Model comparison
Gemini 2.5 Pro vs GLM-5.3
GLM-5.3 is the stronger model overall, scoring 54.8 to 45.0 on the Noometry Index.
Last verified . 32 shared benchmarks.
GoogleSummary
- They share 32 benchmarks with published results for both. Gemini 2.5 Pro scores higher in 1 category and GLM-5.3 in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.3 leads 62.3 to 32.5.
- The biggest single-benchmark swing is FrontierMath (Tiers 1-3): 24.6% for Gemini 2.5 Pro and 68.8% for GLM-5.3.
- GLM-5.3 is cheaper at $1.40 / $4.40 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 1M.
- GLM-5.3 has downloadable open weights; the other is API-only.
Side by side
| Gemini 2.5 Pro | GLM-5.3 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | |
| Noometry Index | 45.0 | 54.8 |
| Released | 2025-03-25 | 2026-08-14 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 1M |
| Max output | 66K | 131K |
| Input $ / M tokens | $1.25 | $1.40 |
| Output $ / M tokens | $10 | $4.40 |
| Results tracked | 78 | 42 |
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Category by category
Coding GLM-5.3 leads
Gemini 2.5 Pro: 42.4 (#101), GLM-5.3: 59.5 (#14)
| Benchmark | Gemini 2.5 Pro | GLM-5.3 |
|---|---|---|
| LMArena WebDev | 1227 | 1622 |
| SciCode | 42.8% | 59% |
| WeirdML | 54% | 75.4% |
| LMArena Coding | 1452 | 1496 |
| ALE-Bench | 785.52 | 1,317 |
| SWE-bench Verified | 57.6% | — |
| DeepSWE | — | 69% |
| FrontierCode | — | 40.1% |
| SWE-bench Verified (bash only) | 53.6% | — |
| Aider Polyglot | 83.1% | — |
| CursorBench | — | 42.6% |
| FrontierSWE | — | 30.2% |
| GSO | 3.9% | — |
| LiveBench Coding | 85.9% | — |
| CadEval | 64% | — |
| AlgoTune | 1.51 | — |
Agentic & Tool Use GLM-5.3 leads
Gemini 2.5 Pro: 29.2 (#88), GLM-5.3: 36.4 (#38)
| Benchmark | Gemini 2.5 Pro | GLM-5.3 |
|---|---|---|
| Vending-Bench 2 | 573.64 | 8,164 |
| Terminal-Bench | 32.6% | — |
| APEX-Agents | — | 56.6% |
| 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% | — |
Reasoning GLM-5.3 leads
Gemini 2.5 Pro: 28.8 (#99), GLM-5.3: 46.1 (#46)
| Benchmark | Gemini 2.5 Pro | GLM-5.3 |
|---|---|---|
| CritPt | 2% | 19.1% |
| Chess Puzzles | 20% | 21% |
| LMArena Hard Prompts | 1455 | 1489 |
| DTBench | 82.4% | 87.7% |
| LMCA | 34.8% | 55.5% |
| Epoch Capabilities Index | 145.32 | 155.61 |
| ARC-AGI-2 | 4.9% | — |
| SimpleBench | 62.4% | — |
| Kagi LLM Benchmark | 70.3% | — |
| NYT Connections (extended) | — | 74.2% |
| ARC-AGI-1 | 41% | — |
| EnigmaEval | 5.6% | — |
| LiveBench Reasoning | 89.8% | — |
| Mystery Game Puzzles | — | 33% |
| LiveBench Data Analysis | 79.9% | — |
| Bench to the Future 3 | — | 0.15 |
| ForecastBench | 61.3 | — |
| LiveBench | 82.3% | — |
Math GLM-5.3 leads
Gemini 2.5 Pro: 32.5 (#213), GLM-5.3: 62.3 (#33)
| Benchmark | Gemini 2.5 Pro | GLM-5.3 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 24.6% | 68.8% |
