May 28th update to the [original DeepSeek R1](/deepseek/deepseek-r1) Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active in an inference pass.…
Model details →DeepSeek: R1 0528 vs IBM: Granite 4.0 Micro
DeepSeek: R1 0528 wins on 3 of 6 axes — pricing and capability skew in its favour for most workloads.
Granite-4.0-H-Micro is a 3B parameter from the Granite 4 family of models. These models are the latest in a series of models released by IBM. They are fine-tuned for long context tool calling.
Model details →Side-by-side comparison
| Capability | DeepSeek: R1 0528 | IBM: Granite 4.0 Micro | Winner |
|---|---|---|---|
| Context window Maximum number of input tokens the model can attend to in a single request. | 164K | 131K | 🏆 DeepSeek: R1 0528 |
| Input price (per 1M) Cost per million input tokens billed by the provider. | $0.45 | $0.02 | 🏆 IBM: Granite 4.0 Micro |
| Output price (per 1M) Cost per million output tokens billed by the provider. | $2.15 | $0.11 | 🏆 IBM: Granite 4.0 Micro |
| Tool / function calling First-class support for emitting structured tool calls. | Yes | — | 🏆 DeepSeek: R1 0528 |
| Vision input Accepts image inputs alongside text. | — | — | Tie |
| Reasoning mode Internal chain-of-thought / extended-thinking support. | Yes | — | 🏆 DeepSeek: R1 0528 |
Frequently asked questions
Is DeepSeek: R1 0528 better than IBM: Granite 4.0 Micro?
DeepSeek: R1 0528 wins on 3 of 6 axes — pricing and capability skew in its favour for most workloads.
What's the price difference between DeepSeek: R1 0528 and IBM: Granite 4.0 Micro?
Input: $0.45 vs $0.02 per 1M tokens. Output: $2.15 vs $0.11 per 1M tokens.
What context windows do DeepSeek: R1 0528 and IBM: Granite 4.0 Micro support?
DeepSeek: R1 0528 supports up to 164K tokens. IBM: Granite 4.0 Micro supports up to 131K tokens.
Do both DeepSeek: R1 0528 and IBM: Granite 4.0 Micro support tool calling?
DeepSeek: R1 0528: yes. IBM: Granite 4.0 Micro: not advertised.