Model comparison
Gemini 2.5 Pro vs GLM-4.5V
Gemini 2.5 Pro is the stronger model overall, scoring 44.9 to 39.2 on the Noometry Index. GLM-4.5V costs 3.8× less per token, which makes it the better buy when Gemini 2.5 Pro's lead doesn't matter for your workload.
Last verified . 15 shared benchmarks.
GoogleSummary
- They share 15 benchmarks with published results for both. Gemini 2.5 Pro scores higher in 8 categories and GLM-4.5V in 1 category; 9 gaps are clear of the uncertainty.
- The widest gap is in long context, where Gemini 2.5 Pro leads 59.7 to 39.6.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 70.3% for Gemini 2.5 Pro and 59.8% for GLM-4.5V.
- GLM-4.5V is cheaper at $0.60 / $1.80 per million input/output tokens, against $1.25 / $10 for Gemini 2.5 Pro.
- Gemini 2.5 Pro accepts more context: 1.05M tokens versus 64K.
- GLM-4.5V has downloadable open weights; the other is API-only.
Side by side
| Gemini 2.5 Pro | GLM-4.5V | |
|---|---|---|
| Provider | Z.ai (Zhipu) | |
| Noometry Index | 44.9 | 39.2 |
| Released | 2025-03-25 | 2025-08-11 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 64K |
| Max output | 66K | 16K |
| Input $ / M tokens | $1.25 | $0.60 |
| Output $ / M tokens | $10 | $1.80 |
| Results tracked | 78 | 15 |
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Category by category
Coding Gemini 2.5 Pro leads
Gemini 2.5 Pro: 42.8 (#86), GLM-4.5V: 38.9 (#152)
| Benchmark | Gemini 2.5 Pro | GLM-4.5V |
|---|---|---|
| LMArena Coding | 1452 | 1347 |
| SWE-bench Verified | 57.6% | — |
| SWE-bench Verified (bash only) | 53.6% | — |
| Aider Polyglot | 83.1% | — |
| LMArena WebDev | 1227 | — |
| SciCode | 42.8% | — |
| GSO | 3.9% | — |
| WeirdML | 54% | — |
| LiveBench Coding | 85.9% | — |
| CadEval | 64% | — |
| ALE-Bench | 785.52 | — |
| AlgoTune | 1.51 | — |
Agentic & Tool Use Not comparable
Gemini 2.5 Pro: 29.1 (#81), GLM-4.5V: —
| Benchmark | Gemini 2.5 Pro | GLM-4.5V |
|---|---|---|
| Terminal-Bench | 32.6% | — |
| GDPval | 23.3% | — |
| Remote Labor Index | 0.8% | — |
| TheAgentCompany | 30.3% | — |
| τ²-bench Banking | 13.7% | — |
| DeepResearch Bench | 42.8% | — |
| BALROG | 43.3% | — |
| LMArena Search | 1142 | — |
| METR Time Horizons | 55.4% | — |
| Vending-Bench 2 | 573.64 | — |
Reasoning Gemini 2.5 Pro leads
Gemini 2.5 Pro: 28.8 (#85), GLM-4.5V: 26.5 (#118)
| Benchmark | Gemini 2.5 Pro | GLM-4.5V |
|---|---|---|
| Kagi LLM Benchmark | 70.3% | 59.8% |
| LMArena Hard Prompts | 1455 | 1335 |
| ARC-AGI-2 | 4.9% | — |
| SimpleBench | 62.4% | — |
| ARC-AGI-1 | 41% | — |
| CritPt | 2% | — |
| Chess Puzzles | 20% | — |
| EnigmaEval | 5.6% | — |
| LiveBench Reasoning | 89.8% | — |
| DTBench | 82.4% | — |
| LiveBench Data Analysis | 79.9% | — |
| LMCA | 34.8% | — |
| Epoch Capabilities Index | 145.32 | — |
| ForecastBench | 61.3 | — |
| LiveBench | 82.3% | — |
Math GLM-4.5V leads
Gemini 2.5 Pro: 31.9 (#191), GLM-4.5V: 34.7 (#157)
| Benchmark | Gemini 2.5 Pro | GLM-4.5V |
|---|---|---|
| LMArena Math | 1450 | 1353 |
| FrontierMath (Tiers 1-3) | 24.6% | — |
