Gemini 3.6 Pro vs Qwen2.5 Coder 32B Instruct benchmark

Side-by-Side Benchmark Matrix

Recommended Workload Routing

Frequently Asked Questions

Which is better overall, Gemini 3.6 Pro or Qwen2.5 Coder 32B Instruct?

Gemini 3.6 Pro has the higher preference Elo in our latest snapshot (1,570). “Better” still depends on coding, price, and latency — see the table.

Which is better at coding, Gemini 3.6 Pro or Qwen2.5 Coder 32B Instruct?

Gemini 3.6 Pro leads SWE-bench at 74.6% vs 65.2%. SWE-bench is one harness, not your repo.

Which is cheaper to run in production?

Qwen2.5 Coder 32B Instruct output tokens are $1/1M versus $5/1M. Input prices and retry rates still move the real bill.

Which is faster, Gemini 3.6 Pro or Qwen2.5 Coder 32B Instruct?

We do not have TTFT for both models.

Which has the larger context window?

Gemini 3.6 Pro accepts 2M tokens versus 33k.

Can I self-host either model?

Neither model is marked open-weights here. You are comparing hosted APIs.

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. Gemini 3.6 Pro and Qwen2.5 Coder 32B Instruct Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.