Compare models. Find the differences.
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Benchmark snapshot
0 benchmarks in common.
4 models selected. 0 benchmarks in common.
Reported scores; test settings may differ.
| Benchmark | Mistral AI | OpenAI | Zhipu AI | Qwen |
|---|---|---|---|---|
| Arena ELOHuman preference | 1,305 | 1,364 | 1,373 | Not in our dataset |
| LiveBench 1.0General reasoning | Not in our dataset | 70.0% | Not in our dataset | Not in our dataset |
| GPQA DiamondScientific knowledge | 70.8% | 77.0% | Not in our dataset | 73.4% |
| HLEAcademic reasoning | Not in our dataset | 13.4% | 10.6% | 9.8% |
| MMLU ProMulti-task understanding | Not in our dataset | Not in our dataset | 81.4% | 80.9% |
| SimpleBenchCommonsense reasoning | Not in our dataset | 22.8% | Not in our dataset | Not in our dataset |
| AIME 2025Mathematics | 64.9% | 86.5% | Not in our dataset | 85.0% |
| GSOGraduate science | Not in our dataset | 1.3% | 2.9% | Not in our dataset |
| AiderCode editing | 47.1% | 60.4% | Not in our dataset | Not in our dataset |
| SWE-Bench VerifiedAgentic coding | Not in our dataset | 49.3% | 57.6% | 22.0% |
| LiveCodeBenchCompetitive coding | 59.4% | 67.4% | Not in our dataset | Not in our dataset |
| Tau-BenchAgentic tool use | Not in our dataset | 57.6% | 77.9% | Not in our dataset |
| ARC-AGI-2Visual reasoning | 0.0% | 3.0% | Not in our dataset | Not in our dataset |
Pricing & context Published USD rates per 1M tokens
| Specification | MistralAI Magistral Medium Thinking | o3 Mini o3-mini | GLM-4.5 Air Thinking | 3 30B A3B Thinking (2507) |
|---|---|---|---|---|
| Input | $2 | $1.1 | $0.13 | $0.12 |
| Output | $5 | $4.4 | $0.85 | $0.5 |
| Context window | 41K | 200K | 131K | 41K |
| 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.