Model comparison
DeepSeek-V2.5 (Sep 2024) vs GLM-4.5V
GLM-4.5V is the stronger model overall, scoring 39.8 to 37.6 on the Noometry Index.
Last verified . 13 shared benchmarks.
DeepSeekSummary
- They share 13 benchmarks with published results for both. DeepSeek-V2.5 (Sep 2024) scores higher in 0 categories and GLM-4.5V in 8 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in coding, where GLM-4.5V leads 39.5 to 31.7.
Side by side
| DeepSeek-V2.5 (Sep 2024) | GLM-4.5V | |
|---|---|---|
| Provider | DeepSeek | Z.ai (Zhipu) |
| Noometry Index | 37.6 | 39.8 |
| Released | 2024-09-06 | 2025-08-11 |
| Weights | Open | Open |
| Context window | — | 64K |
| Max output | — | 16K |
| Input $ / M tokens | — | $0.60 |
| Output $ / M tokens | — | $1.80 |
| Results tracked | 22 | 15 |
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Category by category
Coding GLM-4.5V leads
DeepSeek-V2.5 (Sep 2024): 31.7 (#281), GLM-4.5V: 39.5 (#155)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | GLM-4.5V |
|---|---|---|
| LMArena Coding | 1309 | 1347 |
| Aider Polyglot | 17.8% | — |
| BigCodeBench Instruct | 48.6% | — |
| BigCodeBench Complete | 53.2% | — |
| HumanEval+ | 83.5% | — |
| MBPP+ | 74.1% | — |
Reasoning GLM-4.5V leads
DeepSeek-V2.5 (Sep 2024): 25.6 (#145), GLM-4.5V: 27.4 (#119)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | GLM-4.5V |
|---|---|---|
| LMArena Hard Prompts | 1289 | 1334 |
| Kagi LLM Benchmark | — | 59.8% |
Math GLM-4.5V leads
DeepSeek-V2.5 (Sep 2024): 35.9 (#177), GLM-4.5V: 37.4 (#159)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | GLM-4.5V |
|---|---|---|
| LMArena Math | 1288 | 1354 |
Knowledge GLM-4.5V leads
DeepSeek-V2.5 (Sep 2024): 34.8 (#193), GLM-4.5V: 37.5 (#156)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | GLM-4.5V |
|---|---|---|
| LMArena Expert | 1266 | 1353 |
Multimodal Not comparable
DeepSeek-V2.5 (Sep 2024): —, GLM-4.5V: 34.3 (#92)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | GLM-4.5V |
|---|---|---|
| LMArena Vision | — | 1154 |
Multilingual GLM-4.5V leads
DeepSeek-V2.5 (Sep 2024): 42.5 (#193), GLM-4.5V: 44.6 (#177)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | GLM-4.5V |
|---|---|---|
| LMArena Non-English | 1273 | 1303 |
| LMArena Chinese | 1318 | 1337 |
| LMArena Russian | 1289 | 1298 |
| LMArena Spanish | 1248 | 1336 |
| LMArena French | 1289 | — |
| LMArena German | 1258 | — |
| LMArena Japanese | 1228 | — |
| LMArena Korean | 1209 | — |
Instruction Following GLM-4.5V leads
DeepSeek-V2.5 (Sep 2024): 67.5 (#194), GLM-4.5V: 69.2 (#175)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | GLM-4.5V |
|---|---|---|
| LMArena Instruction Following | 1280 | 1311 |
Long Context Too close to call
DeepSeek-V2.5 (Sep 2024): 39.5 (#174), GLM-4.5V: 39.6 (#171)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | GLM-4.5V |
|---|---|---|
| LMArena Longer Query | 1301 | 1304 |
Writing & Preference GLM-4.5V leads
DeepSeek-V2.5 (Sep 2024): 49.8 (#187), GLM-4.5V: 52.5 (#170)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | GLM-4.5V |
|---|---|---|
| LMArena Text | 1294 | 1333 |
| LMArena Creative Writing | 1285 | 1295 |
| LMArena Multi-Turn | 1297 | 1332 |
Frequently asked questions
Is DeepSeek-V2.5 (Sep 2024) better than GLM-4.5V?
GLM-4.5V is the stronger model overall, scoring 39.8 to 37.6 on the Noometry Index.
Is DeepSeek-V2.5 (Sep 2024) or GLM-4.5V better for coding?
GLM-4.5V scores higher on coding benchmarks: 39.5 versus 31.7 in the Noometry coding category.
How many benchmarks do DeepSeek-V2.5 (Sep 2024) and GLM-4.5V share?
13 benchmarks have published results for both models. DeepSeek-V2.5 (Sep 2024) has 22 scored results on Noometry and GLM-4.5V has 15.