ALLaM-2-7b pricing, context window and limits
ALLaM-2-7b costs $0 per million input tokens and $0 per million output tokens at Groq list price. It accepts up to 4,096 tokens of context and returns up to 4,096 tokens.
- Model id
groq/allam-2-7b - Snapshot
What does ALLaM-2-7b cost?
| Input | $0 |
|---|---|
| Output | $0 |
| Cached input (read) | — |
| Cache write | — |
US dollars per million tokens. A dash means no price is published.
How much can ALLaM-2-7b read and write?
| Context window | 4,096 |
|---|---|
| Max output | 4,096 |
What can ALLaM-2-7b do?
| Vision (image input) | No |
|---|---|
| Tool calling | No |
| Structured output | No |
| Reasoning | No |
| Input | text |
| Output | text |
What does a request to ALLaM-2-7b cost?
| A typical request: 1,000 tokens in, 500 out | $0 |
|---|---|
| A long request: 10,000 tokens in, 1,000 out | $0 |
| 1,000 typical requests | $0 |
| 1 million tokens in and 1 million out | $0 |
Among 10 Groq models with a published price, ALLaM-2-7b is the cheapest by input price. Its context window is the 10th largest of 14. List prices only; discounts, batch prices and prices above 200K tokens are not shown.
How do I call ALLaM-2-7b through DVARA?
Configure the groq provider in the DVARA LLM Gateway and send the model id groq/allam-2-7b. DVARA, the AI governance platform, applies your policies, audit and cost attribution to the call. See LLM cost attribution and budgets.
In DVARA, if you haven't set your own price for a model, its calls are costed at the price in the catalogue that ships with your DVARA version. A price you set in Flightdeck always wins. You can switch catalogue prices off. Then a model with no price you set is counted as costing nothing. Every cost record says which price it used.
What should I check before relying on this data?
The weekly review marked this entry. A person saw each mark and kept the model listed.
- Input and output are both listed at $0, which is unusual for a hosted model.
Which other Groq models are there?
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- Qwen3.6 27B$0.6 in · $3 out · 131K context