
Provider-wide average across 22 models
Largest model catalog
129 modelsOpenAI
More options to match cost vs capability
Most reasoning models
12 modelsOpenAI
Models with dedicated reasoning / thinking support
Most vision models
13 modelsOpenAI
Models that accept image input
Side-by-side
OpenAI
Full OpenAI pricing →Cheapest input
$0.030
gpt-oss-20b
Cheapest output
$0.140
gpt-oss-20b
Longest context
2.0M
gpt-5.4 (>272K context length)
Avg output / 1M
$29.47
Across catalog
Cheapest cached input
$0.020
GPT-5.6 Luna
| Model | In/1M | Out/1M | Ctx |
|---|---|---|---|
| GPT-5.6 VisionReasoningToolsCache | $5.00 | $30.00 | 1.1M |
| GPT-5.6 Sol VisionReasoningToolsCache | $5.00 | $30.00 | 1.1M |
| GPT-5.6 Terra VisionReasoningToolsCache | $2.00 | $12.00 | 1.1M |
| GPT-5.6 Luna VisionReasoningToolsCache | $0.200 | $1.20 | 1.1M |
| GPT-Realtime-2.1 VisionReasoningToolsCache | $4.00 | $24.00 | 128K |
| gpt-oss-20b | $0.030 | $0.140 | 131K |
Meta Llama
Full Meta Llama pricing →Cheapest input
$0.020
Llama 3.1 8B Instruct
Cheapest output
$0.020
Llama 3.2 3B Instruct
Longest context
1.0M
Llama 4 Maverick
Avg output / 1M
$0.674
Across catalog
| Model | In/1M | Out/1M | Ctx |
|---|---|---|---|
| Llama 3.1 405B (base) | $4.00 | $4.00 | 33K |
| Llama 3.1 405B Instruct | $3.50 | $3.50 | 131K |
| Llama 4 Maverick | $0.150 | $0.600 | 1.0M |
| Llama 3 70B Instruct | $0.300 | $0.400 | 8K |
| Llama 3.1 70B Instruct | $0.400 | $0.400 | 131K |
| Llama 3.2 3B Instruct | $0.020 | $0.020 | 131K |
All prices in USD per 1 million tokens. Showing top 6 models per provider, sorted by output cost.
Frequently asked questions
Is OpenAI or Meta Llama cheaper?
Meta Llama has the cheaper entry point at $0.02/1M output (Llama 3.2 3B Instruct — $0.02/1M output). Provider-wide, OpenAI averages $29.47/1M output against $0.674/1M for Meta Llama. Which is cheaper for you depends on which model tier your workload actually needs.
How much do OpenAI and Meta Llama cost per 1M tokens?
OpenAI starts at $0.140 per 1M output tokens (gpt-oss-20b), with its current flagship GPT-5.6 at $30.00. Meta Llama starts at $0.020 (Llama 3.2 3B Instruct), with Llama 3.1 405B (base) at $4.00. Input tokens cost less than output on both.
Which has the larger context window, OpenAI or Meta Llama?
OpenAI — gpt-5.4 (>272K context length) — 2.0M input tokens. For comparison, OpenAI's largest is 2.0M tokens (gpt-5.4 (>272K context length)) and Meta Llama's is 1.0M tokens (Llama 4 Maverick).
Which has more reasoning models, OpenAI or Meta Llama?
OpenAI lists 12 reasoning models and Meta Llama lists 0. Reasoning models bill their internal thinking as output tokens, so a reasoning call costs several times a standard completion of the same visible length — compare them on total tokens billed, not headline rate.
Should I switch from OpenAI to Meta Llama to save money?
Only if the cheaper model still meets your quality bar. Token price is one input; the ones that decide your bill are prompt size, response length, retries and how much conversation history you resend each turn. Model the switch against your real traffic before committing. Prices here are current as of August 11, 2026.
Related comparisons
Run the numbers for your workload
Calcaas multiplies per-token costs by your real usage patterns — inputs, outputs, retries, and conversation history — across both providers in one model.