DeepSeek V4 Pro beats GPT-5.5 Pro on precision

A RuntimeWire benchmark released this week finds DeepSeek V4 Pro delivering higher precision than GPT‑5.5 Pro on a suite of standard LLM evaluation tasks. The two models were run side‑by‑side on zero‑shot reasoning, code generation and summarization benchmarks, and DeepSeek V4 Pro consistently posted a tighter match to the reference answers. In several categories the gap exceeded 10 percentage points, giving DeepSeek a clear edge where exactness matters.

What shipped

The test harness used the same prompts, temperature settings and token limits for both models, ensuring a fair comparison. DeepSeek V4 Pro’s architecture, which incorporates a larger context window and refined token‑level scoring, translated into fewer hallucinations and more accurate factual retrieval. GPT‑5.5 Pro, while competitive on fluency, lagged behind on tasks that penalize even minor errors.

Why it matters

Precision is a decisive factor for enterprises that embed LLMs in customer‑facing or compliance‑sensitive workflows. The benchmark provides a concrete data point for teams weighing model licensing costs against performance guarantees. With DeepSeek V4 Pro now demonstrably more reliable on exact‑answer tasks, vendors may see a shift toward models that prioritize accuracy over raw token throughput.

[RuntimeWire] reports that the results are reproducible across multiple runs, reinforcing the credibility of the findings. Engineers looking to upgrade their production stacks now have a clear, sourced comparison to inform their decisions.