Model comparison
Grok 4.6 vs Qwen3-30B-A3B
Grok 4.6 is the stronger model overall, scoring 56.9 to 38.9 on the Noometry Index. Qwen3-30B-A3B costs 14× less per token, which makes it the better buy when Grok 4.6's lead doesn't matter for your workload.
Last verified . 26 shared benchmarks.
Alibaba (Qwen)Summary
- They share 26 benchmarks with published results for both. Grok 4.6 scores higher in 9 categories and Qwen3-30B-A3B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Grok 4.6 leads 61.4 to 22.2.
- The biggest single-benchmark swing is WeirdML: 67.3% for Grok 4.6 and 29.8% for Qwen3-30B-A3B.
- Qwen3-30B-A3B is cheaper at $0.12 / $0.50 per million input/output tokens, against $2 / $6 for Grok 4.6.
- Grok 4.6 accepts more context: 500K tokens versus 41K.
- Qwen3-30B-A3B has downloadable open weights; the other is API-only.
Side by side
| Grok 4.6 | Qwen3-30B-A3B | |
|---|---|---|
| Provider | xAI | Alibaba (Qwen) |
| Noometry Index | 56.9 | 38.9 |
| Released | 2026-08-12 | 2025-04-28 |
| Weights | Proprietary | Open |
| Context window | 500K | 41K |
| Max output | 500K | 16K |
| Input $ / M tokens | $2 | $0.12 |
| Output $ / M tokens | $6 | $0.50 |
| Results tracked | 49 | 32 |
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Category by category
Coding Grok 4.6 leads
Grok 4.6: 58.5 (#16), Qwen3-30B-A3B: 37.5 (#194)
| Benchmark | Grok 4.6 | Qwen3-30B-A3B |
|---|---|---|
| SciCode | 56.5% | 33.3% |
| WeirdML | 67.3% | 29.8% |
| LMArena Coding | 1465 | 1416 |
| DeepSWE | 67.5% | — |
| FrontierCode | 48% | — |
| CursorBench | 41.4% | — |
| LMArena WebDev | 1617 | — |
| FrontierSWE | 25.3% | — |
| ALE-Bench | 1,508 | — |
Agentic & Tool Use Grok 4.6 leads
Grok 4.6: 39.4 (#27), Qwen3-30B-A3B: 29.8 (#82)
| Benchmark | Grok 4.6 | Qwen3-30B-A3B |
|---|---|---|
| APEX-Agents | 65.3% | — |
| Berkeley Function Calling Leaderboard | — | 41.4% |
| GDP.pdf | 17.2% | — |
| Vending-Bench 2 | 9,047 | — |
Reasoning Grok 4.6 leads
Grok 4.6: 61.4 (#20), Qwen3-30B-A3B: 22.2 (#204)
| Benchmark | Grok 4.6 | Qwen3-30B-A3B |
|---|---|---|
| CritPt | 19.7% | 0.3% |
| Chess Puzzles | 40% | 8% |
| LMArena Hard Prompts | 1447 | 1398 |
| DTBench | 97.3% | 69.3% |
| LMCA | 48.5% | 22.4% |
| Epoch Capabilities Index | 156.44 | 139.63 |
| ARC-AGI-2 | 67.1% | — |
| SimpleBench | 75.9% | — |
| Kagi LLM Benchmark | — | 54.9% |
| NYT Connections (extended) | 80% | — |
| ARC-AGI-1 | 87.5% | — |
| EBR-Bench | 30.5% | — |
| Mystery Game Puzzles | 34% | — |
Math Grok 4.6 leads
Grok 4.6: 67.0 (#24), Qwen3-30B-A3B: 37.4 (#157)
| Benchmark | Grok 4.6 | Qwen3-30B-A3B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 99.2% | 70.3% |
| LMArena Math | 1423 | 1394 |
| FrontierMath (Tiers 1-3) | 66% | — |
| FrontierMath Tier 4 | 31.7% | — |
| MathArena Final-Answer Competitions | — | 47.8% |
| ProofBench | 51% | — |
Knowledge Grok 4.6 leads
Grok 4.6: 63.3 (#20), Qwen3-30B-A3B: 41.8 (#105)
| Benchmark | Grok 4.6 | Qwen3-30B-A3B |
|---|---|---|
| GPQA Diamond | 94% | 70.1% |
| LMArena Expert | 1467 | 1396 |
| SimpleQA Verified | 49.3% | — |
| Confabulations | — | 12.3% |
Multimodal Not comparable
Grok 4.6: 43.6 (#23), Qwen3-30B-A3B: —
| Benchmark | Grok 4.6 | Qwen3-30B-A3B |
|---|---|---|
| LMArena Vision | 1263 | — |
| Blueprint-Bench 2 | 33.2% | — |
| Furniture Assembly | 40% | — |
| LMArena Document | 1452 | — |
Multilingual Grok 4.6 leads
Grok 4.6: 53.0 (#74), Qwen3-30B-A3B: 49.5 (#132)
| Benchmark | Grok 4.6 | Qwen3-30B-A3B |
|---|---|---|
| LMArena Non-English | 1420 | 1372 |
| LMArena Chinese | 1480 | 1433 |
| LMArena French | 1461 | 1418 |
| LMArena German | 1431 | 1380 |
| LMArena Japanese | 1376 | 1337 |
| LMArena Korean | 1397 | 1331 |
| LMArena Russian | 1422 | 1370 |
| LMArena Spanish | 1404 | 1404 |
Instruction Following Grok 4.6 leads
Grok 4.6: 75.4 (#63), Qwen3-30B-A3B: 72.0 (#142)
| Benchmark | Grok 4.6 | Qwen3-30B-A3B |
|---|---|---|
| LMArena Instruction Following | 1431 | 1363 |
Long Context Grok 4.6 leads
Grok 4.6: 44.5 (#66), Qwen3-30B-A3B: 31.0 (#283)
| Benchmark | Grok 4.6 | Qwen3-30B-A3B |
|---|---|---|
| LMArena Longer Query | 1454 | 1379 |
| Fiction.LiveBench | — | 40.6% |
Writing & Preference Grok 4.6 leads
Grok 4.6: 62.3 (#80), Qwen3-30B-A3B: 55.6 (#143)
| Benchmark | Grok 4.6 | Qwen3-30B-A3B |
|---|---|---|
| LMArena Text | 1428 | 1384 |
| LMArena Creative Writing | 1428 | 1317 |
| LMArena Multi-Turn | 1425 | 1378 |
| Short-Story Creative Writing | — | 75.3% |
Frequently asked questions
Is Grok 4.6 better than Qwen3-30B-A3B?
Grok 4.6 is the stronger model overall, scoring 56.9 to 38.9 on the Noometry Index. Qwen3-30B-A3B costs 14× less per token, which makes it the better buy when Grok 4.6's lead doesn't matter for your workload.
Which is cheaper, Grok 4.6 or Qwen3-30B-A3B?
Qwen3-30B-A3B is cheaper. It lists at $0.12 per million input tokens and $0.50 per million output tokens; Grok 4.6 lists at $2 and $6.
Is Grok 4.6 or Qwen3-30B-A3B better for coding?
Grok 4.6 scores higher on coding benchmarks: 58.5 versus 37.5 in the Noometry coding category.
Which has the bigger context window?
Grok 4.6 does, with 500K tokens against 41K.
How many benchmarks do Grok 4.6 and Qwen3-30B-A3B share?
26 benchmarks have published results for both models. Grok 4.6 has 49 scored results on Noometry and Qwen3-30B-A3B has 32.