Model comparison
Grok 4.6 vs Qwen2.5 7B Instruct
Grok 4.6 is the stronger model overall, scoring 56.9 to 29.0 on the Noometry Index. Qwen2.5 7B Instruct costs 9.8× less per token, which makes it the better buy when Grok 4.6's lead doesn't matter for your workload.
Last verified . 6 shared benchmarks.
Alibaba (Qwen)Summary
- They share 6 benchmarks with published results for both. Grok 4.6 scores higher in 7 categories and Qwen2.5 7B Instruct in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where Grok 4.6 leads 67.0 to 12.6.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 99.2% for Grok 4.6 and 2.5% for Qwen2.5 7B Instruct.
- Qwen2.5 7B Instruct is cheaper at $0.17 / $0.70 per million input/output tokens, against $2 / $6 for Grok 4.6.
- Grok 4.6 accepts more context: 500K tokens versus 131K.
- Qwen2.5 7B Instruct has downloadable open weights; the other is API-only.
Side by side
| Grok 4.6 | Qwen2.5 7B Instruct | |
|---|---|---|
| Provider | xAI | Alibaba (Qwen) |
| Noometry Index | 56.9 | 29.0 |
| Released | 2026-08-12 | 2024-09 |
| Weights | Proprietary | Open |
| Context window | 500K | 131K |
| Max output | 500K | 8K |
| Input $ / M tokens | $2 | $0.17 |
| Output $ / M tokens | $6 | $0.70 |
| Results tracked | 49 | 15 |
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Category by category
Coding Grok 4.6 leads
Grok 4.6: 58.5 (#16), Qwen2.5 7B Instruct: 36.5 (#208)
| Benchmark | Grok 4.6 | Qwen2.5 7B Instruct |
|---|---|---|
| DeepSWE | 67.5% | — |
| FrontierCode | 48% | — |
| CursorBench | 41.4% | — |
| LMArena WebDev | 1617 | — |
| FrontierSWE | 25.3% | — |
| SciCode | 56.5% | — |
| WeirdML | 67.3% | — |
| BigCodeBench Instruct | — | 37.6% |
| LMArena Coding | 1465 | — |
| BigCodeBench Complete | — | 46.1% |
| ALE-Bench | 1,508 | — |
Agentic & Tool Use Grok 4.6 leads
Grok 4.6: 39.4 (#27), Qwen2.5 7B Instruct: 23.8 (#124)
| Benchmark | Grok 4.6 | Qwen2.5 7B Instruct |
|---|---|---|
| APEX-Agents | 65.3% | — |
| BALROG | — | 7.8% |
| GDP.pdf | 17.2% | — |
| Vending-Bench 2 | 9,047 | — |
Reasoning Grok 4.6 leads
Grok 4.6: 61.4 (#20), Qwen2.5 7B Instruct: 14.8 (#322)
| Benchmark | Grok 4.6 | Qwen2.5 7B Instruct |
|---|---|---|
| Chess Puzzles | 40% | 0% |
| DTBench | 97.3% | 47.7% |
| LMCA | 48.5% | 6.4% |
| Epoch Capabilities Index | 156.44 | 118.51 |
| ARC-AGI-2 | 67.1% | — |
| SimpleBench | 75.9% | — |
| NYT Connections (extended) | 80% | — |
| ARC-AGI-1 | 87.5% | — |
| CritPt | 19.7% | — |
| EBR-Bench | 30.5% | — |
| LMArena Hard Prompts | 1447 | — |
| Mystery Game Puzzles | 34% | — |
Math Grok 4.6 leads
Grok 4.6: 67.0 (#24), Qwen2.5 7B Instruct: 12.6 (#306)
| Benchmark | Grok 4.6 | Qwen2.5 7B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 99.2% | 2.5% |
| FrontierMath (Tiers 1-3) | 66% | — |
| FrontierMath Tier 4 | 31.7% | — |
| ProofBench | 51% | — |
| Omni-MATH | — | 29.4% |
| LMArena Math | 1423 | — |
Knowledge Grok 4.6 leads
Grok 4.6: 63.3 (#20), Qwen2.5 7B Instruct: 17.0 (#286)
| Benchmark | Grok 4.6 | Qwen2.5 7B Instruct |
|---|---|---|
| GPQA Diamond | 94% | 35.5% |
| SimpleQA Verified | 49.3% | — |
| MMLU-Pro | — | 53.9% |
| GPQA (HELM) | — | 34.1% |
| LMArena Expert | 1467 | — |
| MMLU | — | 72.9% |
Multimodal Not comparable
Grok 4.6: 43.6 (#23), Qwen2.5 7B Instruct: —
| Benchmark | Grok 4.6 | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Vision | 1263 | — |
| Blueprint-Bench 2 | 33.2% | — |
| Furniture Assembly | 40% | — |
| LMArena Document | 1452 | — |
Multilingual Not comparable
Grok 4.6: 53.0 (#74), Qwen2.5 7B Instruct: —
| Benchmark | Grok 4.6 | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Non-English | 1420 | — |
| LMArena Chinese | 1480 | — |
| LMArena French | 1461 | — |
| LMArena German | 1431 | — |
| LMArena Japanese | 1376 | — |
| LMArena Korean | 1397 | — |
| LMArena Russian | 1422 | — |
| LMArena Spanish | 1404 | — |
Instruction Following Grok 4.6 leads
Grok 4.6: 75.4 (#63), Qwen2.5 7B Instruct: 63.2 (#231)
| Benchmark | Grok 4.6 | Qwen2.5 7B Instruct |
|---|---|---|
| IFEval | — | 74.1% |
| LMArena Instruction Following | 1431 | — |
Long Context Not comparable
Grok 4.6: 44.5 (#66), Qwen2.5 7B Instruct: —
| Benchmark | Grok 4.6 | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Longer Query | 1454 | — |
Writing & Preference Grok 4.6 leads
Grok 4.6: 62.3 (#80), Qwen2.5 7B Instruct: 48.8 (#195)
| Benchmark | Grok 4.6 | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Text | 1428 | — |
| LMArena Creative Writing | 1428 | — |
| WildBench | — | 73.1% |
| LMArena Multi-Turn | 1425 | — |
Frequently asked questions
Is Grok 4.6 better than Qwen2.5 7B Instruct?
Grok 4.6 is the stronger model overall, scoring 56.9 to 29.0 on the Noometry Index. Qwen2.5 7B Instruct costs 9.8× 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 Qwen2.5 7B Instruct?
Qwen2.5 7B Instruct is cheaper. It lists at $0.17 per million input tokens and $0.70 per million output tokens; Grok 4.6 lists at $2 and $6.
Is Grok 4.6 or Qwen2.5 7B Instruct better for coding?
Grok 4.6 scores higher on coding benchmarks: 58.5 versus 36.5 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 Qwen2.5 7B Instruct share?
6 benchmarks have published results for both models. Grok 4.6 has 49 scored results on Noometry and Qwen2.5 7B Instruct has 15.