Codestral 25.01 vs DeepSeek R1 benchmark

Interactive Comparison Diagram

Recommended Workload Routing

Frequently Asked Questions

Which is better overall, Codestral 25.01 or DeepSeek R1?

DeepSeek R1 has the higher preference Elo in our latest snapshot (1,358). “Better” still depends on coding, price, and latency — see the table.

Which is better at coding, Codestral 25.01 or DeepSeek R1?

DeepSeek R1 leads SWE-bench at 65.2% vs 51.8%. SWE-bench is one harness, not your repo.

Which is cheaper to run in production?

Codestral 25.01 output tokens are $0.9/1M versus $2.5/1M. Input prices and retry rates still move the real bill.

Which is faster, Codestral 25.01 or DeepSeek R1?

Codestral 25.01 has the lower time-to-first-token (150 ms vs 420 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

Codestral 25.01 accepts 256k tokens versus 64k.

Can I self-host either model?

DeepSeek R1 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. Codestral 25.01 and DeepSeek R1 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.