Pricing comparison

Anthropic vs Meta Llama Pricing

Meta Llama is cheaper than Anthropic at the entry level: Llama 3.2 3B Instruct costs $0.020 per 1M output tokens against $1.25 for Claude 3 Haiku — 62.5× the price. On current flagships, Anthropic's Claude Fable 5 costs $50.00/1M 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 Anthropic 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

$0.02/1M

Meta Llama

Llama 3.1 8B Instruct — $0.02/1M input

Cheapest output tokens

$0.02/1M

Meta Llama

Llama 3.2 3B Instruct — $0.02/1M output

Longest context window

1.0M

Meta Llama

Llama 4 Maverick — 1.0M input tokens

Lowest average output cost

$0.67/1M

Meta Llama

Provider-wide average across 22 models

Largest model catalog

36 models

Anthropic

More options to match cost vs capability

Most reasoning models

4 models

Anthropic

Models with dedicated reasoning / thinking support

Most vision models

4 models

Anthropic

Models that accept image input

Side-by-side

36 models

Anthropic

Full Anthropic pricing →

Cheapest input

$0.250

Claude 3 Haiku

Cheapest output

$1.25

Claude 3 Haiku

Longest context

1.0M

Claude 4 Sonnet (2025-05-14)

Avg output / 1M

$26.11

Across catalog

Cheapest cached input

$0.200

Claude Sonnet 5

ModelIn/1MOut/1MCtx
Claude Opus 5
VisionReasoningToolsCache
$5.00$25.001.0M
Claude Sonnet 5
VisionReasoningToolsCache
$2.00$10.001.0M
Claude Fable 5
VisionReasoningToolsCache
$10.00$50.001.0M
Claude Opus 4.8
VisionReasoningToolsCache
$5.00$25.001.0M
Claude Opus 4.7$5.00$25.00200K
Claude 3 Haiku$0.250$1.25200K
22 models

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

ModelIn/1MOut/1MCtx
Llama 3.1 405B (base)$4.00$4.0033K
Llama 3.1 405B Instruct$3.50$3.50131K
Llama 4 Maverick$0.150$0.6001.0M
Llama 3 70B Instruct$0.300$0.4008K
Llama 3.1 70B Instruct$0.400$0.400131K
Llama 3.2 3B Instruct$0.020$0.020131K

All prices in USD per 1 million tokens. Showing top 6 models per provider, sorted by output cost.

Frequently asked questions

Is Anthropic 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, Anthropic averages $26.11/1M 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 Anthropic and Meta Llama cost per 1M tokens?

Anthropic starts at $1.25 per 1M output tokens (Claude 3 Haiku), with its current flagship Claude Fable 5 at $50.00. 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, Anthropic or Meta Llama?

Meta Llama — Llama 4 Maverick — 1.0M input tokens. For comparison, Anthropic's largest is 1.0M tokens (Claude 4 Sonnet (2025-05-14)) and Meta Llama's is 1.0M tokens (Llama 4 Maverick).

Which has more reasoning models, Anthropic or Meta Llama?

Anthropic lists 4 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 Anthropic 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

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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.