DeepSeek V4 Flash vs Llama 3.1 70B benchmark

Side-by-Side Benchmark Matrix

Recommended Workload Routing

Frequently Asked Questions

Which is better overall, DeepSeek V4 Flash or Llama 3.1 70B?

DeepSeek V4 Flash has the higher preference Elo in our latest snapshot (1,490). “Better” still depends on coding, price, and latency — see the table.

Which is better at coding, DeepSeek V4 Flash or Llama 3.1 70B?

DeepSeek V4 Flash leads SWE-bench at 71.2% vs 40.2%. SWE-bench is one harness, not your repo.

Which is cheaper to run in production?

DeepSeek V4 Flash output tokens are $0.28/1M versus $0.4/1M. Input prices and retry rates still move the real bill.

Which is faster, DeepSeek V4 Flash or Llama 3.1 70B?

DeepSeek V4 Flash has the lower time-to-first-token (165 ms vs 170 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

DeepSeek V4 Flash accepts 160k tokens versus 128k.

Can I self-host either model?

Both DeepSeek V4 Flash and Llama 3.1 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 V4 Flash and Llama 3.1 70B Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.