Model comparison
Grok 4.5 vs Qwen3.5 122B-A10B
Grok 4.5 is the stronger model overall, scoring 55.0 to 42.1 on the Noometry Index. Qwen3.5 122B-A10B costs 2.7× less per token, which makes it the better buy when Grok 4.5's lead doesn't matter for your workload.
Last verified . 24 shared benchmarks.
Alibaba (Qwen)Summary
- They share 24 benchmarks with published results for both. Grok 4.5 scores higher in 8 categories and Qwen3.5 122B-A10B in 1 category; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Grok 4.5 leads 56.1 to 27.2.
- The biggest single-benchmark swing is NYT Connections (extended): 79.9% for Grok 4.5 and 51.7% for Qwen3.5 122B-A10B.
- Qwen3.5 122B-A10B is cheaper at $0.40 / $3.20 per million input/output tokens, against $2 / $6 for Grok 4.5.
- Grok 4.5 accepts more context: 500K tokens versus 262K.
- Qwen3.5 122B-A10B has downloadable open weights; the other is API-only.
Side by side
| Grok 4.5 | Qwen3.5 122B-A10B | |
|---|---|---|
| Provider | xAI | Alibaba (Qwen) |
| Noometry Index | 55.0 | 42.1 |
| Released | 2026-07-08 | 2026-02-23 |
| Weights | Proprietary | Open |
| Context window | 500K | 262K |
| Max output | 500K | 66K |
| Input $ / M tokens | $2 | $0.40 |
| Output $ / M tokens | $6 | $3.20 |
| Results tracked | 52 | 27 |
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Category by category
Coding Grok 4.5 leads
Grok 4.5: 52.2 (#35), Qwen3.5 122B-A10B: 39.1 (#162)
| Benchmark | Grok 4.5 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena WebDev | 1553 | 1360 |
| SciCode | 54.1% | 35.6% |
| LMArena Coding | 1474 | 1436 |
| DeepSWE | 53.8% | — |
| FrontierCode | 42.4% | — |
| WeirdML | 46.4% | — |
| ALE-Bench | 1,309 | — |
Agentic & Tool Use Not comparable
Grok 4.5: 44.4 (#17), Qwen3.5 122B-A10B: —
| Benchmark | Grok 4.5 | Qwen3.5 122B-A10B |
|---|---|---|
| APEX-Agents | 56.2% | — |
| τ²-bench Banking | 47.9% | — |
| PostTrainBench | 23.4% | — |
| GBAEval | 65.4% | — |
| GDP.pdf | 14% | — |
| LMArena Search | 1213 | — |
| Vending-Bench 2 | 3,887 | — |
Reasoning Grok 4.5 leads
Grok 4.5: 56.1 (#25), Qwen3.5 122B-A10B: 27.2 (#123)
| Benchmark | Grok 4.5 | Qwen3.5 122B-A10B |
|---|---|---|
| NYT Connections (extended) | 79.9% | 51.7% |
| CritPt | 15.4% | 0.9% |
| LMArena Hard Prompts | 1462 | 1421 |
| DTBench | 96.5% | 84.3% |
| LMCA | 45.2% | 32.2% |
| ARC-AGI-2 | 52.6% | — |
| SimpleBench | 70% | — |
| Kagi LLM Benchmark | 83.5% | — |
| ARC-AGI-1 | 87.2% | — |
| Chess Puzzles | 36% | — |
| Thematic Generalization | — | 51.2% |
| Mystery Game Puzzles | — | 17% |
| Surface Evolver Bench | 74.4% | — |
| Epoch Capabilities Index | 153.92 | — |
Math Grok 4.5 leads
Grok 4.5: 60.9 (#35), Qwen3.5 122B-A10B: 39.1 (#112)
| Benchmark | Grok 4.5 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Math | 1459 | 1432 |
| FrontierMath (Tiers 1-3) | 57.2% | — |
| FrontierMath Tier 4 | 24.4% | — |
| OTIS Mock AIME 2024-2025 | 97.8% | — |
| ProofBench | 31% | — |
Knowledge Grok 4.5 leads
Grok 4.5: 62.3 (#24), Qwen3.5 122B-A10B: 38.8 (#142)
| Benchmark | Grok 4.5 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Expert | 1466 | 1432 |
| GPQA Diamond | 93.4% | — |
| SimpleQA Verified | 48.3% | — |
| Vectara Hallucination Rate | — | 11.2% |
Multimodal Qwen3.5 122B-A10B leads
Grok 4.5: 37.6 (#72), Qwen3.5 122B-A10B: 39.6 (#57)
| Benchmark | Grok 4.5 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Vision | 1288 | 1245 |
| Blueprint-Bench 2 | 27.3% | — |
| Furniture Assembly | 22.5% | — |
| LMArena Document | 1452 | — |
Multilingual Grok 4.5 leads
Grok 4.5: 54.4 (#42), Qwen3.5 122B-A10B: 51.6 (#107)
| Benchmark | Grok 4.5 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Non-English | 1440 | 1400 |
| LMArena Chinese | 1496 | 1462 |
| LMArena French | 1456 | 1442 |
| LMArena German | 1446 | 1426 |
| LMArena Japanese | 1428 | 1367 |
| LMArena Korean | 1404 | 1352 |
| LMArena Russian | 1448 | 1400 |
| LMArena Spanish | 1450 | 1424 |
Instruction Following Grok 4.5 leads
Grok 4.5: 76.0 (#48), Qwen3.5 122B-A10B: 73.8 (#115)
| Benchmark | Grok 4.5 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Instruction Following | 1446 | 1399 |
Long Context Grok 4.5 leads
Grok 4.5: 44.8 (#56), Qwen3.5 122B-A10B: 43.0 (#109)
| Benchmark | Grok 4.5 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Longer Query | 1463 | 1410 |
Writing & Preference Grok 4.5 leads
Grok 4.5: 65.8 (#42), Qwen3.5 122B-A10B: 60.0 (#105)
| Benchmark | Grok 4.5 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Text | 1448 | 1417 |
| LMArena Creative Writing | 1442 | 1368 |
| LMArena Multi-Turn | 1456 | 1416 |
| EQ-Bench Creative Writing | 1579 | — |
Frequently asked questions
Is Grok 4.5 better than Qwen3.5 122B-A10B?
Grok 4.5 is the stronger model overall, scoring 55.0 to 42.1 on the Noometry Index. Qwen3.5 122B-A10B costs 2.7× less per token, which makes it the better buy when Grok 4.5's lead doesn't matter for your workload.
Which is cheaper, Grok 4.5 or Qwen3.5 122B-A10B?
Qwen3.5 122B-A10B is cheaper. It lists at $0.40 per million input tokens and $3.20 per million output tokens; Grok 4.5 lists at $2 and $6.
Is Grok 4.5 or Qwen3.5 122B-A10B better for coding?
Grok 4.5 scores higher on coding benchmarks: 52.2 versus 39.1 in the Noometry coding category.
Which has the bigger context window?
Grok 4.5 does, with 500K tokens against 262K.
How many benchmarks do Grok 4.5 and Qwen3.5 122B-A10B share?
24 benchmarks have published results for both models. Grok 4.5 has 52 scored results on Noometry and Qwen3.5 122B-A10B has 27.