Granite 4.1 8B is a dense, decoder-only 8-billion-parameter language model from IBM, part of the Granite 4.1 family. It supports a 131K-token context window and is designed for enterprise tasks...
Model details →IBM: Granite 4.1 8B vs meta-llama/Llama-3.1-8B-Instruct
meta-llama/Llama-3.1-8B-Instruct wins on 1 of 6 axes — pricing and capability skew in its favour for most workloads.
Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 8B instruct-tuned version is fast and efficient. It has demonstrated strong performance compared to...
Model details →Side-by-side comparison
| Capability | IBM: Granite 4.1 8B | meta-llama/Llama-3.1-8B-Instruct | Winner |
|---|---|---|---|
| Context window Maximum number of input tokens the model can attend to in a single request. | 131K | 131K | Tie |
| Input price (per 1M) Cost per million input tokens billed by the provider. | $0.05 | $0.05 | Tie |
| Output price (per 1M) Cost per million output tokens billed by the provider. | $0.10 | $0.08 | 🏆 meta-llama/Llama-3.1-8B-Instruct |
| Tool / function calling First-class support for emitting structured tool calls. | Yes | Yes | Tie |
| Vision input Accepts image inputs alongside text. | — | — | Tie |
| Reasoning mode Internal chain-of-thought / extended-thinking support. | — | — | Tie |
Frequently asked questions
Is IBM: Granite 4.1 8B better than meta-llama/Llama-3.1-8B-Instruct?
meta-llama/Llama-3.1-8B-Instruct wins on 1 of 6 axes — pricing and capability skew in its favour for most workloads.
What's the price difference between IBM: Granite 4.1 8B and meta-llama/Llama-3.1-8B-Instruct?
Input: $0.05 vs $0.05 per 1M tokens. Output: $0.10 vs $0.08 per 1M tokens.
What context windows do IBM: Granite 4.1 8B and meta-llama/Llama-3.1-8B-Instruct support?
IBM: Granite 4.1 8B supports up to 131K tokens. meta-llama/Llama-3.1-8B-Instruct supports up to 131K tokens.
Do both IBM: Granite 4.1 8B and meta-llama/Llama-3.1-8B-Instruct support tool calling?
IBM: Granite 4.1 8B: yes. meta-llama/Llama-3.1-8B-Instruct: yes.
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