Model comparison
DeepSeek-V3.2-Speciale vs GLM-4.5V
DeepSeek-V3.2-Speciale and GLM-4.5V score almost the same on the Noometry Index (39.7 vs 39.8), so choose on price, context window or the category you care about most.
Last verified . 0 shared benchmarks.
DeepSeekSummary
- The widest gap is in writing & preference, where GLM-4.5V leads 52.5 to 46.0.
- Both cost about the same: $0.58 input and $1.68 output per million tokens.
- DeepSeek-V3.2-Speciale accepts more context: 128K tokens versus 64K.
Side by side
| DeepSeek-V3.2-Speciale | GLM-4.5V | |
|---|---|---|
| Provider | DeepSeek | Z.ai (Zhipu) |
| Noometry Index | 39.7 | 39.8 |
| Released | 2025-12-01 | 2025-08-11 |
| Weights | Open | Open |
| Context window | 128K | 64K |
| Max output | 128K | 16K |
| Input $ / M tokens | $0.58 | $0.60 |
| Output $ / M tokens | $1.68 | $1.80 |
| Results tracked | 3 | 15 |
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Category by category
Coding Too close to call
DeepSeek-V3.2-Speciale: 40.4 (#140), GLM-4.5V: 39.5 (#155)
| Benchmark | DeepSeek-V3.2-Speciale | GLM-4.5V |
|---|---|---|
| WeirdML | 46.7% | — |
| LMArena Coding | — | 1347 |
Reasoning DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 32.9 (#73), GLM-4.5V: 27.4 (#119)
| Benchmark | DeepSeek-V3.2-Speciale | GLM-4.5V |
|---|---|---|
| SimpleBench | 52.6% | — |
| Kagi LLM Benchmark | — | 59.8% |
| LMArena Hard Prompts | — | 1334 |
Math Not comparable
DeepSeek-V3.2-Speciale: —, GLM-4.5V: 37.4 (#159)
| Benchmark | DeepSeek-V3.2-Speciale | GLM-4.5V |
|---|---|---|
| LMArena Math | — | 1354 |
Knowledge Not comparable
DeepSeek-V3.2-Speciale: —, GLM-4.5V: 37.5 (#156)
| Benchmark | DeepSeek-V3.2-Speciale | GLM-4.5V |
|---|---|---|
| LMArena Expert | — | 1353 |
Multimodal Not comparable
DeepSeek-V3.2-Speciale: —, GLM-4.5V: 34.3 (#92)
| Benchmark | DeepSeek-V3.2-Speciale | GLM-4.5V |
|---|---|---|
| LMArena Vision | — | 1154 |
Multilingual Not comparable
DeepSeek-V3.2-Speciale: —, GLM-4.5V: 44.6 (#177)
| Benchmark | DeepSeek-V3.2-Speciale | GLM-4.5V |
|---|---|---|
| LMArena Non-English | — | 1303 |
| LMArena Chinese | — | 1337 |
| LMArena Russian | — | 1298 |
| LMArena Spanish | — | 1336 |
Instruction Following Not comparable
DeepSeek-V3.2-Speciale: —, GLM-4.5V: 69.2 (#175)
| Benchmark | DeepSeek-V3.2-Speciale | GLM-4.5V |
|---|---|---|
| LMArena Instruction Following | — | 1311 |
Long Context Not comparable
DeepSeek-V3.2-Speciale: —, GLM-4.5V: 39.6 (#171)
| Benchmark | DeepSeek-V3.2-Speciale | GLM-4.5V |
|---|---|---|
| LMArena Longer Query | — | 1304 |
Writing & Preference GLM-4.5V leads
DeepSeek-V3.2-Speciale: 46.0 (#222), GLM-4.5V: 52.5 (#170)
| Benchmark | DeepSeek-V3.2-Speciale | GLM-4.5V |
|---|---|---|
| LMArena Text | — | 1333 |
| LMArena Creative Writing | — | 1295 |
| EQ-Bench Creative Writing | 1276 | — |
| LMArena Multi-Turn | — | 1332 |
Frequently asked questions
Is DeepSeek-V3.2-Speciale better than GLM-4.5V?
DeepSeek-V3.2-Speciale and GLM-4.5V score almost the same on the Noometry Index (39.7 vs 39.8), so choose on price, context window or the category you care about most.
Which is cheaper, DeepSeek-V3.2-Speciale or GLM-4.5V?
DeepSeek-V3.2-Speciale is cheaper. It lists at $0.58 per million input tokens and $1.68 per million output tokens; GLM-4.5V lists at $0.60 and $1.80.
Is DeepSeek-V3.2-Speciale or GLM-4.5V better for coding?
They score almost the same on coding (40.4 vs 39.5); test both on your own repository before choosing.
Which has the bigger context window?
DeepSeek-V3.2-Speciale does, with 128K tokens against 64K.
How many benchmarks do DeepSeek-V3.2-Speciale and GLM-4.5V share?
0 benchmarks have published results for both models. DeepSeek-V3.2-Speciale has 3 scored results on Noometry and GLM-4.5V has 15.