Model comparison
DeepSeek-R1 vs GLM-5.2
GLM-5.2 is the stronger model overall, scoring 51.1 to 42.3 on the Noometry Index. DeepSeek-R1 costs 2.4× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.
Last verified . 29 shared benchmarks.
DeepSeekSummary
- They share 29 benchmarks with published results for both. DeepSeek-R1 scores higher in 1 category and GLM-5.2 in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5.2 leads 42.3 to 18.6.
- The biggest single-benchmark swing is ARC-AGI-1: 21.2% for DeepSeek-R1 and 77% for GLM-5.2.
- DeepSeek-R1 is cheaper at $0.50 / $2.15 per million input/output tokens, against $1.40 / $4.40 for GLM-5.2.
- GLM-5.2 accepts more context: 1M tokens versus 164K.
- GLM-5.2 has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-R1 | GLM-5.2 | |
|---|---|---|
| Provider | DeepSeek | Z.ai (Zhipu) |
| Noometry Index | 42.3 | 51.1 |
| Released | 2025-01-20 | 2026-06-13 |
| Weights | Proprietary | Open |
| Context window | 164K | 1M |
| Max output | 64K | 131K |
| Input $ / M tokens | $0.50 | $1.40 |
| Output $ / M tokens | $2.15 | $4.40 |
| Results tracked | 52 | 51 |
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Category by category
Coding GLM-5.2 leads
DeepSeek-R1: 46.3 (#68), GLM-5.2: 51.3 (#41)
| Benchmark | DeepSeek-R1 | GLM-5.2 |
|---|---|---|
| SciCode | 35.7% | 50.5% |
| WeirdML | 41.6% | 70.1% |
| LMArena Coding | 1427 | 1485 |
| ALE-Bench | 804.12 | 1,047 |
| SWE-bench Verified | — | 78.7% |
| DeepSWE | — | 43.8% |
| FrontierCode | — | 24.5% |
| Aider Polyglot | 71.4% | — |
| LMArena WebDev | — | 1603 |
| LiveBench Coding | 66.7% | — |
| AlgoTune | 1.7 | — |
Agentic & Tool Use GLM-5.2 leads
DeepSeek-R1: 30.7 (#75), GLM-5.2: 32.4 (#63)
| Benchmark | DeepSeek-R1 | GLM-5.2 |
|---|---|---|
| APEX-Agents | — | 45.2% |
| τ²-bench Banking | — | 37.1% |
| DeepResearch Bench | 35.1% | — |
| PostTrainBench | — | 31.7% |
| BALROG | 34.9% | — |
| GBAEval | — | 0% |
| METR Time Horizons | 53.8% | — |
| Vending-Bench 2 | — | 8,314 |
Reasoning GLM-5.2 leads
DeepSeek-R1: 18.6 (#278), GLM-5.2: 42.3 (#52)
| Benchmark | DeepSeek-R1 | GLM-5.2 |
|---|---|---|
| ARC-AGI-2 | 1.3% | 22.8% |
| SimpleBench | 40.8% | 58.8% |
| Kagi LLM Benchmark | 69.4% | 62.6% |
| ARC-AGI-1 | 21.2% | 77% |
| CritPt | 1.1% | 20.9% |
| LMArena Hard Prompts | 1416 | 1480 |
| Epoch Capabilities Index | 141.29 | 151.78 |
| NYT Connections (extended) | — | 74.3% |
| Chess Puzzles | — | 21% |
| EBR-Bench | — | 9.5% |
| LiveBench Reasoning | 83.2% | — |
| Mystery Game Puzzles | — | 19% |
| DTBench | — | 93.6% |
| LiveBench Data Analysis | 69.8% | — |
| LMCA | — | 45.8% |
| Surface Evolver Bench | — | 55.6% |
| ForecastBench | 60 | — |
| LiveBench | 71.6% | — |
Math GLM-5.2 leads
