Model comparison
Gemma 4 31B IT vs GLM-5.3-Flash
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 43.5 on the Noometry Index. Gemma 4 31B IT costs 1.6× less per token, which makes it the better buy when GLM-5.3-Flash's lead doesn't matter for your workload.
Last verified . 24 shared benchmarks.
GoogleSummary
- They share 24 benchmarks with published results for both. Gemma 4 31B IT 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 27.2.
- The biggest single-benchmark swing is Surface Evolver Bench: 30.6% for Gemma 4 31B IT and 52.5% for GLM-5.3-Flash.
- Gemma 4 31B IT is cheaper at $0.09 / $0.34 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 262K.
Side by side
| Gemma 4 31B IT | GLM-5.3-Flash | |
|---|---|---|
| Provider | Z.ai (Zhipu) | |
| Noometry Index | 43.5 | 51.8 |
| Released | 2026-04-02 | 2026-08-20 |
| Weights | Open | Open |
| Context window | 262K | 1M |
| Max output | 33K | 131K |
| Input $ / M tokens | $0.09 | $0.15 |
| Output $ / M tokens | $0.34 | $0.50 |
| Results tracked | 35 | 40 |
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Category by category
Coding GLM-5.3-Flash leads
Gemma 4 31B IT: 42.3 (#108), GLM-5.3-Flash: 53.1 (#31)
| Benchmark | Gemma 4 31B IT | GLM-5.3-Flash |
|---|---|---|
| LMArena WebDev | 1366 | 1609 |
| SciCode | 43.4% | 51.6% |
| LMArena Coding | 1459 | 1508 |
| ALE-Bench | 925.5 | 303.55 |
| DeepSWE | — | 63.4% |
| FrontierCode | — | 31.8% |
| CursorBench | — | 36.8% |
| FrontierSWE | — | 18.1% |
| WeirdML | 52.3% | — |
Agentic & Tool Use Not comparable
Gemma 4 31B IT: —, GLM-5.3-Flash: 34.2 (#47)
| Benchmark | Gemma 4 31B IT | GLM-5.3-Flash |
|---|---|---|
| APEX-Agents | — | 52.8% |
| GDP.pdf | — | 14% |
Reasoning GLM-5.3-Flash leads
Gemma 4 31B IT: 27.2 (#122), GLM-5.3-Flash: 48.0 (#42)
| Benchmark | Gemma 4 31B IT | GLM-5.3-Flash |
|---|---|---|
| CritPt | 1.4% | 15.4% |
| Chess Puzzles | 5% | 14% |
| LMArena Hard Prompts | 1448 | 1491 |
| Surface Evolver Bench | 30.6% | 52.5% |
| Epoch Capabilities Index | 142.74 | 151.88 |
| ARC-AGI-2 | — | 65.8% |
| Kagi LLM Benchmark | 63.5% | — |
| NYT Connections (extended) | 70.6% | — |
| ARC-AGI-1 | — | 91% |
| Thematic Generalization | 53% | — |
| Mystery Game Puzzles | — | 8% |
| DTBench | 82.7% | — |
| LMCA | 39.3% | — |
| Bench to the Future 3 | — | 0.15 |
Math GLM-5.3-Flash leads
Gemma 4 31B IT: 43.2 (#81), GLM-5.3-Flash: 53.3 (#47)
| Benchmark | Gemma 4 31B IT | GLM-5.3-Flash |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 73.3% | 93.9% |
| LMArena Math | 1465 | 1500 |
| FrontierMath (Tiers 1-3) | — | 55.8% |
| FrontierMath Tier 4 | — | 17.1% |
| ProofBench | — | 21% |
Knowledge GLM-5.3-Flash leads
Gemma 4 31B IT: 37.9 (#151), GLM-5.3-Flash: 58.4 (#36)
| Benchmark | Gemma 4 31B IT | GLM-5.3-Flash |
|---|---|---|
| GPQA Diamond | 75.8% | 90.2% |
| LMArena Expert | 1465 | 1513 |
| SimpleQA Verified | 10.4% | — |
| Vectara Hallucination Rate | 7.4% | — |
Multimodal GLM-5.3-Flash leads
Gemma 4 31B IT: 41.6 (#34), GLM-5.3-Flash: 42.8 (#27)
| Benchmark | Gemma 4 31B IT | GLM-5.3-Flash |
|---|---|---|
| LMArena Vision | 1277 | 1296 |
| LMArena Document | 1425 | — |
Multilingual GLM-5.3-Flash leads
Gemma 4 31B IT: 53.8 (#57), GLM-5.3-Flash: 56.0 (#25)
| Benchmark | Gemma 4 31B IT | GLM-5.3-Flash |
|---|---|---|
| LMArena Non-English | 1431 | 1462 |
| LMArena Chinese | 1476 | 1527 |
| LMArena French | 1435 | 1496 |
| LMArena Russian | 1460 | 1469 |
| LMArena Spanish | 1444 | 1471 |
| LMArena German | — | 1470 |
| LMArena Japanese | — | 1429 |
| LMArena Korean | — | 1446 |
Instruction Following GLM-5.3-Flash leads
Gemma 4 31B IT: 75.5 (#61), GLM-5.3-Flash: 77.5 (#20)
| Benchmark | Gemma 4 31B IT | GLM-5.3-Flash |
|---|---|---|
| LMArena Instruction Following | 1433 | 1478 |
Long Context GLM-5.3-Flash leads
Gemma 4 31B IT: 44.2 (#71), GLM-5.3-Flash: 45.4 (#39)
| Benchmark | Gemma 4 31B IT | GLM-5.3-Flash |
|---|---|---|
| LMArena Longer Query | 1446 | 1482 |
Writing & Preference GLM-5.3-Flash leads
Gemma 4 31B IT: 60.5 (#96), GLM-5.3-Flash: 65.3 (#50)
| Benchmark | Gemma 4 31B IT | GLM-5.3-Flash |
|---|---|---|
| LMArena Text | 1443 | 1471 |
| LMArena Creative Writing | 1415 | 1442 |
| LMArena Multi-Turn | 1452 | 1467 |
| EQ-Bench Creative Writing | 1368 | — |
| EQ-Bench 4 | 1120 | — |
Frequently asked questions
Is Gemma 4 31B IT better than GLM-5.3-Flash?
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 43.5 on the Noometry Index. Gemma 4 31B IT costs 1.6× 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 4 31B IT or GLM-5.3-Flash?
Gemma 4 31B IT is cheaper. It lists at $0.09 per million input tokens and $0.34 per million output tokens; GLM-5.3-Flash lists at $0.15 and $0.50.
Is Gemma 4 31B IT or GLM-5.3-Flash better for coding?
GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 42.3 in the Noometry coding category.
Which has the bigger context window?
GLM-5.3-Flash does, with 1M tokens against 262K.
How many benchmarks do Gemma 4 31B IT and GLM-5.3-Flash share?
24 benchmarks have published results for both models. Gemma 4 31B IT has 35 scored results on Noometry and GLM-5.3-Flash has 40.