Compare models. Find the differences.
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Benchmark snapshot
1 benchmarks in common.
4 models selected. 1 benchmarks in common.
Reported scores; test settings may differ.
| Benchmark | Zhipu AI | Qwen | Google | Zhipu AI |
|---|---|---|---|---|
| Arena ELOHuman preference | 1,475 | 1,460 | 1,452 | 1,465 |
| LiveBench 1.0General reasoning | 73.2% | Not in our dataset | Not in our dataset | Not in our dataset |
| GPQA DiamondScientific knowledge | 91.2% | Not in our dataset | 84.3% | 86.2% |
| HLEAcademic reasoning | Not in our dataset | Not in our dataset | 19.5% | 31.0% |
| MMLU ProMulti-task understanding | Not in our dataset | Not in our dataset | 85.2% | 85.9% |
| SimpleBenchCommonsense reasoning | 58.8% | 63.0% | Not in our dataset | 55.1% |
| SWE-Bench VerifiedAgentic coding | Not in our dataset | Not in our dataset | Not in our dataset | 73.8% |
| LiveCodeBenchCompetitive coding | Not in our dataset | Not in our dataset | 80.0% | Not in our dataset |
| Tau-BenchAgentic tool use | Not in our dataset | Not in our dataset | Not in our dataset | 84.1% |
| Terminal-BenchAgentic terminal | Not in our dataset | Not in our dataset | Not in our dataset | 63.5% |
| ARC-AGI-2Visual reasoning | 22.8% | Not in our dataset | Not in our dataset | Not in our dataset |
Pricing & context Published USD rates per 1M tokens
| Specification | GLM-5.2 Thinking | Qwen3 Max Qwen 3.6 Max Preview | Gemma 4 31B IT Thinking | GLM-5.1 Thinking |
|---|---|---|---|---|
| Input | $0.006 | $1.027 | $0.2 | $1.4 |
| Output | $6.8 | $6.162 | $0.4 | $4.4 |
| Context window | 1.0M | 262K | 262K | 205K |
| Pricing details | Explore pricing → | Explore pricing → | Explore pricing → | Explore pricing → |
How to read this comparison
Each column keeps the exact model version you selected. Shared benchmarks are the intersection of their reported results; “all coverage” also shows where evidence is missing. Missing results never count as zero.
These are published benchmark scores, not a controlled head-to-head test. The available evidence does not establish matching evaluation settings, so we show no cross-model winner highlights or deltas. Composite quality scores are omitted because they can summarize different benchmark sets.
Scores and statistics are computed upstream. This page reads a fixed data snapshot; its date describes the snapshot, not the date of every measurement.