DeepSeek R1 vs Llama 3.3 70B benchmark

Side-by-Side Benchmark Matrix

Recommended Workload Routing

Frequently Asked Questions

Which is better overall, DeepSeek R1 or Llama 3.3 70B?

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, DeepSeek R1 or Llama 3.3 70B?

DeepSeek R1 leads SWE-bench at 65.2% vs 45.1%. SWE-bench is one harness, not your repo.

Which is cheaper to run in production?

Llama 3.3 70B output tokens are $0.59/1M versus $2.19/1M. Input prices and retry rates still move the real bill.

Which is faster, DeepSeek R1 or Llama 3.3 70B?

Llama 3.3 70B has the lower time-to-first-token (160 ms vs 420 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

DeepSeek R1 accepts 128k tokens versus 128k.

Can I self-host either model?

Both DeepSeek R1 and Llama 3.3 70B are marked open-weights. Hosting cost is not in this table.

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 R1 and Llama 3.3 70B Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.