Model comparison
DeepSeek-V2.5 (Sep 2024) vs GLM-5.3-Flash
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 37.6 on the Noometry Index.
Last verified . 17 shared benchmarks.
DeepSeekSummary
- They share 17 benchmarks with published results for both. DeepSeek-V2.5 (Sep 2024) 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 knowledge, where GLM-5.3-Flash leads 58.4 to 34.8.
Side by side
| DeepSeek-V2.5 (Sep 2024) | GLM-5.3-Flash | |
|---|---|---|
| Provider | DeepSeek | Z.ai (Zhipu) |
| Noometry Index | 37.6 | 51.8 |
| Released | 2024-09-06 | 2026-08-20 |
| Weights | Open | Open |
| Context window | — | 1M |
| Max output | — | 131K |
| Input $ / M tokens | — | $0.15 |
| Output $ / M tokens | — | $0.50 |
| Results tracked | 22 | 40 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding GLM-5.3-Flash leads
DeepSeek-V2.5 (Sep 2024): 31.7 (#281), GLM-5.3-Flash: 53.1 (#31)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | GLM-5.3-Flash |
|---|---|---|
| LMArena Coding | 1309 | 1508 |
| DeepSWE | — | 63.4% |
| FrontierCode | — | 31.8% |
| Aider Polyglot | 17.8% | — |
| CursorBench | — | 36.8% |
| LMArena WebDev | — | 1609 |
| FrontierSWE | — | 18.1% |
| SciCode | — | 51.6% |
| BigCodeBench Instruct | 48.6% | — |
| BigCodeBench Complete | 53.2% | — |
| ALE-Bench | — | 303.55 |
| HumanEval+ | 83.5% | — |
| MBPP+ | 74.1% | — |
Agentic & Tool Use Not comparable
DeepSeek-V2.5 (Sep 2024): —, GLM-5.3-Flash: 34.2 (#47)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | GLM-5.3-Flash |
|---|---|---|
| APEX-Agents | — | 52.8% |
| GDP.pdf | — | 14% |
Reasoning GLM-5.3-Flash leads
DeepSeek-V2.5 (Sep 2024): 25.6 (#145), GLM-5.3-Flash: 48.0 (#42)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | GLM-5.3-Flash |
|---|---|---|
| LMArena Hard Prompts | 1289 | 1491 |
| ARC-AGI-2 | — | 65.8% |
| ARC-AGI-1 | — | 91% |
| CritPt | — | 15.4% |
| Chess Puzzles | — | 14% |
| Mystery Game Puzzles | — | 8% |
| Surface Evolver Bench | — | 52.5% |
| Bench to the Future 3 | — | 0.15 |
| Epoch Capabilities Index | — | 151.88 |
Math GLM-5.3-Flash leads
DeepSeek-V2.5 (Sep 2024): 35.9 (#177), GLM-5.3-Flash: 53.3 (#47)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | GLM-5.3-Flash |
|---|---|---|
| LMArena Math | 1288 | 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-V2.5 (Sep 2024): 34.8 (#193), GLM-5.3-Flash: 58.4 (#36)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | GLM-5.3-Flash |
|---|---|---|
| LMArena Expert | 1266 | 1513 |
| GPQA Diamond | — | 90.2% |
Multimodal Not comparable
DeepSeek-V2.5 (Sep 2024): —, GLM-5.3-Flash: 42.8 (#27)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | GLM-5.3-Flash |
|---|---|---|
| LMArena Vision | — | 1296 |
Multilingual GLM-5.3-Flash leads
DeepSeek-V2.5 (Sep 2024): 42.5 (#193), GLM-5.3-Flash: 56.0 (#25)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | GLM-5.3-Flash |
|---|---|---|
| LMArena Non-English | 1273 | 1462 |
| LMArena Chinese | 1318 | 1527 |
| LMArena French | 1289 | 1496 |
| LMArena German | 1258 | 1470 |
| LMArena Japanese | 1228 | 1429 |
| LMArena Korean | 1209 | 1446 |
| LMArena Russian | 1289 | 1469 |
| LMArena Spanish | 1248 | 1471 |
Instruction Following GLM-5.3-Flash leads
DeepSeek-V2.5 (Sep 2024): 67.5 (#194), GLM-5.3-Flash: 77.5 (#20)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | GLM-5.3-Flash |
|---|---|---|
| LMArena Instruction Following | 1280 | 1478 |
Long Context GLM-5.3-Flash leads
DeepSeek-V2.5 (Sep 2024): 39.5 (#174), GLM-5.3-Flash: 45.4 (#39)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | GLM-5.3-Flash |
|---|---|---|
| LMArena Longer Query | 1301 | 1482 |
Writing & Preference GLM-5.3-Flash leads
DeepSeek-V2.5 (Sep 2024): 49.8 (#187), GLM-5.3-Flash: 65.3 (#50)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | GLM-5.3-Flash |
|---|---|---|
| LMArena Text | 1294 | 1471 |
| LMArena Creative Writing | 1285 | 1442 |
| LMArena Multi-Turn | 1297 | 1467 |
Frequently asked questions
Is DeepSeek-V2.5 (Sep 2024) better than GLM-5.3-Flash?
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 37.6 on the Noometry Index.
Is DeepSeek-V2.5 (Sep 2024) or GLM-5.3-Flash better for coding?
GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 31.7 in the Noometry coding category.
How many benchmarks do DeepSeek-V2.5 (Sep 2024) and GLM-5.3-Flash share?
17 benchmarks have published results for both models. DeepSeek-V2.5 (Sep 2024) has 22 scored results on Noometry and GLM-5.3-Flash has 40.