DeepSeek V4 Pro Base vs Granite Embedding English r2

This comparison does not currently contain a protocol-matched coding benchmark for both DeepSeek V4 Pro Base and Granite Embedding English r2, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Neither model has a directly sourced input price in this comparison. Neither model has a directly sourced output price in this comparison.

DeepSeek V4 Pro Base has the larger sourced context window. DeepSeek V4 Pro Base supports 1,049K and Granite Embedding English r2 supports 8K.

There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.

Both models have the same recorded self-hosting status: supported. DeepSeek V4 Pro Base is open weight; Granite Embedding English r2 is open weight.

DeepSeek V4 Pro Base is not documented with image input; Granite Embedding English r2 is not documented with image input. This reflects supported input modalities, not vision quality.

Neither has a larger sourced maximum output. DeepSeek V4 Pro Base is — and Granite Embedding English r2 is —.

DeepSeek V4 Pro Base: none of these features are definitively sourced. Granite Embedding English r2: none of these features are definitively sourced. Feature support does not establish relative quality.

DeepSeek V4 Pro Base has 0 sourced provider routes; Granite Embedding English r2 has 0, a tie.

There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.