Models

Meta-Llama-3-70B-Instruct

hyperbolic · 131K ctx · 3 providers · updated 2026-08-05 00:15 UTC

Cheapest
$0.165 Official list · Hyperbolic
Avg price
$0.694 /1M
providers
3
(3 × $0.120 + $0.300) ÷ 4 = $0.165/1M — the $/1M figure blends input and output at 3:1 — that is the only way models with two different rates can be ranked on one axis. Your actual rate depends on your own input:output mix — input only $0.120, 3:1 $0.165, 1:1 $0.210, output only $0.300 per 1M.

The board shows this model's cheapest measured offer ($0.165/1M); the smaller figure under it is the mean of the 3 offers listed below ($0.694/1M). Add those up and divide — it matches.

ProviderRouter / Source$ in /1M$ out /1MTTFTTPSUptimeEvidence
hyperbolic Pricebook (LiteLLM) $0.120$0.300 Official list
azure_ai Pricebook (LiteLLM) $1.10$0.370 Official list
anyscale Pricebook (LiteLLM) $1.00$1.00 Official list

Price trend

Cheapest blended price, last 31 days (USD/1M): $0.165 → $0.165 → $0.165 → $0.165 → $0.165 → $0.165 → $0.165 → $0.165 → $0.165 → $0.165 → $0.165 → $0.165 → $0.165 → $0.165 → $0.165 → $0.165 → $0.165 → $0.165 → $0.165 → $0.165 → $0.165 → $0.165 → $0.165 → $0.165 → $0.165 → $0.165 → $0.165 → $0.165 → $0.165 → $0.165 → $0.165

Data & method

Cheapest ranked by blended = (3×in + out) ÷ 4; performance columns show only when a source provides them, never estimated. “Official list” = LiteLLM open pricebook (MIT); “API-measured” = provider/aggregator public APIs. Machine-readable data: /api/v1/models/meta-llama-3-70b-instruct.json. Free reuse requires attribution to tkx.org.

FAQ

How much does Meta-Llama-3-70B-Instruct cost?

Cheapest measured offer right now: $0.12/1M input, $0.3/1M output via hyperbolic — across 3 tracked providers, refreshed hourly.

Who serves Meta-Llama-3-70B-Instruct?

3 providers currently serve it. The table above lists each with input/output price, TTFT, TPS, uptime and an evidence tier.

Is the price of Meta-Llama-3-70B-Instruct going up or down?

The daily candles above track the cheapest blended price ($/1M, 3:1 in:out). TKX snapshots every hour, so drops and hikes show up the same day they happen.

Related analysis

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