Model comparison
Grok 4.6 vs Qwen3-Next 80B-A3B Instruct
Grok 4.6 is the stronger model overall, scoring 56.8 to 42.5 on the Noometry Index. Qwen3-Next 80B-A3B Instruct costs 3.4× less per token, which makes it the better buy when Grok 4.6's lead doesn't matter for your workload.
Last verified . 17 shared benchmarks.
Alibaba (Qwen)Summary
- They share 17 benchmarks with published results for both. Grok 4.6 scores higher in 8 categories and Qwen3-Next 80B-A3B Instruct in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Grok 4.6 leads 61.3 to 30.1.
- Qwen3-Next 80B-A3B Instruct is cheaper at $0.50 / $2 per million input/output tokens, against $2 / $6 for Grok 4.6.
- Grok 4.6 accepts more context: 500K tokens versus 131K.
- Qwen3-Next 80B-A3B Instruct has downloadable open weights; the other is API-only.
Side by side
| Grok 4.6 | Qwen3-Next 80B-A3B Instruct | |
|---|---|---|
| Provider | xAI | Alibaba (Qwen) |
| Noometry Index | 56.8 | 42.5 |
| Released | 2026-08-12 | 2025-09 |
| Weights | Proprietary | Open |
| Context window | 500K | 131K |
| Max output | 500K | 33K |
| Input $ / M tokens | $2 | $0.50 |
| Output $ / M tokens | $6 | $2 |
| Results tracked | 49 | 25 |
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Category by category
Coding Grok 4.6 leads
Grok 4.6: 58.0 (#14), Qwen3-Next 80B-A3B Instruct: 41.9 (#101)
| Benchmark | Grok 4.6 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Coding | 1464 | 1441 |
| DeepSWE | 67.5% | — |
| FrontierCode | 48% | — |
| CursorBench | 41.4% | — |
| LMArena WebDev | 1620 | — |
| FrontierSWE | 25.3% | — |
| SciCode | 56.5% | — |
| WeirdML | 67.3% | — |
| ALE-Bench | 1,508 | — |
Agentic & Tool Use Not comparable
Grok 4.6: 39.4 (#25), Qwen3-Next 80B-A3B Instruct: —
| Benchmark | Grok 4.6 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| APEX-Agents | 65.3% | — |
| GDP.pdf | 17.2% | — |
| Vending-Bench 2 | 9,047 | — |
Reasoning Grok 4.6 leads
Grok 4.6: 61.3 (#18), Qwen3-Next 80B-A3B Instruct: 30.1 (#79)
| Benchmark | Grok 4.6 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Hard Prompts | 1445 | 1428 |
| ARC-AGI-2 | 67.1% | — |
| SimpleBench | 75.9% | — |
| Kagi LLM Benchmark | — | 66.7% |
| NYT Connections (extended) | 80% | — |
| ARC-AGI-1 | 87.5% | — |
| CritPt | 19.7% | — |
| Chess Puzzles | 40% | — |
| EBR-Bench | 30.5% | — |
| Mystery Game Puzzles | 34% | — |
| DTBench | 97.3% | — |
| LMCA | 48.5% | — |
| Epoch Capabilities Index | 156.48 | — |
Math Grok 4.6 leads
Grok 4.6: 66.8 (#19), Qwen3-Next 80B-A3B Instruct: 36.3 (#118)
| Benchmark | Grok 4.6 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Math | 1416 | 1440 |
| FrontierMath (Tiers 1-3) | 66% | — |
| FrontierMath Tier 4 | 31.7% | — |
| OTIS Mock AIME 2024-2025 | 99.2% | — |
| ProofBench | 51% | — |
| Omni-MATH | — | 46.7% |
Knowledge Grok 4.6 leads
Grok 4.6: 63.3 (#18), Qwen3-Next 80B-A3B Instruct: 41.3 (#102)
| Benchmark | Grok 4.6 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Expert | 1463 | 1416 |
| GPQA Diamond | 94% | — |
| SimpleQA Verified | 49.3% | — |
| MMLU-Pro | — | 78.6% |
| Vectara Hallucination Rate | — | 9.3% |
| GPQA (HELM) | — | 63% |
Multimodal Not comparable
Grok 4.6: 43.9 (#18), Qwen3-Next 80B-A3B Instruct: —
| Benchmark | Grok 4.6 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Vision | 1265 | — |
| Blueprint-Bench 2 | 33.2% | — |
| Furniture Assembly | 40% | — |
| LMArena Document | 1452 | — |
Multilingual Grok 4.6 leads
Grok 4.6: 53.3 (#67), Qwen3-Next 80B-A3B Instruct: 52.3 (#88)
| Benchmark | Grok 4.6 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Non-English | 1422 | 1407 |
| LMArena Chinese | 1479 | 1461 |
| LMArena French | 1458 | 1415 |
| LMArena German | 1425 | 1416 |
| LMArena Japanese | 1383 | 1398 |
| LMArena Korean | 1395 | 1366 |
| LMArena Russian | 1427 | 1403 |
| LMArena Spanish | 1408 | 1436 |
Instruction Following Grok 4.6 leads
Grok 4.6: 75.6 (#59), Qwen3-Next 80B-A3B Instruct: 71.0 (#154)
| Benchmark | Grok 4.6 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Instruction Following | 1431 | 1388 |
| IFEval | — | 81% |
Long Context Grok 4.6 leads
Grok 4.6: 44.4 (#62), Qwen3-Next 80B-A3B Instruct: 37.0 (#218)
| Benchmark | Grok 4.6 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Longer Query | 1453 | 1403 |
| Fiction.LiveBench | — | 55.6% |
Writing & Preference Grok 4.6 leads
Grok 4.6: 62.5 (#77), Qwen3-Next 80B-A3B Instruct: 58.2 (#116)
| Benchmark | Grok 4.6 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Text | 1427 | 1417 |
| LMArena Creative Writing | 1430 | 1333 |
| LMArena Multi-Turn | 1422 | 1415 |
| WildBench | — | 80.7% |
Frequently asked questions
Is Grok 4.6 better than Qwen3-Next 80B-A3B Instruct?
Grok 4.6 is the stronger model overall, scoring 56.8 to 42.5 on the Noometry Index. Qwen3-Next 80B-A3B Instruct costs 3.4× 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-Next 80B-A3B Instruct?
Qwen3-Next 80B-A3B Instruct is cheaper. It lists at $0.50 per million input tokens and $2 per million output tokens; Grok 4.6 lists at $2 and $6.
Is Grok 4.6 or Qwen3-Next 80B-A3B Instruct better for coding?
Grok 4.6 scores higher on coding benchmarks: 58.0 versus 41.9 in the Noometry coding category.
Which has the bigger context window?
Grok 4.6 does, with 500K tokens against 131K.
How many benchmarks do Grok 4.6 and Qwen3-Next 80B-A3B Instruct share?
17 benchmarks have published results for both models. Grok 4.6 has 49 scored results on Noometry and Qwen3-Next 80B-A3B Instruct has 25.