Model comparison
Qwen2.5-Coder-32B vs Qwen3-30B-A3B
Qwen3-30B-A3B is the stronger model overall, scoring 38.9 to 33.4 on the Noometry Index.
Last verified . 13 shared benchmarks.
Alibaba (Qwen)Summary
- They share 13 benchmarks with published results for both. Qwen2.5-Coder-32B scores higher in 1 category and Qwen3-30B-A3B in 7 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in coding, where Qwen3-30B-A3B leads 37.5 to 22.6.
- Qwen3-30B-A3B is cheaper at $0.12 / $0.50 per million input/output tokens, against $0.66 / $1 for Qwen2.5-Coder-32B.
- Qwen3-30B-A3B accepts more context: 41K tokens versus 33K.
Side by side
| Qwen2.5-Coder-32B | Qwen3-30B-A3B | |
|---|---|---|
| Provider | Alibaba (Qwen) | Alibaba (Qwen) |
| Noometry Index | 33.4 | 38.9 |
| Released | 2024-09-18 | 2025-04-28 |
| Weights | Open | Open |
| Context window | 33K | 41K |
| Max output | 29K | 16K |
| Input $ / M tokens | $0.66 | $0.12 |
| Output $ / M tokens | $1 | $0.50 |
| Results tracked | 31 | 32 |
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Category by category
Coding Qwen3-30B-A3B leads
Qwen2.5-Coder-32B: 22.6 (#333), Qwen3-30B-A3B: 37.5 (#194)
| Benchmark | Qwen2.5-Coder-32B | Qwen3-30B-A3B |
|---|---|---|
| LMArena Coding | 1276 | 1416 |
| SWE-bench Verified (bash only) | 9% | — |
| Aider Polyglot | 16.4% | — |
| SciCode | — | 33.3% |
| WeirdML | — | 29.8% |
| BigCodeBench Instruct | 49% | — |
| LiveBench Coding | 56.9% | — |
| BigCodeBench Complete | 58% | — |
| HumanEval+ | 87.2% | — |
| MBPP+ | 77% | — |
Agentic & Tool Use Not comparable
Qwen2.5-Coder-32B: —, Qwen3-30B-A3B: 29.8 (#82)
| Benchmark | Qwen2.5-Coder-32B | Qwen3-30B-A3B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 41.4% |
Reasoning Too close to call
Qwen2.5-Coder-32B: 21.2 (#225), Qwen3-30B-A3B: 22.2 (#204)
| Benchmark | Qwen2.5-Coder-32B | Qwen3-30B-A3B |
|---|---|---|
| LMArena Hard Prompts | 1251 | 1398 |
| Epoch Capabilities Index | 119.49 | 139.63 |
| Kagi LLM Benchmark | — | 54.9% |
| CritPt | — | 0.3% |
| Chess Puzzles | — | 8% |
| LiveBench Reasoning | 42.1% | — |
| DTBench | — | 69.3% |
| LiveBench Data Analysis | 49.9% | — |
| LMCA | — | 22.4% |
| HellaSwag | 83% | — |
| LiveBench | 46.2% | — |
| WinoGrande | 80.8% | — |
Math Qwen3-30B-A3B leads
Qwen2.5-Coder-32B: 33.3 (#204), Qwen3-30B-A3B: 37.4 (#157)
| Benchmark | Qwen2.5-Coder-32B | Qwen3-30B-A3B |
|---|---|---|
| LMArena Math | 1251 | 1394 |
| MathArena Final-Answer Competitions | — | 47.8% |
| OTIS Mock AIME 2024-2025 | — | 70.3% |
| LiveBench Math | 46.6% | — |
| GSM8K | 93% | — |
Knowledge Qwen3-30B-A3B leads
Qwen2.5-Coder-32B: 33.4 (#203), Qwen3-30B-A3B: 41.8 (#105)
| Benchmark | Qwen2.5-Coder-32B | Qwen3-30B-A3B |
|---|---|---|
| LMArena Expert | 1221 | 1396 |
| GPQA Diamond | — | 70.1% |
| Confabulations | — | 12.3% |
| ARC (AI2) Challenge | 70.5% | — |
| MMLU | 79.1% | — |
Multilingual Qwen3-30B-A3B leads
Qwen2.5-Coder-32B: 37.8 (#235), Qwen3-30B-A3B: 49.5 (#132)
| Benchmark | Qwen2.5-Coder-32B | Qwen3-30B-A3B |
|---|---|---|
| LMArena Non-English | 1205 | 1372 |
| LMArena Chinese | 1222 | 1433 |
| LMArena Russian | 1228 | 1370 |
| LMArena French | — | 1418 |
| LMArena German | — | 1380 |
| LMArena Japanese | — | 1337 |
| LMArena Korean | — | 1331 |
| LMArena Spanish | — | 1404 |
Instruction Following Qwen3-30B-A3B leads
Qwen2.5-Coder-32B: 61.4 (#245), Qwen3-30B-A3B: 72.0 (#142)
| Benchmark | Qwen2.5-Coder-32B | Qwen3-30B-A3B |
|---|---|---|
| LMArena Instruction Following | 1223 | 1363 |
| LiveBench Instruction Following | 58.7% | — |
Long Context Qwen2.5-Coder-32B leads
Qwen2.5-Coder-32B: 38.0 (#208), Qwen3-30B-A3B: 31.0 (#283)
| Benchmark | Qwen2.5-Coder-32B | Qwen3-30B-A3B |
|---|---|---|
| LMArena Longer Query | 1251 | 1379 |
| Fiction.LiveBench | — | 40.6% |
Writing & Preference Qwen3-30B-A3B leads
Qwen2.5-Coder-32B: 41.6 (#240), Qwen3-30B-A3B: 55.6 (#143)
| Benchmark | Qwen2.5-Coder-32B | Qwen3-30B-A3B |
|---|---|---|
| LMArena Text | 1230 | 1384 |
| LMArena Creative Writing | 1174 | 1317 |
| LMArena Multi-Turn | 1222 | 1378 |
| Short-Story Creative Writing | — | 75.3% |
| LiveBench Language | 23.3% | — |
Frequently asked questions
Is Qwen2.5-Coder-32B better than Qwen3-30B-A3B?
Qwen3-30B-A3B is the stronger model overall, scoring 38.9 to 33.4 on the Noometry Index.
Which is cheaper, Qwen2.5-Coder-32B 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; Qwen2.5-Coder-32B lists at $0.66 and $1.
Is Qwen2.5-Coder-32B or Qwen3-30B-A3B better for coding?
Qwen3-30B-A3B scores higher on coding benchmarks: 37.5 versus 22.6 in the Noometry coding category.
Which has the bigger context window?
Qwen3-30B-A3B does, with 41K tokens against 33K.
How many benchmarks do Qwen2.5-Coder-32B and Qwen3-30B-A3B share?
13 benchmarks have published results for both models. Qwen2.5-Coder-32B has 31 scored results on Noometry and Qwen3-30B-A3B has 32.