Model comparison
Grok 4.5 vs Qwen3 235B-A22B
Grok 4.5 is the stronger model overall, scoring 55.0 to 43.2 on the Noometry Index. Qwen3 235B-A22B costs 2.4× less per token, which makes it the better buy when Grok 4.5's lead doesn't matter for your workload.
Last verified . 33 shared benchmarks.
Alibaba (Qwen)Summary
- They share 33 benchmarks with published results for both. Grok 4.5 scores higher in 8 categories and Qwen3 235B-A22B in 1 category; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Grok 4.5 leads 56.0 to 15.8.
- The biggest single-benchmark swing is ARC-AGI-1: 87.2% for Grok 4.5 and 11% for Qwen3 235B-A22B.
- Qwen3 235B-A22B is cheaper at $0.70 / $2.80 per million input/output tokens, against $2 / $6 for Grok 4.5.
- Grok 4.5 accepts more context: 500K tokens versus 131K.
- Qwen3 235B-A22B has downloadable open weights; the other is API-only.
Side by side
| Grok 4.5 | Qwen3 235B-A22B | |
|---|---|---|
| Provider | xAI | Alibaba (Qwen) |
| Noometry Index | 55.0 | 43.2 |
| Released | 2026-07-08 | 2025-04 |
| Weights | Proprietary | Open |
| Context window | 500K | 131K |
| Max output | 500K | 16K |
| Input $ / M tokens | $2 | $0.70 |
| Output $ / M tokens | $6 | $2.80 |
| Results tracked | 52 | 49 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Grok 4.5 leads
Grok 4.5: 51.7 (#34), Qwen3 235B-A22B: 43.8 (#72)
| Benchmark | Grok 4.5 | Qwen3 235B-A22B |
|---|---|---|
| SciCode | 54.1% | 42.4% |
| WeirdML | 46.4% | 41% |
| LMArena Coding | 1473 | 1445 |
| DeepSWE | 53.8% | — |
| FrontierCode | 42.4% | — |
| Aider Polyglot | — | 59.6% |
| LMArena WebDev | 1552 | — |
| ALE-Bench | 1,309 | — |
Agentic & Tool Use Grok 4.5 leads
Grok 4.5: 44.4 (#15), Qwen3 235B-A22B: 33.7 (#48)
| Benchmark | Grok 4.5 | Qwen3 235B-A22B |
|---|---|---|
| Vending-Bench 2 | 3,887 | -11.34 |
| APEX-Agents | 56.2% | — |
| Berkeley Function Calling Leaderboard | — | 52.1% |
| τ²-bench Banking | 47.9% | — |
| PostTrainBench | 23.4% | — |
| GBAEval | 65.4% | — |
| GDP.pdf | 14% | — |
| LMArena Search | 1213 | — |
Reasoning Grok 4.5 leads
Grok 4.5: 56.0 (#23), Qwen3 235B-A22B: 15.8 (#302)
| Benchmark | Grok 4.5 | Qwen3 235B-A22B |
|---|---|---|
| ARC-AGI-2 | 52.6% | 1.3% |
| SimpleBench | 70% | 31% |
| Kagi LLM Benchmark | 83.5% | 69.4% |
| ARC-AGI-1 | 87.2% | 11% |
| CritPt | 15.4% | 0% |
| Chess Puzzles | 36% | 12% |
| LMArena Hard Prompts | 1462 | 1433 |
| DTBench | 96.5% | 80.3% |
| LMCA | 45.2% | 29.3% |
| Epoch Capabilities Index | 154.02 | 143.87 |
| NYT Connections (extended) | 79.9% | — |
| Mystery Game Puzzles | — | 9% |
| Surface Evolver Bench | 74.4% | — |
| ForecastBench | — | 59.7 |
Math Grok 4.5 leads
Grok 4.5: 60.7 (#29), Qwen3 235B-A22B: 48.5 (#51)
| Benchmark | Grok 4.5 | Qwen3 235B-A22B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 97.8% | 86.7% |
| LMArena Math | 1457 | 1431 |
