Model comparison
DeepSeek-V3.1 vs GLM-5.3-Flash
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 42.8 on the Noometry Index.
Last verified . 18 shared benchmarks.
DeepSeekSummary
- They share 18 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 0 categories 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 27.9.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $0.25 / $0.95 for DeepSeek-V3.1.
- GLM-5.3-Flash accepts more context: 1M tokens versus 164K.
Side by side
| DeepSeek-V3.1 | GLM-5.3-Flash | |
|---|---|---|
| Provider | DeepSeek | Z.ai (Zhipu) |
| Noometry Index | 42.8 | 51.8 |
| Released | 2025-08-21 | 2026-08-20 |
| Weights | Open | Open |
| Context window | 164K | 1M |
| Max output | 8K | 131K |
| Input $ / M tokens | $0.25 | $0.15 |
| Output $ / M tokens | $0.95 | $0.50 |
| Results tracked | 27 | 40 |
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Category by category
Coding GLM-5.3-Flash leads
DeepSeek-V3.1: 40.3 (#144), GLM-5.3-Flash: 53.1 (#31)
| Benchmark | DeepSeek-V3.1 | GLM-5.3-Flash |
|---|---|---|
| LMArena Coding | 1417 | 1508 |
| DeepSWE | — | 63.4% |
| FrontierCode | — | 31.8% |
| CursorBench | — | 36.8% |
| LMArena WebDev | — | 1609 |
| FrontierSWE | — | 18.1% |
| SciCode | — | 51.6% |
| WeirdML | 38.4% | — |
| ALE-Bench | — | 303.55 |
Agentic & Tool Use Not comparable
DeepSeek-V3.1: —, GLM-5.3-Flash: 34.2 (#47)
| Benchmark | DeepSeek-V3.1 | GLM-5.3-Flash |
|---|---|---|
| APEX-Agents | — | 52.8% |
| GDP.pdf | — | 14% |
Reasoning GLM-5.3-Flash leads
DeepSeek-V3.1: 27.9 (#110), GLM-5.3-Flash: 48.0 (#42)
| Benchmark | DeepSeek-V3.1 | GLM-5.3-Flash |
|---|---|---|
| LMArena Hard Prompts | 1417 | 1491 |
| Epoch Capabilities Index | 139.92 | 151.88 |
| ARC-AGI-2 | — | 65.8% |
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| ARC-AGI-1 | — | 91% |
| CritPt | — | 15.4% |
| Chess Puzzles | — | 14% |
| Mystery Game Puzzles | — | 8% |
| DTBench | 82.7% | — |
| LMCA | 24.3% | — |
| Surface Evolver Bench | — | 52.5% |
| Bench to the Future 3 | — | 0.15 |
| ForecastBench | 58 | — |
Math GLM-5.3-Flash leads
DeepSeek-V3.1: 38.9 (#122), GLM-5.3-Flash: 53.3 (#47)
| Benchmark | DeepSeek-V3.1 | GLM-5.3-Flash |
|---|---|---|
| LMArena Math | 1420 | 1500 |
| FrontierMath (Tiers 1-3) | — | 55.8% |
| FrontierMath Tier 4 | — | 17.1% |
| OTIS Mock AIME 2024-2025 | — | 93.9% |
| ProofBench | — | 21% |
Knowledge GLM-5.3-Flash leads
DeepSeek-V3.1: 43.7 (#90), GLM-5.3-Flash: 58.4 (#36)
| Benchmark | DeepSeek-V3.1 | GLM-5.3-Flash |
|---|---|---|
| LMArena Expert | 1405 | 1513 |
| GPQA Diamond | — | 90.2% |
| Vectara Hallucination Rate | 5.5% | — |
Multimodal Not comparable
DeepSeek-V3.1: —, GLM-5.3-Flash: 42.8 (#27)
| Benchmark | DeepSeek-V3.1 | GLM-5.3-Flash |
|---|---|---|
| LMArena Vision | — | 1296 |
Multilingual GLM-5.3-Flash leads
DeepSeek-V3.1: 51.6 (#106), GLM-5.3-Flash: 56.0 (#25)
| Benchmark | DeepSeek-V3.1 | GLM-5.3-Flash |
|---|---|---|
| LMArena Non-English | 1400 | 1462 |
| LMArena Chinese | 1469 | 1527 |
| LMArena French | 1447 | 1496 |
| LMArena German | 1411 | 1470 |
| LMArena Japanese | 1378 | 1429 |
| LMArena Korean | 1337 | 1446 |
| LMArena Russian | 1405 | 1469 |
| LMArena Spanish | 1431 | 1471 |
Instruction Following GLM-5.3-Flash leads
DeepSeek-V3.1: 73.9 (#110), GLM-5.3-Flash: 77.5 (#20)
| Benchmark | DeepSeek-V3.1 | GLM-5.3-Flash |
|---|---|---|
| LMArena Instruction Following | 1400 | 1478 |
Long Context GLM-5.3-Flash leads
DeepSeek-V3.1: 36.3 (#232), GLM-5.3-Flash: 45.4 (#39)
| Benchmark | DeepSeek-V3.1 | GLM-5.3-Flash |
|---|---|---|
| LMArena Longer Query | 1422 | 1482 |
| Fiction.LiveBench | 52.8% | — |
Writing & Preference GLM-5.3-Flash leads
DeepSeek-V3.1: 60.3 (#98), GLM-5.3-Flash: 65.3 (#50)
| Benchmark | DeepSeek-V3.1 | GLM-5.3-Flash |
|---|---|---|
| LMArena Text | 1420 | 1471 |
| LMArena Creative Writing | 1401 | 1442 |
| LMArena Multi-Turn | 1408 | 1467 |
| EQ-Bench Creative Writing | 1436 | — |
Frequently asked questions
Is DeepSeek-V3.1 better than GLM-5.3-Flash?
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 42.8 on the Noometry Index.
Which is cheaper, DeepSeek-V3.1 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-V3.1 lists at $0.25 and $0.95.
Is DeepSeek-V3.1 or GLM-5.3-Flash better for coding?
GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 40.3 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-V3.1 and GLM-5.3-Flash share?
18 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and GLM-5.3-Flash has 40.