DeepSeek-R1: 43.8 (#79), GLM-5.2: 55.7 (#43)
| Benchmark | DeepSeek-R1 | GLM-5.2 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 66.4% | 86.4% |
| LMArena Math | 1400 | 1482 |
| FrontierMath (Tiers 1-3) | — | 59.2% |
| FrontierMath Tier 4 | — | 29.3% |
| MathArena Final-Answer Competitions | — | 67.6% |
| ProofBench | — | 35% |
| Omni-MATH | 42.4% | — |
| LiveBench Math | 80.7% | — |
| MATH Level 5 | 96.6% | — |
Knowledge GLM-5.2 leads
DeepSeek-R1: 44.5 (#87), GLM-5.2: 57.1 (#40)
| Benchmark | DeepSeek-R1 | GLM-5.2 |
|---|---|---|
| GPQA Diamond | 76.3% | 91.9% |
| LMArena Expert | 1394 | 1486 |
| SimpleQA Verified | — | 34.2% |
| MMLU-Pro | 79.3% | — |
| Confabulations | 12.7% | — |
| Vectara Hallucination Rate | 11.3% | — |
| GPQA (HELM) | 66.6% | — |
Multilingual GLM-5.2 leads
DeepSeek-R1: 52.4 (#85), GLM-5.2: 55.8 (#26)
| Benchmark | DeepSeek-R1 | GLM-5.2 |
|---|---|---|
| LMArena Non-English | 1412 | 1459 |
| LMArena Chinese | 1442 | 1519 |
| LMArena French | 1417 | 1479 |
| LMArena German | 1404 | 1468 |
| LMArena Japanese | 1391 | 1451 |
| LMArena Korean | 1360 | 1445 |
| LMArena Russian | 1423 | 1466 |
| LMArena Spanish | 1411 | 1477 |
Instruction Following GLM-5.2 leads
DeepSeek-R1: 72.0 (#143), GLM-5.2: 76.9 (#34)
| Benchmark | DeepSeek-R1 | GLM-5.2 |
|---|---|---|
| LMArena Instruction Following | 1382 | 1465 |
| LiveBench Instruction Following | 80.5% | — |
| IFEval | 78.4% | — |
Long Context Too close to call
DeepSeek-R1: 45.4 (#36), GLM-5.2: 45.3 (#43)
| Benchmark | DeepSeek-R1 | GLM-5.2 |
|---|---|---|
| LMArena Longer Query | 1391 | 1479 |
| Fiction.LiveBench | 75% | — |
Writing & Preference GLM-5.2 leads
DeepSeek-R1: 61.4 (#88), GLM-5.2: 70.4 (#21)
| Benchmark | DeepSeek-R1 | GLM-5.2 |
|---|---|---|
| LMArena Text | 1428 | 1470 |
| LMArena Creative Writing | 1405 | 1462 |
| EQ-Bench Creative Writing | 1500 | 1757 |
| LMArena Multi-Turn | 1405 | 1469 |
| Short-Story Creative Writing | 83% | — |
| WildBench | 82.8% | — |
| EQ-Bench 4 | — | 1222 |
| LiveBench Language | 48.5% | — |
Frequently asked questions
Is DeepSeek-R1 better than GLM-5.2?
GLM-5.2 is the stronger model overall, scoring 51.1 to 42.3 on the Noometry Index. DeepSeek-R1 costs 2.4× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-R1 or GLM-5.2?
DeepSeek-R1 is cheaper. It lists at $0.50 per million input tokens and $2.15 per million output tokens; GLM-5.2 lists at $1.40 and $4.40.
Is DeepSeek-R1 or GLM-5.2 better for coding?
GLM-5.2 scores higher on coding benchmarks: 51.3 versus 46.3 in the Noometry coding category.
Which has the bigger context window?
GLM-5.2 does, with 1M tokens against 164K.
How many benchmarks do DeepSeek-R1 and GLM-5.2 share?
29 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and GLM-5.2 has 51.