Model comparison
DeepSeek-V3.2-Exp vs GLM-5.3-Flash
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 44.3 on the Noometry Index.
Last verified . 28 shared benchmarks.
DeepSeekSummary
- They share 28 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 1 category and GLM-5.3-Flash in 8 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5.3-Flash leads 48.0 to 22.1.
- The biggest single-benchmark swing is ARC-AGI-2: 4% for DeepSeek-V3.2-Exp and 65.8% for GLM-5.3-Flash.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $0.26 / $0.38 for DeepSeek-V3.2-Exp.
- GLM-5.3-Flash accepts more context: 1M tokens versus 164K.
Side by side
| DeepSeek-V3.2-Exp | GLM-5.3-Flash | |
|---|---|---|
| Provider | DeepSeek | Z.ai (Zhipu) |
| Noometry Index | 44.3 | 51.8 |
| Released | 2025-09-29 | 2026-08-20 |
| Weights | Open | Open |
| Context window | 164K | 1M |
| Max output | 66K | 131K |
| Input $ / M tokens | $0.26 | $0.15 |
| Output $ / M tokens | $0.38 | $0.50 |
| Results tracked | 49 | 40 |
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Category by category
Coding GLM-5.3-Flash leads
DeepSeek-V3.2-Exp: 46.5 (#65), GLM-5.3-Flash: 53.1 (#31)
| Benchmark | DeepSeek-V3.2-Exp | GLM-5.3-Flash |
|---|---|---|
| LMArena WebDev | 1362 | 1609 |
| SciCode | 38.9% | 51.6% |
| LMArena Coding | 1454 | 1508 |
| DeepSWE | — | 63.4% |
| FrontierCode | — | 31.8% |
| SWE-bench Verified (bash only) | 70% | — |
| Aider Polyglot | 74.2% | — |
| CursorBench | — | 36.8% |
| SWE-bench Multilingual | 59% | — |
| FrontierSWE | — | 18.1% |
| WeirdML | 39.5% | — |
| ALE-Bench | — | 303.55 |
Agentic & Tool Use GLM-5.3-Flash leads
DeepSeek-V3.2-Exp: 32.7 (#59), GLM-5.3-Flash: 34.2 (#47)
| Benchmark | DeepSeek-V3.2-Exp | GLM-5.3-Flash |
|---|---|---|
| APEX-Agents | 21.3% | 52.8% |
| Terminal-Bench | 39.6% | — |
| Berkeley Function Calling Leaderboard | 56.7% | — |
| TheAgentCompany | 42.9% | — |
| GDP.pdf | — | 14% |
| Vending-Bench 2 | 1,034 | — |
Reasoning GLM-5.3-Flash leads
DeepSeek-V3.2-Exp: 22.1 (#208), GLM-5.3-Flash: 48.0 (#42)
| Benchmark | DeepSeek-V3.2-Exp | GLM-5.3-Flash |
|---|---|---|
| ARC-AGI-2 | 4% | 65.8% |
| ARC-AGI-1 | 57% | 91% |
| CritPt | 2.9% | 15.4% |
| Chess Puzzles | 14% | 14% |
| LMArena Hard Prompts | 1434 | 1491 |
| Epoch Capabilities Index | 146.27 | 151.88 |
| Kagi LLM Benchmark | 52.2% | — |
| NYT Connections (extended) | 36.7% | — |
| Thematic Generalization | 65% | — |
| Mystery Game Puzzles | — | 8% |
| DTBench | 87.7% | — |
| LMCA | 29.1% | — |
| Surface Evolver Bench | — | 52.5% |
| Bench to the Future 3 | — | 0.15 |
Math GLM-5.3-Flash leads
DeepSeek-V3.2-Exp: 41.7 (#87), GLM-5.3-Flash: 53.3 (#47)
| Benchmark | DeepSeek-V3.2-Exp | GLM-5.3-Flash |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 87.8% | 93.9% |
| ProofBench | 8% | 21% |
| LMArena Math | 1435 | 1500 |
| FrontierMath (Tiers 1-3) | — | 55.8% |
| FrontierMath Tier 4 | — | 17.1% |
| MathArena Final-Answer Competitions | 57.7% | — |
| FrontierMath (Feb 2025 set) | 22.1% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge GLM-5.3-Flash leads
DeepSeek-V3.2-Exp: 51.7 (#66), GLM-5.3-Flash: 58.4 (#36)
| Benchmark | DeepSeek-V3.2-Exp | GLM-5.3-Flash |
|---|---|---|
| GPQA Diamond | 83.4% | 90.2% |
| LMArena Expert | 1436 | 1513 |
| Vectara Hallucination Rate | 5.3% | — |
Multimodal Not comparable
DeepSeek-V3.2-Exp: —, GLM-5.3-Flash: 42.8 (#27)
| Benchmark | DeepSeek-V3.2-Exp | GLM-5.3-Flash |
|---|---|---|
| LMArena Vision | — | 1296 |
Multilingual GLM-5.3-Flash leads
DeepSeek-V3.2-Exp: 52.2 (#90), GLM-5.3-Flash: 56.0 (#25)
| Benchmark | DeepSeek-V3.2-Exp | GLM-5.3-Flash |
|---|---|---|
| LMArena Non-English | 1409 | 1462 |
| LMArena Chinese | 1461 | 1527 |
| LMArena French | 1433 | 1496 |
| LMArena German | 1440 | 1470 |
| LMArena Japanese | 1374 | 1429 |
| LMArena Korean | 1371 | 1446 |
| LMArena Russian | 1424 | 1469 |
| LMArena Spanish | 1440 | 1471 |
Instruction Following GLM-5.3-Flash leads
DeepSeek-V3.2-Exp: 74.5 (#93), GLM-5.3-Flash: 77.5 (#20)
| Benchmark | DeepSeek-V3.2-Exp | GLM-5.3-Flash |
|---|---|---|
| LMArena Instruction Following | 1413 | 1478 |
Long Context DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 47.6 (#16), GLM-5.3-Flash: 45.4 (#39)
| Benchmark | DeepSeek-V3.2-Exp | GLM-5.3-Flash |
|---|---|---|
| LMArena Longer Query | 1428 | 1482 |
| Fiction.LiveBench | 83.3% | — |
| CL-bench | 13.2% | — |
| CL-bench Life | 9.5% | — |
Writing & Preference GLM-5.3-Flash leads
DeepSeek-V3.2-Exp: 62.4 (#77), GLM-5.3-Flash: 65.3 (#50)
| Benchmark | DeepSeek-V3.2-Exp | GLM-5.3-Flash |
|---|---|---|
| LMArena Text | 1425 | 1471 |
| LMArena Creative Writing | 1403 | 1442 |
| LMArena Multi-Turn | 1427 | 1467 |
| EQ-Bench Creative Writing | 1515 | — |
Frequently asked questions
Is DeepSeek-V3.2-Exp better than GLM-5.3-Flash?
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 44.3 on the Noometry Index.
Which is cheaper, DeepSeek-V3.2-Exp 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-V3.2-Exp lists at $0.26 and $0.38.
Is DeepSeek-V3.2-Exp or GLM-5.3-Flash better for coding?
GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 46.5 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-V3.2-Exp and GLM-5.3-Flash share?
28 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 49 scored results on Noometry and GLM-5.3-Flash has 40.