Model comparison
Gemini 2.5 Flash vs GLM-5V-Turbo
GLM-5V-Turbo is the stronger model overall, scoring 43.8 to 39.3 on the Noometry Index. Gemini 2.5 Flash costs 2.2× 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. Gemini 2.5 Flash scores higher in 4 categories and GLM-5V-Turbo in 5 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5V-Turbo leads 29.7 to 18.1.
- Gemini 2.5 Flash is cheaper at $0.30 / $2.50 per million input/output tokens, against $1.20 / $4 for GLM-5V-Turbo.
- Gemini 2.5 Flash accepts more context: 1.05M tokens versus 200K.
Side by side
| Gemini 2.5 Flash | GLM-5V-Turbo | |
|---|---|---|
| Provider | Z.ai (Zhipu) | |
| Noometry Index | 39.3 | 43.8 |
| Released | 2025-04-17 | 2026-04-01 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | 200K |
| Max output | 66K | 131K |
| Input $ / M tokens | $0.30 | $1.20 |
| Output $ / M tokens | $2.50 | $4 |
| Results tracked | 54 | 19 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding GLM-5V-Turbo leads
Gemini 2.5 Flash: 35.8 (#220), GLM-5V-Turbo: 42.1 (#111)
| Benchmark | Gemini 2.5 Flash | GLM-5V-Turbo |
|---|---|---|
| LMArena Coding | 1424 | 1466 |
| SWE-bench Verified (bash only) | 28.7% | — |
| Aider Polyglot | 55.1% | — |
| LMArena WebDev | — | 1401 |
| WeirdML | 41.9% | — |
| ALE-Bench | 661.88 | — |
Agentic & Tool Use Not comparable
Gemini 2.5 Flash: 30.8 (#74), GLM-5V-Turbo: —
| Benchmark | Gemini 2.5 Flash | GLM-5V-Turbo |
|---|---|---|
| Terminal-Bench | 17.1% | — |
| Berkeley Function Calling Leaderboard | 56.2% | — |
| TheAgentCompany | 41.1% | — |
| BALROG | 33.5% | — |
| Vending-Bench 2 | 548.84 | — |
Reasoning GLM-5V-Turbo leads
Gemini 2.5 Flash: 18.1 (#286), GLM-5V-Turbo: 29.7 (#89)
| Benchmark | Gemini 2.5 Flash | GLM-5V-Turbo |
|---|---|---|
| LMArena Hard Prompts | 1422 | 1443 |
| ARC-AGI-2 | 2.5% | — |
| SimpleBench | 41.2% | — |
| Kagi LLM Benchmark | 56.8% | — |
| ARC-AGI-1 | 33.3% | — |
| CritPt | 1.1% | — |
| EnigmaEval | 2.7% | — |
| DTBench | 76.5% | — |
| LMCA | 27.5% | — |
| Epoch Capabilities Index | 143.03 | — |
| ForecastBench | 60.6 | — |
Math Too close to call
Gemini 2.5 Flash: 39.9 (#98), GLM-5V-Turbo: 39.4 (#106)
| Benchmark | Gemini 2.5 Flash | GLM-5V-Turbo |
|---|---|---|
| LMArena Math | 1415 | 1441 |
| OTIS Mock AIME 2024-2025 | 73.1% | — |
| Omni-MATH | 38.5% | — |
| FrontierMath (Feb 2025 set) | 4.8% | — |
| FrontierMath Tier 4 (v1) | 4.2% | — |
Knowledge GLM-5V-Turbo leads
Gemini 2.5 Flash: 36.4 (#168), GLM-5V-Turbo: 40.6 (#117)
| Benchmark | Gemini 2.5 Flash | GLM-5V-Turbo |
|---|---|---|
| LMArena Expert | 1426 | 1452 |
| Humanity's Last Exam | 12.1% | — |
| MMLU-Pro | 63.9% | — |
| Confabulations | 16.8% | — |
| Vectara Hallucination Rate | 7.8% | — |
| GPQA (HELM) | 39% | — |
Multimodal Too close to call
Gemini 2.5 Flash: 41.8 (#32), GLM-5V-Turbo: 40.9 (#42)
| Benchmark | Gemini 2.5 Flash | GLM-5V-Turbo |
|---|---|---|
| LMArena Vision | 1253 | 1264 |
| GeoBench | 76% | — |
| VPCT | 46.2% | — |
| LMArena Document | — | 1416 |
| SpatialViz-Bench | 36.9% | — |
Multilingual Too close to call
Gemini 2.5 Flash: 52.3 (#88), GLM-5V-Turbo: 53.0 (#73)
| Benchmark | Gemini 2.5 Flash | GLM-5V-Turbo |
|---|---|---|
| LMArena Non-English | 1409 | 1420 |
| LMArena Chinese | 1450 | 1488 |
| LMArena French | 1433 | 1444 |
| LMArena German | 1418 | 1423 |
| LMArena Korean | 1385 | 1396 |
| LMArena Russian | 1415 | 1431 |
| LMArena Spanish | 1421 | 1450 |
| LMArena Japanese | 1405 | — |
Instruction Following Too close to call
Gemini 2.5 Flash: 75.7 (#54), GLM-5V-Turbo: 75.0 (#80)
| Benchmark | Gemini 2.5 Flash | GLM-5V-Turbo |
|---|---|---|
| LMArena Instruction Following | 1405 | 1423 |
| IFEval | 89.8% | — |
Long Context Gemini 2.5 Flash leads
Gemini 2.5 Flash: 47.5 (#17), GLM-5V-Turbo: 44.0 (#80)
| Benchmark | Gemini 2.5 Flash | GLM-5V-Turbo |
|---|---|---|
| LMArena Longer Query | 1419 | 1438 |
| Fiction.LiveBench | 77.8% | — |
Writing & Preference GLM-5V-Turbo leads
Gemini 2.5 Flash: 53.8 (#157), GLM-5V-Turbo: 62.5 (#73)
| Benchmark | Gemini 2.5 Flash | GLM-5V-Turbo |
|---|---|---|
| LMArena Text | 1417 | 1437 |
| LMArena Creative Writing | 1400 | 1416 |
| LMArena Multi-Turn | 1408 | 1432 |
| Short-Story Creative Writing | 76.5% | — |
| EQ-Bench Creative Writing | 1137 | — |
| WildBench | 81.7% | — |
Frequently asked questions
Is Gemini 2.5 Flash better than GLM-5V-Turbo?
GLM-5V-Turbo is the stronger model overall, scoring 43.8 to 39.3 on the Noometry Index. Gemini 2.5 Flash costs 2.2× less per token, which makes it the better buy when GLM-5V-Turbo's lead doesn't matter for your workload.
Which is cheaper, Gemini 2.5 Flash or GLM-5V-Turbo?
Gemini 2.5 Flash is cheaper. It lists at $0.30 per million input tokens and $2.50 per million output tokens; GLM-5V-Turbo lists at $1.20 and $4.
Is Gemini 2.5 Flash or GLM-5V-Turbo better for coding?
GLM-5V-Turbo scores higher on coding benchmarks: 42.1 versus 35.8 in the Noometry coding category.
Which has the bigger context window?
Gemini 2.5 Flash does, with 1.05M tokens against 200K.
How many benchmarks do Gemini 2.5 Flash and GLM-5V-Turbo share?
17 benchmarks have published results for both models. Gemini 2.5 Flash has 54 scored results on Noometry and GLM-5V-Turbo has 19.