| FrontierMath (Tiers 1-3) | 57.2% | — |
| FrontierMath Tier 4 | 24.4% | — |
| ProofBench | 31% | — |
| Omni-MATH | — | 71.8% |
| MATH Level 5 | — | 68.9% |
| FrontierMath (Feb 2025 set) | — | 8.5% |
| FrontierMath Tier 4 (v1) | — | 0% |
Knowledge Grok 4.5 leads
Grok 4.5: 62.3 (#21), Qwen3 235B-A22B: 49.5 (#67)
| Benchmark | Grok 4.5 | Qwen3 235B-A22B |
|---|---|---|
| GPQA Diamond | 93.4% | 80.1% |
| SimpleQA Verified | 48.3% | 40.4% |
| LMArena Expert | 1466 | 1460 |
| MMLU-Pro | — | 84.4% |
| Confabulations | — | 15.6% |
| Vectara Hallucination Rate | — | 9.3% |
| GPQA (HELM) | — | 72.7% |
Multimodal Not comparable
Grok 4.5: 37.8 (#63), Qwen3 235B-A22B: —
| Benchmark | Grok 4.5 | Qwen3 235B-A22B |
|---|---|---|
| LMArena Vision | 1287 | — |
| Blueprint-Bench 2 | 27.3% | — |
| Furniture Assembly | 22.5% | — |
| LMArena Document | 1452 | — |
Multilingual Grok 4.5 leads
Grok 4.5: 54.5 (#43), Qwen3 235B-A22B: 52.5 (#84)
| Benchmark | Grok 4.5 | Qwen3 235B-A22B |
|---|---|---|
| LMArena Non-English | 1439 | 1410 |
| LMArena Chinese | 1497 | 1481 |
| LMArena French | 1458 | 1445 |
| LMArena German | 1448 | 1432 |
| LMArena Japanese | 1425 | 1400 |
| LMArena Korean | 1402 | 1391 |
| LMArena Russian | 1447 | 1411 |
| LMArena Spanish | 1452 | 1429 |
Instruction Following Grok 4.5 leads
Grok 4.5: 76.3 (#45), Qwen3 235B-A22B: 72.8 (#132)
| Benchmark | Grok 4.5 | Qwen3 235B-A22B |
|---|---|---|
| LMArena Instruction Following | 1446 | 1408 |
| IFEval | — | 83.5% |
Long Context Qwen3 235B-A22B leads
Grok 4.5: 44.8 (#54), Qwen3 235B-A22B: 46.1 (#25)
| Benchmark | Grok 4.5 | Qwen3 235B-A22B |
|---|---|---|
| LMArena Longer Query | 1463 | 1426 |
| Fiction.LiveBench | — | 75% |
Writing & Preference Grok 4.5 leads
Grok 4.5: 65.9 (#40), Qwen3 235B-A22B: 59.8 (#103)
| Benchmark | Grok 4.5 | Qwen3 235B-A22B |
|---|---|---|
| LMArena Text | 1448 | 1419 |
| LMArena Creative Writing | 1442 | 1384 |
| EQ-Bench Creative Writing | 1579 | 1366 |
| LMArena Multi-Turn | 1457 | 1432 |
| Short-Story Creative Writing | — | 83% |
| WildBench | — | 86.6% |
Frequently asked questions
Is Grok 4.5 better than Qwen3 235B-A22B?
Grok 4.5 is the stronger model overall, scoring 55.0 to 43.2 on the Noometry Index. Qwen3 235B-A22B costs 2.4× 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 235B-A22B?
Qwen3 235B-A22B is cheaper. It lists at $0.70 per million input tokens and $2.80 per million output tokens; Grok 4.5 lists at $2 and $6.
Is Grok 4.5 or Qwen3 235B-A22B better for coding?
Grok 4.5 scores higher on coding benchmarks: 51.7 versus 43.8 in the Noometry coding category.
Which has the bigger context window?
Grok 4.5 does, with 500K tokens against 131K.
How many benchmarks do Grok 4.5 and Qwen3 235B-A22B share?
33 benchmarks have published results for both models. Grok 4.5 has 52 scored results on Noometry and Qwen3 235B-A22B has 49.