Model comparison
DeepSeek-V3.1 vs GLM-5.3-Flash
GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 42.2 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 math, where GLM-5.3-Flash leads 53.1 to 36.1.
- 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 | 42.2 | 49.6 |
| 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 | 36 |
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Category by category
Coding GLM-5.3-Flash leads
DeepSeek-V3.1: 39.8 (#140), GLM-5.3-Flash: 53.7 (#26)
| Benchmark | DeepSeek-V3.1 | GLM-5.3-Flash |
|---|---|---|
| LMArena Coding | 1417 | 1506 |
| DeepSWE | — | 63.4% |
| FrontierCode | — | 31.8% |
| LMArena WebDev | — | 1614 |
| SciCode | — | 51.6% |
| WeirdML | 38.4% | — |
| ALE-Bench | — | 303.55 |
Agentic & Tool Use Not comparable
DeepSeek-V3.1: —, GLM-5.3-Flash: 34.3 (#41)
| Benchmark | DeepSeek-V3.1 | GLM-5.3-Flash |
|---|---|---|
| APEX-Agents | — | 52.8% |
| GDP.pdf | — | 14% |
Reasoning GLM-5.3-Flash leads
DeepSeek-V3.1: 27.3 (#109), GLM-5.3-Flash: 31.7 (#69)
| Benchmark | DeepSeek-V3.1 | GLM-5.3-Flash |
|---|---|---|
| LMArena Hard Prompts | 1417 | 1490 |
| Epoch Capabilities Index | 139.92 | 151.9 |
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| 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: 36.1 (#125), GLM-5.3-Flash: 53.1 (#38)
| Benchmark | DeepSeek-V3.1 | GLM-5.3-Flash |
|---|---|---|
| LMArena Math | 1420 | 1499 |
| 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: 43.0 (#86), GLM-5.3-Flash: 58.4 (#33)
| Benchmark | DeepSeek-V3.1 | GLM-5.3-Flash |
|---|---|---|
| LMArena Expert | 1403 | 1512 |
| GPQA Diamond | — | 90.2% |
| Vectara Hallucination Rate | 5.5% | — |
Multimodal Not comparable
DeepSeek-V3.1: —, GLM-5.3-Flash: 42.6 (#25)
| Benchmark | DeepSeek-V3.1 | GLM-5.3-Flash |
|---|---|---|
| LMArena Vision | — | 1304 |
Multilingual GLM-5.3-Flash leads
DeepSeek-V3.1: 51.8 (#100), GLM-5.3-Flash: 56.2 (#24)
| Benchmark | DeepSeek-V3.1 | GLM-5.3-Flash |
|---|---|---|
| LMArena Non-English | 1400 | 1462 |
| LMArena Chinese | 1470 | 1529 |
| LMArena French | 1447 | 1495 |
| LMArena German | 1412 | 1457 |
| LMArena Japanese | 1374 | 1420 |
| LMArena Korean | 1337 | 1443 |
| LMArena Russian | 1405 | 1472 |
| LMArena Spanish | 1429 | 1471 |
Instruction Following GLM-5.3-Flash leads
DeepSeek-V3.1: 74.1 (#105), GLM-5.3-Flash: 77.8 (#17)
| Benchmark | DeepSeek-V3.1 | GLM-5.3-Flash |
|---|---|---|
| LMArena Instruction Following | 1400 | 1480 |
Long Context GLM-5.3-Flash leads
DeepSeek-V3.1: 36.3 (#227), GLM-5.3-Flash: 45.3 (#41)
| Benchmark | DeepSeek-V3.1 | GLM-5.3-Flash |
|---|---|---|
| LMArena Longer Query | 1422 | 1479 |
| Fiction.LiveBench | 52.8% | — |
Writing & Preference GLM-5.3-Flash leads
DeepSeek-V3.1: 60.5 (#93), GLM-5.3-Flash: 65.4 (#49)
| Benchmark | DeepSeek-V3.1 | GLM-5.3-Flash |
|---|---|---|
| LMArena Text | 1420 | 1471 |
| LMArena Creative Writing | 1401 | 1440 |
| LMArena Multi-Turn | 1408 | 1471 |
| 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 49.6 to 42.2 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: 53.7 versus 39.8 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 36.