Model comparison
Grok 4.7 vs Qwen2.5-Coder-32B
Grok 4.7 is the stronger model overall, scoring 53.1 to 33.4 on the Noometry Index. Qwen2.5-Coder-32B costs 4.0× less per token, which makes it the better buy when Grok 4.7's lead doesn't matter for your workload.
Last verified . 13 shared benchmarks.
Alibaba (Qwen)Summary
- They share 13 benchmarks with published results for both. Grok 4.7 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 4.7 leads 58.0 to 22.6.
- Qwen2.5-Coder-32B is cheaper at $0.66 / $1 per million input/output tokens, against $2 / $6 for Grok 4.7.
- Grok 4.7 accepts more context: 500K tokens versus 33K.
- Qwen2.5-Coder-32B has downloadable open weights; the other is API-only.
Side by side
| Grok 4.7 | Qwen2.5-Coder-32B | |
|---|---|---|
| Provider | xAI | Alibaba (Qwen) |
| Noometry Index | 53.1 | 33.4 |
| Released | 2026-09-21 | 2024-09-18 |
| Weights | Proprietary | Open |
| Context window | 500K | 33K |
| Max output | 500K | 29K |
| Input $ / M tokens | $2 | $0.66 |
| Output $ / M tokens | $6 | $1 |
| Results tracked | 39 | 31 |
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Category by category
Coding Grok 4.7 leads
Grok 4.7: 58.0 (#18), Qwen2.5-Coder-32B: 22.6 (#333)
| Benchmark | Grok 4.7 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Coding | 1427 | 1276 |
| FrontierCode | 47.6% | — |
| SWE-bench Verified (bash only) | — | 9% |
| Aider Polyglot | — | 16.4% |
| CursorBench | 46.3% | — |
| LMArena WebDev | 1639 | — |
| FrontierSWE | 29.5% | — |
| SciCode | 57.8% | — |
| BigCodeBench Instruct | — | 49% |
| LiveBench Coding | — | 56.9% |
| BigCodeBench Complete | — | 58% |
| HumanEval+ | — | 87.2% |
| MBPP+ | — | 77% |
Agentic & Tool Use Not comparable
Grok 4.7: 36.7 (#37), Qwen2.5-Coder-32B: —
| Benchmark | Grok 4.7 | Qwen2.5-Coder-32B |
|---|---|---|
| APEX-Agents | 54.6% | — |
| GDP.pdf | 22.8% | — |
| Vending-Bench 2 | 10,537 | — |
Reasoning Grok 4.7 leads
Grok 4.7: 49.1 (#40), Qwen2.5-Coder-32B: 21.2 (#225)
| Benchmark | Grok 4.7 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Hard Prompts | 1413 | 1251 |
| Epoch Capabilities Index | 153.53 | 119.49 |
| NYT Connections (extended) | 76.8% | — |
| CritPt | 18% | — |
| Chess Puzzles | 38% | — |
| LiveBench Reasoning | — | 42.1% |
| Mystery Game Puzzles | 29% | — |
| DTBench | 96% | — |
| LiveBench Data Analysis | — | 49.9% |
| LMCA | 49.4% | — |
| HellaSwag | — | 83% |
| LiveBench | — | 46.2% |
| WinoGrande | — | 80.8% |
Math Grok 4.7 leads
Grok 4.7: 57.8 (#39), Qwen2.5-Coder-32B: 33.3 (#204)
| Benchmark | Grok 4.7 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Math | 1407 | 1251 |
| FrontierMath (Tiers 1-3) | 53% | — |
| FrontierMath Tier 4 | 17.1% | — |
| OTIS Mock AIME 2024-2025 | 98.1% | — |
| ProofBench | 34% | — |
| LiveBench Math | — | 46.6% |
| GSM8K | — | 93% |
Knowledge Grok 4.7 leads
Grok 4.7: 62.8 (#22), Qwen2.5-Coder-32B: 33.4 (#203)
| Benchmark | Grok 4.7 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Expert | 1422 | 1221 |
| GPQA Diamond | 92.7% | — |
| SimpleQA Verified | 56% | — |
| ARC (AI2) Challenge | — | 70.5% |
| MMLU | — | 79.1% |
Multimodal Not comparable
Grok 4.7: 35.5 (#87), Qwen2.5-Coder-32B: —
| Benchmark | Grok 4.7 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Vision | 1228 | — |
| Blueprint-Bench 2 | 32.5% | — |
| Furniture Assembly | 20.8% | — |
Multilingual Grok 4.7 leads
Grok 4.7: 50.8 (#116), Qwen2.5-Coder-32B: 37.8 (#235)
| Benchmark | Grok 4.7 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Non-English | 1389 | 1205 |
| LMArena Chinese | 1455 | 1222 |
| LMArena Russian | 1397 | 1228 |
| LMArena French | 1455 | — |
| LMArena Spanish | 1400 | — |
Instruction Following Grok 4.7 leads
Grok 4.7: 74.1 (#105), Qwen2.5-Coder-32B: 61.4 (#245)
| Benchmark | Grok 4.7 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Instruction Following | 1404 | 1223 |
| LiveBench Instruction Following | — | 58.7% |
Long Context Grok 4.7 leads
Grok 4.7: 43.1 (#104), Qwen2.5-Coder-32B: 38.0 (#208)
| Benchmark | Grok 4.7 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Longer Query | 1413 | 1251 |
Writing & Preference Grok 4.7 leads
Grok 4.7: 70.0 (#24), Qwen2.5-Coder-32B: 41.6 (#240)
| Benchmark | Grok 4.7 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Text | 1399 | 1230 |
| LMArena Creative Writing | 1391 | 1174 |
| LMArena Multi-Turn | 1393 | 1222 |
| EQ-Bench Creative Writing | 2007 | — |
| LiveBench Language | — | 23.3% |
Frequently asked questions
Is Grok 4.7 better than Qwen2.5-Coder-32B?
Grok 4.7 is the stronger model overall, scoring 53.1 to 33.4 on the Noometry Index. Qwen2.5-Coder-32B costs 4.0× less per token, which makes it the better buy when Grok 4.7's lead doesn't matter for your workload.
Which is cheaper, Grok 4.7 or Qwen2.5-Coder-32B?
Qwen2.5-Coder-32B is cheaper. It lists at $0.66 per million input tokens and $1 per million output tokens; Grok 4.7 lists at $2 and $6.
Is Grok 4.7 or Qwen2.5-Coder-32B better for coding?
Grok 4.7 scores higher on coding benchmarks: 58.0 versus 22.6 in the Noometry coding category.
Which has the bigger context window?
Grok 4.7 does, with 500K tokens against 33K.
How many benchmarks do Grok 4.7 and Qwen2.5-Coder-32B share?
13 benchmarks have published results for both models. Grok 4.7 has 39 scored results on Noometry and Qwen2.5-Coder-32B has 31.