Last refreshed 2026-05-11. Next refresh: weekly.
Why use Llama 3.2 1B Instruct on OpenRouter?
OpenRouter offers Llama 3.2 1B Instruct with pay-as-you-go pricing at $0.03/1M input tokens. OpenRouter is a multi-provider LLM aggregator offering unified API access to 300+ models from all major labs and emerging providers, with automatic failover for reliability.
Compare Llama 3.2 1B Instruct across 5 providers to find the best fit for your use caseSetup recipe
Docs fallbackUse the provider REST API or SDKCreate a provider API keymodel: meta-llama/llama-3.2-1b-instructmeta-llama/llama-3.2-1b-instructRequest example
meta-llama/llama-3.2-1b-instruct.Gotchas
- Use provider model ID "meta-llama/llama-3.2-1b-instruct", not the LLMReference slug "llama-3.2-1b-instruct".
Compare Llama 3.2 1B Instruct Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| OpenRouter | $0.03 | $0.20 |
| Fireworks AI | $0.10 | $0.10 |
| NVIDIA NIM | — | — |
| Bitdeer AI | $0.15 | $0.45 |
| AWS Bedrock | $0.10 | $0.10 |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.03 |
| Output tokens | $0.20 |
Capabilities
About Llama 3.2 1B Instruct
Llama 3.2 1B Instruct available on AWS Bedrock
FAQ
What does Llama 3.2 1B Instruct cost on OpenRouter?
On OpenRouter, Llama 3.2 1B Instruct costs $0.027 per 1M input tokens and $0.2 per 1M output tokens.
What is the context window for Llama 3.2 1B Instruct on OpenRouter?
Llama 3.2 1B Instruct supports a 60,000 token context window on OpenRouter.
How does OpenRouter compare to other Llama 3.2 1B Instruct providers?
Llama 3.2 1B Instruct is available from 5 providers. The cheapest input pricing is $0.027/1M tokens from OpenRouter.
What API model ID do I use for Llama 3.2 1B Instruct on OpenRouter?
Use the model ID meta-llama/llama-3.2-1b-instruct when calling OpenRouter's API.
Who created Llama 3.2 1B Instruct?
Llama 3.2 1B Instruct was created by AI at Meta as part of the Llama 3.2 model family.
Is Llama 3.2 1B Instruct open source?
Llama 3.2 1B Instruct is open source according to the seed data.