Model comparison
DeepSeek-V3.2-Speciale vs GLM-5.3
GLM-5.3 is the stronger model overall, scoring 54.6 to 39.1 on the Noometry Index. DeepSeek-V3.2-Speciale costs 2.5× less per token, which makes it the better buy when GLM-5.3's lead doesn't matter for your workload.
Last verified . 2 shared benchmarks.
DeepSeekSummary
- They share 2 benchmarks with published results for both. DeepSeek-V3.2-Speciale scores higher in 0 categories and GLM-5.3 in 3 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-5.3 leads 75.9 to 46.1.
- The biggest single-benchmark swing is WeirdML: 46.7% for DeepSeek-V3.2-Speciale and 75.4% for GLM-5.3.
- DeepSeek-V3.2-Speciale is cheaper at $0.58 / $1.68 per million input/output tokens, against $1.40 / $4.40 for GLM-5.3.
- GLM-5.3 accepts more context: 1M tokens versus 128K.
Side by side
| DeepSeek-V3.2-Speciale | GLM-5.3 | |
|---|---|---|
| Provider | DeepSeek | Z.ai (Zhipu) |
| Noometry Index | 39.1 | 54.6 |
| Released | 2025-12-01 | 2026-08-14 |
| Weights | Open | Open |
| Context window | 128K | 1M |
| Max output | 128K | 131K |
| Input $ / M tokens | $0.58 | $1.40 |
| Output $ / M tokens | $1.68 | $4.40 |
| Results tracked | 3 | 41 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding GLM-5.3 leads
DeepSeek-V3.2-Speciale: 39.9 (#136), GLM-5.3: 59.0 (#12)
| Benchmark | DeepSeek-V3.2-Speciale | GLM-5.3 |
|---|---|---|
| WeirdML | 46.7% | 75.4% |
| DeepSWE | — | 69% |
| FrontierCode | — | 40.1% |
| LMArena WebDev | — | 1623 |
| FrontierSWE | — | 30.2% |
| SciCode | — | 59% |
| LMArena Coding | — | 1499 |
| ALE-Bench | — | 1,317 |
Agentic & Tool Use Not comparable
DeepSeek-V3.2-Speciale: —, GLM-5.3: 36.3 (#34)
| Benchmark | DeepSeek-V3.2-Speciale | GLM-5.3 |
|---|---|---|
| APEX-Agents | — | 56.6% |
| Vending-Bench 2 | — | 8,164 |
Reasoning GLM-5.3 leads
DeepSeek-V3.2-Speciale: 32.1 (#65), GLM-5.3: 45.2 (#42)
| Benchmark | DeepSeek-V3.2-Speciale | GLM-5.3 |
|---|---|---|
| SimpleBench | 52.6% | — |
| NYT Connections (extended) | — | 74.2% |
| CritPt | — | 19.1% |
| Chess Puzzles | — | 21% |
| LMArena Hard Prompts | — | 1491 |
| Mystery Game Puzzles | — | 33% |
| DTBench | — | 87.7% |
| LMCA | — | 55.5% |
| Bench to the Future 3 | — | 0.15 |
| Epoch Capabilities Index | — | 155.56 |
Math Not comparable
DeepSeek-V3.2-Speciale: —, GLM-5.3: 62.1 (#27)
| Benchmark | DeepSeek-V3.2-Speciale | GLM-5.3 |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 68.8% |
| FrontierMath Tier 4 | — | 29.3% |
| OTIS Mock AIME 2024-2025 | — | 91.1% |
| ProofBench | — | 49% |
| LMArena Math | — | 1493 |
Knowledge Not comparable
DeepSeek-V3.2-Speciale: —, GLM-5.3: 58.3 (#34)
| Benchmark | DeepSeek-V3.2-Speciale | GLM-5.3 |
|---|---|---|
| GPQA Diamond | — | 90.9% |
| SimpleQA Verified | — | 41% |
| LMArena Expert | — | 1512 |
Multilingual Not comparable
DeepSeek-V3.2-Speciale: —, GLM-5.3: 55.9 (#27)
| Benchmark | DeepSeek-V3.2-Speciale | GLM-5.3 |
|---|---|---|
| LMArena Non-English | — | 1457 |
| LMArena Chinese | — | 1523 |
| LMArena French | — | 1499 |
| LMArena German | — | 1505 |
| LMArena Japanese | — | 1438 |
| LMArena Korean | — | 1478 |
| LMArena Russian | — | 1460 |
| LMArena Spanish | — | 1444 |
Instruction Following Not comparable
DeepSeek-V3.2-Speciale: —, GLM-5.3: 77.8 (#15)
| Benchmark | DeepSeek-V3.2-Speciale | GLM-5.3 |
|---|---|---|
| LMArena Instruction Following | — | 1481 |
Long Context Not comparable
DeepSeek-V3.2-Speciale: —, GLM-5.3: 45.5 (#32)
| Benchmark | DeepSeek-V3.2-Speciale | GLM-5.3 |
|---|---|---|
| LMArena Longer Query | — | 1485 |
Writing & Preference GLM-5.3 leads
DeepSeek-V3.2-Speciale: 46.1 (#217), GLM-5.3: 75.9 (#6)
| Benchmark | DeepSeek-V3.2-Speciale | GLM-5.3 |
|---|---|---|
| EQ-Bench Creative Writing | 1276 | 2075 |
| LMArena Text | — | 1473 |
| LMArena Creative Writing | — | 1459 |
| LMArena Multi-Turn | — | 1473 |
Frequently asked questions
Is DeepSeek-V3.2-Speciale better than GLM-5.3?
GLM-5.3 is the stronger model overall, scoring 54.6 to 39.1 on the Noometry Index. DeepSeek-V3.2-Speciale costs 2.5× less per token, which makes it the better buy when GLM-5.3's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3.2-Speciale or GLM-5.3?
DeepSeek-V3.2-Speciale is cheaper. It lists at $0.58 per million input tokens and $1.68 per million output tokens; GLM-5.3 lists at $1.40 and $4.40.
Is DeepSeek-V3.2-Speciale or GLM-5.3 better for coding?
GLM-5.3 scores higher on coding benchmarks: 59.0 versus 39.9 in the Noometry coding category.
Which has the bigger context window?
GLM-5.3 does, with 1M tokens against 128K.
How many benchmarks do DeepSeek-V3.2-Speciale and GLM-5.3 share?
2 benchmarks have published results for both models. DeepSeek-V3.2-Speciale has 3 scored results on Noometry and GLM-5.3 has 41.