Model comparison
Gemma 3 12B vs GLM-5.3-Flash
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 32.1 on the Noometry Index. Gemma 3 12B costs 3.2× less per token, which makes it the better buy when GLM-5.3-Flash's lead doesn't matter for your workload.
Last verified . 18 shared benchmarks.
GoogleSummary
- They share 18 benchmarks with published results for both. Gemma 3 12B scores higher in 0 categories and GLM-5.3-Flash in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5.3-Flash leads 48.0 to 15.7.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 16.7% for Gemma 3 12B and 93.9% for GLM-5.3-Flash.
- Gemma 3 12B is cheaper at $0.05 / $0.15 per million input/output tokens, against $0.15 / $0.50 for GLM-5.3-Flash.
- GLM-5.3-Flash accepts more context: 1M tokens versus 131K.
Side by side
| Gemma 3 12B | GLM-5.3-Flash | |
|---|---|---|
| Provider | Z.ai (Zhipu) | |
| Noometry Index | 32.1 | 51.8 |
| Released | 2025-03-12 | 2026-08-20 |
| Weights | Open | Open |
| Context window | 131K | 1M |
| Max output | 8K | 131K |
| Input $ / M tokens | $0.05 | $0.15 |
| Output $ / M tokens | $0.15 | $0.50 |
| Results tracked | 24 | 40 |
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Category by category
Coding GLM-5.3-Flash leads
Gemma 3 12B: 31.7 (#280), GLM-5.3-Flash: 53.1 (#31)
| Benchmark | Gemma 3 12B | GLM-5.3-Flash |
|---|---|---|
| SciCode | 17.4% | 51.6% |
| LMArena Coding | 1281 | 1508 |
| DeepSWE | — | 63.4% |
| FrontierCode | — | 31.8% |
| CursorBench | — | 36.8% |
| LMArena WebDev | — | 1609 |
| FrontierSWE | — | 18.1% |
| ALE-Bench | — | 303.55 |
Agentic & Tool Use GLM-5.3-Flash leads
Gemma 3 12B: 25.5 (#108), GLM-5.3-Flash: 34.2 (#47)
| Benchmark | Gemma 3 12B | GLM-5.3-Flash |
|---|---|---|
| APEX-Agents | — | 52.8% |
| Berkeley Function Calling Leaderboard | 30.4% | — |
| GDP.pdf | — | 14% |
Reasoning GLM-5.3-Flash leads
Gemma 3 12B: 15.7 (#313), GLM-5.3-Flash: 48.0 (#42)
| Benchmark | Gemma 3 12B | GLM-5.3-Flash |
|---|---|---|
| CritPt | 0% | 15.4% |
| Chess Puzzles | 0% | 14% |
| LMArena Hard Prompts | 1309 | 1491 |
| Epoch Capabilities Index | 123.5 | 151.88 |
| ARC-AGI-2 | — | 65.8% |
| ARC-AGI-1 | — | 91% |
| Mystery Game Puzzles | — | 8% |
| DTBench | 48.8% | — |
| LMCA | 4.5% | — |
| Surface Evolver Bench | — | 52.5% |
| Bench to the Future 3 | — | 0.15 |
Math GLM-5.3-Flash leads
Gemma 3 12B: 22.3 (#279), GLM-5.3-Flash: 53.3 (#47)
| Benchmark | Gemma 3 12B | GLM-5.3-Flash |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 16.7% | 93.9% |
| LMArena Math | 1307 | 1500 |
| FrontierMath (Tiers 1-3) | — | 55.8% |
| FrontierMath Tier 4 | — | 17.1% |
| ProofBench | — | 21% |
Knowledge GLM-5.3-Flash leads
Gemma 3 12B: 26.5 (#257), GLM-5.3-Flash: 58.4 (#36)
| Benchmark | Gemma 3 12B | GLM-5.3-Flash |
|---|---|---|
| GPQA Diamond | 39.5% | 90.2% |
| LMArena Expert | 1248 | 1513 |
| Vectara Hallucination Rate | 4.4% | — |
Multimodal Not comparable
Gemma 3 12B: —, GLM-5.3-Flash: 42.8 (#27)
| Benchmark | Gemma 3 12B | GLM-5.3-Flash |
|---|---|---|
| LMArena Vision | — | 1296 |
| MindCube | 46.7% | — |
Multilingual GLM-5.3-Flash leads
Gemma 3 12B: 45.7 (#165), GLM-5.3-Flash: 56.0 (#25)
| Benchmark | Gemma 3 12B | GLM-5.3-Flash |
|---|---|---|
| LMArena Non-English | 1318 | 1462 |
| LMArena German | 1370 | 1470 |
| LMArena Russian | 1335 | 1469 |
| LMArena Chinese | — | 1527 |
| LMArena French | — | 1496 |
| LMArena Japanese | — | 1429 |
| LMArena Korean | — | 1446 |
| LMArena Spanish | — | 1471 |
Instruction Following GLM-5.3-Flash leads
Gemma 3 12B: 68.6 (#186), GLM-5.3-Flash: 77.5 (#20)
| Benchmark | Gemma 3 12B | GLM-5.3-Flash |
|---|---|---|
| LMArena Instruction Following | 1299 | 1478 |
Long Context GLM-5.3-Flash leads
Gemma 3 12B: 40.0 (#162), GLM-5.3-Flash: 45.4 (#39)
| Benchmark | Gemma 3 12B | GLM-5.3-Flash |
|---|---|---|
| LMArena Longer Query | 1317 | 1482 |
Writing & Preference GLM-5.3-Flash leads
Gemma 3 12B: 47.5 (#209), GLM-5.3-Flash: 65.3 (#50)
| Benchmark | Gemma 3 12B | GLM-5.3-Flash |
|---|---|---|
| LMArena Text | 1334 | 1471 |
| LMArena Creative Writing | 1331 | 1442 |
| LMArena Multi-Turn | 1334 | 1467 |
| EQ-Bench Creative Writing | 1126 | — |
Frequently asked questions
Is Gemma 3 12B better than GLM-5.3-Flash?
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 32.1 on the Noometry Index. Gemma 3 12B costs 3.2× less per token, which makes it the better buy when GLM-5.3-Flash's lead doesn't matter for your workload.
Which is cheaper, Gemma 3 12B or GLM-5.3-Flash?
Gemma 3 12B is cheaper. It lists at $0.05 per million input tokens and $0.15 per million output tokens; GLM-5.3-Flash lists at $0.15 and $0.50.
Is Gemma 3 12B 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.
Which has the bigger context window?
GLM-5.3-Flash does, with 1M tokens against 131K.
How many benchmarks do Gemma 3 12B and GLM-5.3-Flash share?
18 benchmarks have published results for both models. Gemma 3 12B has 24 scored results on Noometry and GLM-5.3-Flash has 40.