Model comparison
DeepSeek-R1 vs GLM-5.3-Flash
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 42.3 on the Noometry Index.
Last verified . 25 shared benchmarks.
DeepSeekSummary
- They share 25 benchmarks with published results for both. DeepSeek-R1 scores higher in 1 category and GLM-5.3-Flash in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5.3-Flash leads 48.0 to 18.6.
- The biggest single-benchmark swing is ARC-AGI-1: 21.2% for DeepSeek-R1 and 91% for GLM-5.3-Flash.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $0.50 / $2.15 for DeepSeek-R1.
- GLM-5.3-Flash accepts more context: 1M tokens versus 164K.
- GLM-5.3-Flash has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-R1 | GLM-5.3-Flash | |
|---|---|---|
| Provider | DeepSeek | Z.ai (Zhipu) |
| Noometry Index | 42.3 | 51.8 |
| Released | 2025-01-20 | 2026-08-20 |
| Weights | Proprietary | Open |
| Context window | 164K | 1M |
| Max output | 64K | 131K |
| Input $ / M tokens | $0.50 | $0.15 |
| Output $ / M tokens | $2.15 | $0.50 |
| Results tracked | 52 | 40 |
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Category by category
Coding GLM-5.3-Flash leads
DeepSeek-R1: 46.3 (#68), GLM-5.3-Flash: 53.1 (#31)
| Benchmark | DeepSeek-R1 | GLM-5.3-Flash |
|---|---|---|
| SciCode | 35.7% | 51.6% |
| LMArena Coding | 1427 | 1508 |
| ALE-Bench | 804.12 | 303.55 |
| DeepSWE | — | 63.4% |
| FrontierCode | — | 31.8% |
| Aider Polyglot | 71.4% | — |
| CursorBench | — | 36.8% |
| LMArena WebDev | — | 1609 |
| FrontierSWE | — | 18.1% |
| WeirdML | 41.6% | — |
| LiveBench Coding | 66.7% | — |
| AlgoTune | 1.7 | — |
Agentic & Tool Use GLM-5.3-Flash leads
DeepSeek-R1: 30.7 (#75), GLM-5.3-Flash: 34.2 (#47)
| Benchmark | DeepSeek-R1 | GLM-5.3-Flash |
|---|---|---|
| APEX-Agents | — | 52.8% |
| DeepResearch Bench | 35.1% | — |
| BALROG | 34.9% | — |
| GDP.pdf | — | 14% |
| METR Time Horizons | 53.8% | — |
Reasoning GLM-5.3-Flash leads
DeepSeek-R1: 18.6 (#278), GLM-5.3-Flash: 48.0 (#42)
| Benchmark | DeepSeek-R1 | GLM-5.3-Flash |
|---|---|---|
| ARC-AGI-2 | 1.3% | 65.8% |
| ARC-AGI-1 | 21.2% | 91% |
| CritPt | 1.1% | 15.4% |
| LMArena Hard Prompts | 1416 | 1491 |
| Epoch Capabilities Index | 141.29 | 151.88 |
| SimpleBench | 40.8% | — |
| Kagi LLM Benchmark | 69.4% | — |
| Chess Puzzles | — | 14% |
| LiveBench Reasoning | 83.2% | — |
| Mystery Game Puzzles | — | 8% |
| LiveBench Data Analysis | 69.8% | — |
| Surface Evolver Bench | — | 52.5% |
| Bench to the Future 3 | — | 0.15 |
| ForecastBench | 60 | — |
| LiveBench | 71.6% | — |
Math GLM-5.3-Flash leads
DeepSeek-R1: 43.8 (#79), GLM-5.3-Flash: 53.3 (#47)
| Benchmark | DeepSeek-R1 | GLM-5.3-Flash |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 66.4% | 93.9% |
| LMArena Math | 1400 | 1500 |
| FrontierMath (Tiers 1-3) | — | 55.8% |
| FrontierMath Tier 4 | — | 17.1% |
| ProofBench | — | 21% |
| Omni-MATH | 42.4% | — |
| LiveBench Math | 80.7% | — |
| MATH Level 5 | 96.6% | — |
Knowledge GLM-5.3-Flash leads
DeepSeek-R1: 44.5 (#87), GLM-5.3-Flash: 58.4 (#36)
| Benchmark | DeepSeek-R1 | GLM-5.3-Flash |
|---|---|---|
| GPQA Diamond | 76.3% | 90.2% |
| LMArena Expert | 1394 | 1513 |
| MMLU-Pro | 79.3% | — |
| Confabulations | 12.7% | — |
| Vectara Hallucination Rate | 11.3% | — |
| GPQA (HELM) | 66.6% | — |
Multimodal Not comparable
DeepSeek-R1: —, GLM-5.3-Flash: 42.8 (#27)
| Benchmark | DeepSeek-R1 | GLM-5.3-Flash |
|---|---|---|
| LMArena Vision | — | 1296 |
Multilingual GLM-5.3-Flash leads
DeepSeek-R1: 52.4 (#85), GLM-5.3-Flash: 56.0 (#25)
| Benchmark | DeepSeek-R1 | GLM-5.3-Flash |
|---|---|---|
| LMArena Non-English | 1412 | 1462 |
| LMArena Chinese | 1442 | 1527 |
| LMArena French | 1417 | 1496 |
| LMArena German | 1404 | 1470 |
| LMArena Japanese | 1391 | 1429 |
| LMArena Korean | 1360 | 1446 |
| LMArena Russian | 1423 | 1469 |
| LMArena Spanish | 1411 | 1471 |
Instruction Following GLM-5.3-Flash leads
DeepSeek-R1: 72.0 (#143), GLM-5.3-Flash: 77.5 (#20)
| Benchmark | DeepSeek-R1 | GLM-5.3-Flash |
|---|---|---|
| LMArena Instruction Following | 1382 | 1478 |
| LiveBench Instruction Following | 80.5% | — |
| IFEval | 78.4% | — |
Long Context Too close to call
DeepSeek-R1: 45.4 (#36), GLM-5.3-Flash: 45.4 (#39)
| Benchmark | DeepSeek-R1 | GLM-5.3-Flash |
|---|---|---|
| LMArena Longer Query | 1391 | 1482 |
| Fiction.LiveBench | 75% | — |
Writing & Preference GLM-5.3-Flash leads
DeepSeek-R1: 61.4 (#88), GLM-5.3-Flash: 65.3 (#50)
| Benchmark | DeepSeek-R1 | GLM-5.3-Flash |
|---|---|---|
| LMArena Text | 1428 | 1471 |
| LMArena Creative Writing | 1405 | 1442 |
| LMArena Multi-Turn | 1405 | 1467 |
| Short-Story Creative Writing | 83% | — |
| EQ-Bench Creative Writing | 1500 | — |
| WildBench | 82.8% | — |
| LiveBench Language | 48.5% | — |
Frequently asked questions
Is DeepSeek-R1 better than GLM-5.3-Flash?
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 42.3 on the Noometry Index.
Which is cheaper, DeepSeek-R1 or GLM-5.3-Flash?
GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; DeepSeek-R1 lists at $0.50 and $2.15.
Is DeepSeek-R1 or GLM-5.3-Flash better for coding?
GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 46.3 in the Noometry coding category.
Which has the bigger context window?
GLM-5.3-Flash does, with 1M tokens against 164K.
How many benchmarks do DeepSeek-R1 and GLM-5.3-Flash share?
25 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and GLM-5.3-Flash has 40.