Model comparison
Gemma 3 27B vs GLM-5V-Turbo
GLM-5V-Turbo is the stronger model overall, scoring 43.8 to 30.8 on the Noometry Index. Gemma 3 27B costs 19× less per token, which makes it the better buy when GLM-5V-Turbo's lead doesn't matter for your workload.
Last verified . 17 shared benchmarks.
GoogleSummary
- They share 17 benchmarks with published results for both. Gemma 3 27B scores higher in 0 categories and GLM-5V-Turbo in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in coding, where GLM-5V-Turbo leads 42.1 to 22.5.
- Gemma 3 27B is cheaper at $0.08 / $0.16 per million input/output tokens, against $1.20 / $4 for GLM-5V-Turbo.
- GLM-5V-Turbo accepts more context: 200K tokens versus 131K.
- Gemma 3 27B has downloadable open weights; the other is API-only.
Side by side
| Gemma 3 27B | GLM-5V-Turbo | |
|---|---|---|
| Provider | Z.ai (Zhipu) | |
| Noometry Index | 30.8 | 43.8 |
| Released | 2025-03-11 | 2026-04-01 |
| Weights | Open | Proprietary |
| Context window | 131K | 200K |
| Max output | 8K | 131K |
| Input $ / M tokens | $0.08 | $1.20 |
| Output $ / M tokens | $0.16 | $4 |
| Results tracked | 43 | 19 |
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Category by category
Coding GLM-5V-Turbo leads
Gemma 3 27B: 22.5 (#334), GLM-5V-Turbo: 42.1 (#111)
| Benchmark | Gemma 3 27B | GLM-5V-Turbo |
|---|---|---|
| LMArena Coding | 1322 | 1466 |
| Aider Polyglot | 4.9% | — |
| LMArena WebDev | — | 1401 |
| SciCode | 21.2% | — |
| LiveBench Coding | 39.9% | — |
Agentic & Tool Use Not comparable
Gemma 3 27B: 25.1 (#110), GLM-5V-Turbo: —
| Benchmark | Gemma 3 27B | GLM-5V-Turbo |
|---|---|---|
| Berkeley Function Calling Leaderboard | 29.5% | — |
Reasoning GLM-5V-Turbo leads
Gemma 3 27B: 16.7 (#301), GLM-5V-Turbo: 29.7 (#89)
| Benchmark | Gemma 3 27B | GLM-5V-Turbo |
|---|---|---|
| LMArena Hard Prompts | 1340 | 1443 |
| Kagi LLM Benchmark | 40.4% | — |
| CritPt | 0% | — |
| Chess Puzzles | 0% | — |
| LiveBench Reasoning | 43.8% | — |
| DTBench | 52.5% | — |
| LiveBench Data Analysis | 51.5% | — |
| LMCA | 12.3% | — |
| Epoch Capabilities Index | 130.04 | — |
| LiveBench | 50% | — |
Math GLM-5V-Turbo leads
Gemma 3 27B: 25.9 (#265), GLM-5V-Turbo: 39.4 (#106)
| Benchmark | Gemma 3 27B | GLM-5V-Turbo |
|---|---|---|
| LMArena Math | 1312 | 1441 |
| OTIS Mock AIME 2024-2025 | 22.5% | — |
| LiveBench Math | 55.4% | — |
| MATH Level 5 | 74% | — |
Knowledge GLM-5V-Turbo leads
Gemma 3 27B: 25.5 (#261), GLM-5V-Turbo: 40.6 (#117)
| Benchmark | Gemma 3 27B | GLM-5V-Turbo |
|---|---|---|
| LMArena Expert | 1304 | 1452 |
| GPQA Diamond | 47.7% | — |
| Confabulations | 40.3% | — |
| Vectara Hallucination Rate | 7.4% | — |
Multimodal GLM-5V-Turbo leads
Gemma 3 27B: 32.6 (#100), GLM-5V-Turbo: 40.9 (#42)
| Benchmark | Gemma 3 27B | GLM-5V-Turbo |
|---|---|---|
| LMArena Vision | 1164 | 1264 |
| GeoBench | 52% | — |
| LMArena Document | — | 1416 |
Multilingual GLM-5V-Turbo leads
Gemma 3 27B: 46.9 (#155), GLM-5V-Turbo: 53.0 (#73)
| Benchmark | Gemma 3 27B | GLM-5V-Turbo |
|---|---|---|
| LMArena Non-English | 1334 | 1420 |
| LMArena Chinese | 1346 | 1488 |
| LMArena French | 1368 | 1444 |
| LMArena German | 1362 | 1423 |
| LMArena Korean | 1308 | 1396 |
| LMArena Russian | 1349 | 1431 |
| LMArena Spanish | 1349 | 1450 |
| LMArena Japanese | 1287 | — |
Instruction Following GLM-5V-Turbo leads
Gemma 3 27B: 70.6 (#160), GLM-5V-Turbo: 75.0 (#80)
| Benchmark | Gemma 3 27B | GLM-5V-Turbo |
|---|---|---|
| LMArena Instruction Following | 1321 | 1423 |
| LiveBench Instruction Following | 74.9% | — |
Long Context GLM-5V-Turbo leads
Gemma 3 27B: 27.6 (#293), GLM-5V-Turbo: 44.0 (#80)
| Benchmark | Gemma 3 27B | GLM-5V-Turbo |
|---|---|---|
| LMArena Longer Query | 1333 | 1438 |
| Fiction.LiveBench | 33.3% | — |
Writing & Preference GLM-5V-Turbo leads
Gemma 3 27B: 52.5 (#168), GLM-5V-Turbo: 62.5 (#73)
| Benchmark | Gemma 3 27B | GLM-5V-Turbo |
|---|---|---|
| LMArena Text | 1358 | 1437 |
| LMArena Creative Writing | 1346 | 1416 |
| LMArena Multi-Turn | 1345 | 1432 |
| Short-Story Creative Writing | 79.9% | — |
| EQ-Bench Creative Writing | 1266 | — |
| LiveBench Language | 34.6% | — |
Frequently asked questions
Is Gemma 3 27B better than GLM-5V-Turbo?
GLM-5V-Turbo is the stronger model overall, scoring 43.8 to 30.8 on the Noometry Index. Gemma 3 27B costs 19× less per token, which makes it the better buy when GLM-5V-Turbo's lead doesn't matter for your workload.
Which is cheaper, Gemma 3 27B or GLM-5V-Turbo?
Gemma 3 27B is cheaper. It lists at $0.08 per million input tokens and $0.16 per million output tokens; GLM-5V-Turbo lists at $1.20 and $4.
Is Gemma 3 27B or GLM-5V-Turbo better for coding?
GLM-5V-Turbo scores higher on coding benchmarks: 42.1 versus 22.5 in the Noometry coding category.
Which has the bigger context window?
GLM-5V-Turbo does, with 200K tokens against 131K.
How many benchmarks do Gemma 3 27B and GLM-5V-Turbo share?
17 benchmarks have published results for both models. Gemma 3 27B has 43 scored results on Noometry and GLM-5V-Turbo has 19.