Model comparison
Grok 4.7 vs Qwen1.5-14B
Grok 4.7 is the stronger model overall, scoring 46.6 to 32.1 on the Noometry Index.
Last verified . 12 shared benchmarks.
Alibaba (Qwen)Summary
- They share 12 benchmarks with published results for both. Grok 4.7 scores higher in 8 categories and Qwen1.5-14B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where Grok 4.7 leads 70.1 to 33.7.
- Qwen1.5-14B has downloadable open weights; the other is API-only.
Side by side
| Grok 4.7 | Qwen1.5-14B | |
|---|---|---|
| Provider | xAI | Alibaba (Qwen) |
| Noometry Index | 46.6 | 32.1 |
| Released | 2026-09-21 | 2024-02-04 |
| Weights | Proprietary | Open |
| Context window | 500K | — |
| Max output | 500K | — |
| Input $ / M tokens | $2 | — |
| Output $ / M tokens | $6 | — |
| Results tracked | 21 | 17 |
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Category by category
Coding Grok 4.7 leads
Grok 4.7: 55.0 (#23), Qwen1.5-14B: 32.5 (#259)
| Benchmark | Grok 4.7 | Qwen1.5-14B |
|---|---|---|
| LMArena Coding | 1423 | 1138 |
| CursorBench | 46.3% | — |
| LMArena WebDev | 1636 | — |
| FrontierSWE | 29.5% | — |
| SciCode | 57.4% | — |
Reasoning Grok 4.7 leads
Grok 4.7: 40.1 (#49), Qwen1.5-14B: 20.5 (#217)
| Benchmark | Grok 4.7 | Qwen1.5-14B |
|---|---|---|
| LMArena Hard Prompts | 1409 | 1113 |
| NYT Connections (extended) | 76.8% | — |
| CritPt | 17.7% | — |
Math Grok 4.7 leads
Grok 4.7: 41.1 (#73), Qwen1.5-14B: 29.9 (#214)
| Benchmark | Grok 4.7 | Qwen1.5-14B |
|---|---|---|
| LMArena Math | 1393 | 1125 |
| ProofBench | 34% | — |
Knowledge Grok 4.7 leads
Grok 4.7: 38.7 (#130), Qwen1.5-14B: 29.3 (#230)
| Benchmark | Grok 4.7 | Qwen1.5-14B |
|---|---|---|
| LMArena Expert | 1413 | 1094 |
| MMLU | — | 68.6% |
Multimodal Not comparable
Grok 4.7: 28.8 (#107), Qwen1.5-14B: —
| Benchmark | Grok 4.7 | Qwen1.5-14B |
|---|---|---|
| Furniture Assembly | 20.8% | — |
Multilingual Grok 4.7 leads
Grok 4.7: 51.2 (#108), Qwen1.5-14B: 30.8 (#257)
| Benchmark | Grok 4.7 | Qwen1.5-14B |
|---|---|---|
| LMArena Non-English | 1392 | 1095 |
| LMArena Chinese | 1476 | 1147 |
| LMArena Russian | 1396 | 1046 |
| LMArena French | — | 1115 |
| LMArena German | — | 1043 |
| LMArena Japanese | — | 1019 |
| LMArena Spanish | — | 1085 |
Instruction Following Grok 4.7 leads
Grok 4.7: 74.3 (#98), Qwen1.5-14B: 57.1 (#266)
| Benchmark | Grok 4.7 | Qwen1.5-14B |
|---|---|---|
| LMArena Instruction Following | 1406 | 1102 |
Long Context Grok 4.7 leads
Grok 4.7: 43.2 (#97), Qwen1.5-14B: 33.7 (#252)
| Benchmark | Grok 4.7 | Qwen1.5-14B |
|---|---|---|
| LMArena Longer Query | 1416 | 1113 |
Writing & Preference Grok 4.7 leads
Grok 4.7: 70.1 (#23), Qwen1.5-14B: 33.7 (#270)
| Benchmark | Grok 4.7 | Qwen1.5-14B |
|---|---|---|
| LMArena Text | 1397 | 1128 |
| LMArena Creative Writing | 1391 | 1091 |
| LMArena Multi-Turn | 1389 | 1110 |
| EQ-Bench Creative Writing | 2007 | — |
Frequently asked questions
Is Grok 4.7 better than Qwen1.5-14B?
Grok 4.7 is the stronger model overall, scoring 46.6 to 32.1 on the Noometry Index.
Is Grok 4.7 or Qwen1.5-14B better for coding?
Grok 4.7 scores higher on coding benchmarks: 55.0 versus 32.5 in the Noometry coding category.
How many benchmarks do Grok 4.7 and Qwen1.5-14B share?
12 benchmarks have published results for both models. Grok 4.7 has 21 scored results on Noometry and Qwen1.5-14B has 17.