GLM-5.2 vs Yi-Large benchmark

Side-by-Side Benchmark Matrix

Recommended Workload Routing

Frequently Asked Questions

Which is better overall, GLM-5.2 or Yi-Large?

GLM-5.2 has the higher preference Elo in our latest snapshot (1,492). “Better” still depends on coding, price, and latency — see the table.

Which is better at coding, GLM-5.2 or Yi-Large?

GLM-5.2 leads SWE-bench at 69.3% vs 53.8%. SWE-bench is one harness, not your repo.

Which is cheaper to run in production?

Yi-Large output tokens are $0.3/1M versus $1.8/1M. Input prices and retry rates still move the real bill.

Which is faster, GLM-5.2 or Yi-Large?

Yi-Large has the lower time-to-first-token (220 ms vs 270 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

GLM-5.2 accepts 200k tokens versus 33k.

Can I self-host either model?

GLM-5.2 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. GLM-5.2 and Yi-Large Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.