| FrontierMath Tier 4 | 0% | 29.3% |
| OTIS Mock AIME 2024-2025 | 84.7% | 91.1% |
| LMArena Math | 1450 | 1489 |
| ProofBench | — | 49% |
| 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 GLM-5.3 leads
Gemini 2.5 Pro: 56.0 (#46), GLM-5.3: 58.3 (#37)
| Benchmark | Gemini 2.5 Pro | GLM-5.3 |
|---|---|---|
| GPQA Diamond | 85.3% | 90.9% |
| LMArena Expert | 1452 | 1516 |
| Humanity's Last Exam | 21.6% | — |
| SimpleQA Verified | — | 41% |
| MMLU-Pro | 86.3% | — |
| Confabulations | 10.6% | — |
| Vectara Hallucination Rate | 7% | — |
| GPQA (HELM) | 74.9% | — |
Multimodal Not comparable
Gemini 2.5 Pro: 45.2 (#18), GLM-5.3: —
| Benchmark | Gemini 2.5 Pro | GLM-5.3 |
|---|---|---|
| LMArena Vision | 1263 | — |
| GeoBench | 86% | — |
| VPCT | 48% | — |
| LMArena Document | 1421 | — |
| SpatialViz-Bench | 44.7% | — |
Multilingual Too close to call
Gemini 2.5 Pro: 55.3 (#31), GLM-5.3: 55.7 (#28)
| Benchmark | Gemini 2.5 Pro | GLM-5.3 |
|---|---|---|
| LMArena Non-English | 1451 | 1457 |
| LMArena Chinese | 1507 | 1528 |
| LMArena French | 1472 | 1499 |
| LMArena German | 1487 | 1499 |
| LMArena Japanese | 1461 | 1453 |
| LMArena Korean | 1434 | 1472 |
| LMArena Russian | 1461 | 1463 |
| LMArena Spanish | 1473 | 1460 |
Instruction Following GLM-5.3 leads
Gemini 2.5 Pro: 75.0 (#75), GLM-5.3: 77.5 (#23)
| Benchmark | Gemini 2.5 Pro | GLM-5.3 |
|---|---|---|
| LMArena Instruction Following | 1437 | 1477 |
| LiveBench Instruction Following | 80.6% | — |
| IFEval | 84% | — |
Long Context Gemini 2.5 Pro leads
Gemini 2.5 Pro: 59.8 (#5), GLM-5.3: 45.4 (#41)
| Benchmark | Gemini 2.5 Pro | GLM-5.3 |
|---|---|---|
| LMArena Longer Query | 1449 | 1482 |
| Fiction.LiveBench | 91.7% | — |
Writing & Preference GLM-5.3 leads
Gemini 2.5 Pro: 63.7 (#62), GLM-5.3: 75.7 (#6)
| Benchmark | Gemini 2.5 Pro | GLM-5.3 |
|---|---|---|
| LMArena Text | 1458 | 1471 |
| LMArena Creative Writing | 1454 | 1457 |
| EQ-Bench Creative Writing | 1421 | 2075 |
| LMArena Multi-Turn | 1453 | 1472 |
| Short-Story Creative Writing | 83.8% | — |
| WildBench | 85.7% | — |
| LiveBench Language | 67.8% | — |
Frequently asked questions
Is Gemini 2.5 Pro better than GLM-5.3?
GLM-5.3 is the stronger model overall, scoring 54.8 to 45.0 on the Noometry Index.
Which is cheaper, Gemini 2.5 Pro or GLM-5.3?
GLM-5.3 is cheaper. It lists at $1.40 per million input tokens and $4.40 per million output tokens; Gemini 2.5 Pro lists at $1.25 and $10.
Is Gemini 2.5 Pro or GLM-5.3 better for coding?
GLM-5.3 scores higher on coding benchmarks: 59.5 versus 42.4 in the Noometry coding category.
Which has the bigger context window?
Gemini 2.5 Pro does, with 1.05M tokens against 1M.
How many benchmarks do Gemini 2.5 Pro and GLM-5.3 share?
32 benchmarks have published results for both models. Gemini 2.5 Pro has 78 scored results on Noometry and GLM-5.3 has 42.