| FrontierMath Tier 4 | 0% | — |
| OTIS Mock AIME 2024-2025 | 84.7% | — |
| Omni-MATH | 41.6% | — |
| LiveBench Math | 90.2% | — |
| MATH Level 5 | 95.9% | — |
| FrontierMath (Feb 2025 set) | 14.1% | — |
| FrontierMath Tier 4 (v1) | 4.2% | — |
Knowledge Gemini 2.5 Pro leads
Gemini 2.5 Pro: 55.8 (#41), GLM-4.5V: 36.8 (#155)
| Benchmark | Gemini 2.5 Pro | GLM-4.5V |
|---|---|---|
| LMArena Expert | 1452 | 1353 |
| GPQA Diamond | 85.3% | — |
| Humanity's Last Exam | 21.6% | — |
| MMLU-Pro | 86.3% | — |
| Confabulations | 10.6% | — |
| Vectara Hallucination Rate | 7% | — |
| GPQA (HELM) | 74.9% | — |
Multimodal Gemini 2.5 Pro leads
Gemini 2.5 Pro: 44.5 (#16), GLM-4.5V: 33.6 (#86)
| Benchmark | Gemini 2.5 Pro | GLM-4.5V |
|---|---|---|
| LMArena Vision | 1263 | 1154 |
| GeoBench | 86% | — |
| VPCT | 48% | — |
| LMArena Document | 1421 | — |
| SpatialViz-Bench | 44.7% | — |
Multilingual Gemini 2.5 Pro leads
Gemini 2.5 Pro: 55.5 (#30), GLM-4.5V: 44.9 (#172)
| Benchmark | Gemini 2.5 Pro | GLM-4.5V |
|---|---|---|
| LMArena Non-English | 1452 | 1304 |
| LMArena Chinese | 1507 | 1338 |
| LMArena Russian | 1460 | 1299 |
| LMArena Spanish | 1473 | 1335 |
| LMArena French | 1473 | — |
| LMArena German | 1487 | — |
| LMArena Japanese | 1462 | — |
| LMArena Korean | 1435 | — |
Instruction Following Gemini 2.5 Pro leads
Gemini 2.5 Pro: 75.2 (#71), GLM-4.5V: 69.4 (#170)
| Benchmark | Gemini 2.5 Pro | GLM-4.5V |
|---|---|---|
| LMArena Instruction Following | 1436 | 1311 |
| LiveBench Instruction Following | 80.6% | — |
| IFEval | 84% | — |
Long Context Gemini 2.5 Pro leads
Gemini 2.5 Pro: 59.7 (#5), GLM-4.5V: 39.6 (#166)
| Benchmark | Gemini 2.5 Pro | GLM-4.5V |
|---|---|---|
| LMArena Longer Query | 1449 | 1305 |
| Fiction.LiveBench | 91.7% | — |
Writing & Preference Gemini 2.5 Pro leads
Gemini 2.5 Pro: 63.9 (#60), GLM-4.5V: 52.7 (#162)
| Benchmark | Gemini 2.5 Pro | GLM-4.5V |
|---|---|---|
| LMArena Text | 1458 | 1333 |
| LMArena Creative Writing | 1453 | 1295 |
| LMArena Multi-Turn | 1453 | 1333 |
| Short-Story Creative Writing | 83.8% | — |
| EQ-Bench Creative Writing | 1421 | — |
| WildBench | 85.7% | — |
| LiveBench Language | 67.8% | — |
Frequently asked questions
Is Gemini 2.5 Pro better than GLM-4.5V?
Gemini 2.5 Pro is the stronger model overall, scoring 44.9 to 39.2 on the Noometry Index. GLM-4.5V costs 3.8× less per token, which makes it the better buy when Gemini 2.5 Pro's lead doesn't matter for your workload.
Which is cheaper, Gemini 2.5 Pro or GLM-4.5V?
GLM-4.5V is cheaper. It lists at $0.60 per million input tokens and $1.80 per million output tokens; Gemini 2.5 Pro lists at $1.25 and $10.
Is Gemini 2.5 Pro or GLM-4.5V better for coding?
Gemini 2.5 Pro scores higher on coding benchmarks: 42.8 versus 38.9 in the Noometry coding category.
Which has the bigger context window?
Gemini 2.5 Pro does, with 1.05M tokens against 64K.
How many benchmarks do Gemini 2.5 Pro and GLM-4.5V share?
15 benchmarks have published results for both models. Gemini 2.5 Pro has 78 scored results on Noometry and GLM-4.5V has 15.