DeepSeek R1 vs GLM-5.2 benchmark

Side-by-Side Benchmark Matrix

Recommended Workload Routing

Frequently Asked Questions

Which is better overall, DeepSeek R1 or GLM-5.2?

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, DeepSeek R1 or GLM-5.2?

GLM-5.2 leads SWE-bench at 69.3% vs 65.2%. SWE-bench is one harness, not your repo.

Which is cheaper to run in production?

GLM-5.2 output tokens are $1.8/1M versus $2.19/1M. Input prices and retry rates still move the real bill.

Which is faster, DeepSeek R1 or GLM-5.2?

GLM-5.2 has the lower time-to-first-token (270 ms vs 420 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 128k.

Can I self-host either model?

Both DeepSeek R1 and GLM-5.2 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 GLM-5.2 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.