Llama 3.3 70B vs Qwen: Qwen3 235B A22B Thinking 2507 benchmark

Side-by-Side Benchmark Matrix

Recommended Workload Routing

Frequently Asked Questions

Which is better overall, Llama 3.3 70B or Qwen: Qwen3 235B A22B Thinking 2507?

We do not have preference Elo for both models, so we do not declare an overall winner. Compare the metrics that exist.

Which is better at coding, Llama 3.3 70B or Qwen: Qwen3 235B A22B Thinking 2507?

SWE-bench is missing for at least one model, so we do not rank coding from a single number.

Which is cheaper to run in production?

Llama 3.3 70B output tokens are $0.32/1M versus $2.3/1M. Input prices and retry rates still move the real bill.

Which is faster, Llama 3.3 70B or Qwen: Qwen3 235B A22B Thinking 2507?

We do not have TTFT for both models.

Which has the larger context window?

Qwen: Qwen3 235B A22B Thinking 2507 accepts 262k tokens versus 131k.

Can I self-host either model?

Llama 3.3 70B 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. Llama 3.3 70B and Qwen: Qwen3 235B A22B Thinking 2507 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.