DeepSeek V4 Pro Base vs Llama 3.1 70B

This comparison does not currently contain a protocol-matched coding benchmark for both DeepSeek V4 Pro Base and Llama 3.1 70B, 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 Llama 3.1 70B supports 131K.

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; Llama 3.1 70B is open weight.

DeepSeek V4 Pro Base is not documented with image input; Llama 3.1 70B 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 Llama 3.1 70B is —.

DeepSeek V4 Pro Base: none of these features are definitively sourced. Llama 3.1 70B: none of these features are definitively sourced. Feature support does not establish relative quality.

DeepSeek V4 Pro Base has 0 sourced provider routes; Llama 3.1 70B has 1, so Llama 3.1 70B has broader tracked availability.

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.