Model comparison
DeepSeek-R1 vs GLM-4.6
DeepSeek-R1 and GLM-4.6 score almost the same on the Noometry Index (42.3 vs 41.4), so choose on price, context window or the category you care about most.
Last verified . 23 shared benchmarks.
DeepSeekSummary
- They share 23 benchmarks with published results for both. DeepSeek-R1 scores higher in 5 categories and GLM-4.6 in 4 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in coding, where DeepSeek-R1 leads 46.3 to 40.1.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 69.4% for DeepSeek-R1 and 47.4% for GLM-4.6.
- DeepSeek-R1 is cheaper at $0.50 / $2.15 per million input/output tokens, against $0.60 / $2.20 for GLM-4.6.
- GLM-4.6 accepts more context: 205K tokens versus 164K.
- GLM-4.6 has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-R1 | GLM-4.6 | |
|---|---|---|
| Provider | DeepSeek | Z.ai (Zhipu) |
| Noometry Index | 42.3 | 41.4 |
| Released | 2025-01-20 | 2025-09-30 |
| Weights | Proprietary | Open |
| Context window | 164K | 205K |
| Max output | 64K | 131K |
| Input $ / M tokens | $0.50 | $0.60 |
| Output $ / M tokens | $2.15 | $2.20 |
| Results tracked | 52 | 29 |
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Category by category
Coding DeepSeek-R1 leads
DeepSeek-R1: 46.3 (#68), GLM-4.6: 40.1 (#148)
| Benchmark | DeepSeek-R1 | GLM-4.6 |
|---|---|---|
| SciCode | 35.7% | 38.4% |
| LMArena Coding | 1427 | 1449 |
| ALE-Bench | 804.12 | 340.82 |
| SWE-bench Verified (bash only) | — | 55.4% |
| Aider Polyglot | 71.4% | — |
| LMArena WebDev | — | 1340 |
| WeirdML | 41.6% | — |
| LiveBench Coding | 66.7% | — |
| AlgoTune | 1.7 | — |
Agentic & Tool Use GLM-4.6 leads
DeepSeek-R1: 30.7 (#75), GLM-4.6: 32.3 (#66)
| Benchmark | DeepSeek-R1 | GLM-4.6 |
|---|---|---|
| Terminal-Bench | — | 24.5% |
| Berkeley Function Calling Leaderboard | — | 72.4% |
| DeepResearch Bench | 35.1% | — |
| BALROG | 34.9% | — |
| METR Time Horizons | 53.8% | — |
Reasoning GLM-4.6 leads
DeepSeek-R1: 18.6 (#278), GLM-4.6: 23.7 (#172)
| Benchmark | DeepSeek-R1 | GLM-4.6 |
|---|---|---|
| Kagi LLM Benchmark | 69.4% | 47.4% |
| CritPt | 1.1% | 1.1% |
| LMArena Hard Prompts | 1416 | 1440 |
| ARC-AGI-2 | 1.3% | — |
| SimpleBench | 40.8% | — |
| ARC-AGI-1 | 21.2% | — |
| LiveBench Reasoning | 83.2% | — |
| LiveBench Data Analysis | 69.8% | — |
| Epoch Capabilities Index | 141.29 | — |
| ForecastBench | 60 | — |
| LiveBench | 71.6% | — |
Math DeepSeek-R1 leads
DeepSeek-R1: 43.8 (#79), GLM-4.6: 39.1 (#111)
| Benchmark | DeepSeek-R1 | GLM-4.6 |
|---|---|---|
| LMArena Math | 1400 | 1432 |
| OTIS Mock AIME 2024-2025 | 66.4% | — |
| Omni-MATH | 42.4% | — |
| LiveBench Math | 80.7% | — |
| MATH Level 5 | 96.6% | — |
| FrontierMath (Feb 2025 set) | — | 3.8% |
| FrontierMath Tier 4 (v1) | — | 2.1% |
Knowledge DeepSeek-R1 leads
DeepSeek-R1: 44.5 (#87), GLM-4.6: 40.2 (#124)
| Benchmark | DeepSeek-R1 | GLM-4.6 |
|---|---|---|
| Vectara Hallucination Rate | 11.3% | 9.5% |
| LMArena Expert | 1394 | 1431 |
| GPQA Diamond | 76.3% | — |
| MMLU-Pro | 79.3% | — |
| Confabulations | 12.7% | — |
| GPQA (HELM) | 66.6% | — |
Multilingual GLM-4.6 leads
DeepSeek-R1: 52.4 (#85), GLM-4.6: 53.5 (#66)
| Benchmark | DeepSeek-R1 | GLM-4.6 |
|---|---|---|
| LMArena Non-English | 1412 | 1426 |
| LMArena Chinese | 1442 | 1499 |
| LMArena French | 1417 | 1459 |
| LMArena German | 1404 | 1447 |
| LMArena Japanese | 1391 | 1393 |
| LMArena Korean | 1360 | 1400 |
| LMArena Russian | 1423 | 1419 |
| LMArena Spanish | 1411 | 1436 |
Instruction Following GLM-4.6 leads
DeepSeek-R1: 72.0 (#143), GLM-4.6: 74.3 (#98)
| Benchmark | DeepSeek-R1 | GLM-4.6 |
|---|---|---|
| LMArena Instruction Following | 1382 | 1410 |
| LiveBench Instruction Following | 80.5% | — |
| IFEval | 78.4% | — |
Long Context DeepSeek-R1 leads
DeepSeek-R1: 45.4 (#36), GLM-4.6: 43.4 (#94)
| Benchmark | DeepSeek-R1 | GLM-4.6 |
|---|---|---|
| LMArena Longer Query | 1391 | 1422 |
| Fiction.LiveBench | 75% | — |
Writing & Preference Too close to call
DeepSeek-R1: 61.4 (#88), GLM-4.6: 61.1 (#90)
| Benchmark | DeepSeek-R1 | GLM-4.6 |
|---|---|---|
| LMArena Text | 1428 | 1440 |
| LMArena Creative Writing | 1405 | 1411 |
| EQ-Bench Creative Writing | 1500 | 1411 |
| LMArena Multi-Turn | 1405 | 1427 |
| Short-Story Creative Writing | 83% | — |
| WildBench | 82.8% | — |
| LiveBench Language | 48.5% | — |
Frequently asked questions
Is DeepSeek-R1 better than GLM-4.6?
DeepSeek-R1 and GLM-4.6 score almost the same on the Noometry Index (42.3 vs 41.4), so choose on price, context window or the category you care about most.
Which is cheaper, DeepSeek-R1 or GLM-4.6?
DeepSeek-R1 is cheaper. It lists at $0.50 per million input tokens and $2.15 per million output tokens; GLM-4.6 lists at $0.60 and $2.20.
Is DeepSeek-R1 or GLM-4.6 better for coding?
DeepSeek-R1 scores higher on coding benchmarks: 46.3 versus 40.1 in the Noometry coding category.
Which has the bigger context window?
GLM-4.6 does, with 205K tokens against 164K.
How many benchmarks do DeepSeek-R1 and GLM-4.6 share?
23 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and GLM-4.6 has 29.