Groq pricing: API cost per model, user, and month

Groq lists 5 priced models in the ByteCosts index. This provider page has 10 source-backed model rows, but no source-backed 70% input / 30% output blended chat row to quote, so ByteCosts does not make a lowest-price claim here. For most AI apps the bill is driven by output tokens, retry rate, and prompt-cache hit rate far more than the headline input price, so Groq is cost-effective when your workload is input-heavy (RAG, classification) or can cache a large shared prefix. Compare Groq against alternatives on a real workload, seats, requests, and token mix, before committing to a model or a subscription, because a cheaper per-token price can still lose once power users and long outputs are priced in.

Groq pricing at a glance

• 10 source-backed model rows on this provider page; no blended chat minimum is quoted without comparable input/output data
• Last recorded price event: GPT OSS 20B 128k batch input on Aug 22, 2026
• 5 priced models in the index, updated August 22, 2026

Groq model prices

Per-million-token list prices for current Groq models, from the ByteCosts pricing index. Output tokens usually cost several times more than input.

Recent Groq price events

Source-backed price events recorded for Groq in the ByteCosts ledger, newest first. Every event below is a first-time listing, the ledger started tracking that rate; the model itself may be older.

When Groq is cost-effective

Token price alone does not decide cost. The variables that move an AI bill are: how many output tokens each call emits (output is the expensive side), how often calls retry, what fraction of the input prefix can be served from prompt cache, and how heavily your top 1% of users use the product.

Groq tends to win when your workload is input-heavy with short outputs, when you can cache a large shared system prompt or document context, or when a smaller Groq model clears your quality bar. It tends to lose when outputs are long and uncached, where a cheaper-per-output model compounds the saving across millions of calls.

Hidden costs to watch with Groq

List pricing rarely equals your invoice. Budget for these before you commit:

• Output-token blow-up: reasoning and verbose responses multiply the most expensive token class.
• Retries and tool loops: a single failed agent step can re-send the whole context.
• Cache misses: prompt caching only saves money above a break-even reuse count; cold prefixes pay the write premium.
• Power users: a small share of heavy users can dominate spend and erase a plan’s margin.
• Egress and gateway fees: LLM gateways and observability add a percentage on top of raw model cost.

Limitations before production billing decisions

Treat ByteCosts calculations as planning estimates, not final billing totals. Real invoices can differ because token mix, retry rate, cache hit rate, rate limits, taxes, gateway fees, regional pricing, and negotiated discounts change the effective cost.

Verify the provider source before production billing decisions, then compare the estimate with your own logs or invoice once production traffic is live.

Frequently asked questions

Groq pricing. ByteCosts. Updated August 22, 2026. https://bytecosts.com/pricing/groq/

Sources

• Groq official pricing
• Groq website

Machine-readable

• JSON reference
• CSV reference