DeepSeek V3 vs Gemini 2.5 Pro benchmark

Interactive Comparison Diagram

Recommended Workload Routing

Frequently Asked Questions

Which is better overall, DeepSeek V3 or Gemini 2.5 Pro?

Gemini 2.5 Pro has the higher preference Elo in our latest snapshot (1,350). “Better” still depends on coding, price, and latency — see the table.

Which is better at coding, DeepSeek V3 or Gemini 2.5 Pro?

Gemini 2.5 Pro leads SWE-bench at 63.8% vs 48.6%. SWE-bench is one harness, not your repo.

Which is cheaper to run in production?

DeepSeek V3 output tokens are $1.03/1M versus $10/1M. Input prices and retry rates still move the real bill.

Which is faster, DeepSeek V3 or Gemini 2.5 Pro?

Gemini 2.5 Pro has the lower time-to-first-token (210 ms vs 380 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

Gemini 2.5 Pro accepts 1M tokens versus 164k.

Can I self-host either model?

DeepSeek V3 is marked open-weights in our catalog. The other side is a closed API. Check the provider license before you ship weights.

Are these scores from CompareLLM’s own evals?

No. V1 aggregates public leaderboards (and optional first-party latency pings). Each cell names its source and date. Read /methodology.

What does preference Elo mean on this page?

Preference Elo is a crowd vote from LMArena / Arena. People see two hidden answers and pick the one they like more. The model that wins more often gets a higher Elo. That means people preferred it — not that it passed a school test. It is not SWE-bench, not accuracy, and not a number we invent. DeepSeek V3 and Gemini 2.5 Pro Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.