Model comparison
Grok 4 vs Qwen2.5-Coder-32B
Grok 4 is the stronger model overall, scoring 48.1 to 33.4 on the Noometry Index.
Last verified . 14 shared benchmarks.
Alibaba (Qwen)Summary
- They share 14 benchmarks with published results for both. Grok 4 scores higher in 8 categories and Qwen2.5-Coder-32B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in coding, where Grok 4 leads 50.3 to 22.6.
- The biggest single-benchmark swing is Aider Polyglot: 79.6% for Grok 4 and 16.4% for Qwen2.5-Coder-32B.
- Qwen2.5-Coder-32B has downloadable open weights; the other is API-only.
Side by side
| Grok 4 | Qwen2.5-Coder-32B | |
|---|---|---|
| Provider | xAI | Alibaba (Qwen) |
| Noometry Index | 48.1 | 33.4 |
| Released | 2025-07-09 | 2024-09-18 |
| Weights | Proprietary | Open |
| Context window | — | 33K |
| Max output | — | 29K |
| Input $ / M tokens | — | $0.66 |
| Output $ / M tokens | — | $1 |
| Results tracked | 48 | 31 |
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Category by category
Coding Grok 4 leads
Grok 4: 50.3 (#46), Qwen2.5-Coder-32B: 22.6 (#333)
| Benchmark | Grok 4 | Qwen2.5-Coder-32B |
|---|---|---|
| Aider Polyglot | 79.6% | 16.4% |
| LMArena Coding | 1408 | 1276 |
| SWE-bench Verified (bash only) | — | 9% |
| WeirdML | 45.7% | — |
| BigCodeBench Instruct | — | 49% |
| LiveBench Coding | — | 56.9% |
| BigCodeBench Complete | — | 58% |
| HumanEval+ | — | 87.2% |
| MBPP+ | — | 77% |
Agentic & Tool Use Not comparable
Grok 4: 32.3 (#68), Qwen2.5-Coder-32B: —
| Benchmark | Grok 4 | Qwen2.5-Coder-32B |
|---|---|---|
| Terminal-Bench | 27.2% | — |
| Berkeley Function Calling Leaderboard | 63% | — |
| GDPval | 21.1% | — |
| Cybench | 43% | — |
| DeepResearch Bench | 47.3% | — |
| BALROG | 43.6% | — |
| LMArena Search | 1142 | — |
| METR Time Horizons | 66.6% | — |
Reasoning Grok 4 leads
Grok 4: 36.7 (#65), Qwen2.5-Coder-32B: 21.2 (#225)
| Benchmark | Grok 4 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Hard Prompts | 1409 | 1251 |
| Epoch Capabilities Index | 146.44 | 119.49 |
| ARC-AGI-2 | 16% | — |
| SimpleBench | 60.5% | — |
| Kagi LLM Benchmark | 73.6% | — |
| ARC-AGI-1 | 66.7% | — |
| Chess Puzzles | 28% | — |
| LiveBench Reasoning | — | 42.1% |
| LiveBench Data Analysis | — | 49.9% |
| ForecastBench | 60.9 | — |
| HellaSwag | — | 83% |
| LiveBench | — | 46.2% |
| WinoGrande | — | 80.8% |
Math Grok 4 leads
Grok 4: 48.4 (#64), Qwen2.5-Coder-32B: 33.3 (#204)
| Benchmark | Grok 4 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Math | 1422 | 1251 |
| OTIS Mock AIME 2024-2025 | 84% | — |
| Omni-MATH | 60.3% | — |
| LiveBench Math | — | 46.6% |
| FrontierMath (Feb 2025 set) | 19.7% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
| GSM8K | — | 93% |
Knowledge Grok 4 leads
Grok 4: 53.8 (#55), Qwen2.5-Coder-32B: 33.4 (#203)
| Benchmark | Grok 4 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Expert | 1415 | 1221 |
| GPQA Diamond | 87% | — |
| MMLU-Pro | 85.1% | — |
| Confabulations | 12.4% | — |
| GPQA (HELM) | 72.7% | — |
| ARC (AI2) Challenge | — | 70.5% |
| MMLU | — | 79.1% |
Multimodal Not comparable
Grok 4: 33.7 (#94), Qwen2.5-Coder-32B: —
| Benchmark | Grok 4 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Vision | 1210 | — |
| GeoBench | 45% | — |
Multilingual Grok 4 leads
Grok 4: 51.8 (#103), Qwen2.5-Coder-32B: 37.8 (#235)
| Benchmark | Grok 4 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Non-English | 1403 | 1205 |
| LMArena Chinese | 1427 | 1222 |
| LMArena Russian | 1410 | 1228 |
| LMArena French | 1418 | — |
| LMArena German | 1429 | — |
| LMArena Japanese | 1394 | — |
| LMArena Korean | 1377 | — |
| LMArena Spanish | 1420 | — |
Instruction Following Grok 4 leads
Grok 4: 79.2 (#5), Qwen2.5-Coder-32B: 61.4 (#245)
| Benchmark | Grok 4 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Instruction Following | 1387 | 1223 |
| LiveBench Instruction Following | — | 58.7% |
| IFEval | 94.9% | — |
Long Context Grok 4 leads
Grok 4: 63.1 (#4), Qwen2.5-Coder-32B: 38.0 (#208)
| Benchmark | Grok 4 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Longer Query | 1409 | 1251 |
| Fiction.LiveBench | 94.4% | — |
Writing & Preference Grok 4 leads
Grok 4: 58.5 (#116), Qwen2.5-Coder-32B: 41.6 (#240)
| Benchmark | Grok 4 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Text | 1411 | 1230 |
| LMArena Creative Writing | 1397 | 1174 |
| LMArena Multi-Turn | 1416 | 1222 |
| Short-Story Creative Writing | 76.9% | — |
| WildBench | 79.7% | — |
| LiveBench Language | — | 23.3% |
Frequently asked questions
Is Grok 4 better than Qwen2.5-Coder-32B?
Grok 4 is the stronger model overall, scoring 48.1 to 33.4 on the Noometry Index.
Is Grok 4 or Qwen2.5-Coder-32B better for coding?
Grok 4 scores higher on coding benchmarks: 50.3 versus 22.6 in the Noometry coding category.
How many benchmarks do Grok 4 and Qwen2.5-Coder-32B share?
14 benchmarks have published results for both models. Grok 4 has 48 scored results on Noometry and Qwen2.5-Coder-32B has 31.