Model comparison
Qwen2.5 32B Instruct vs Qwen3 14B
Qwen3 14B is the stronger model overall, scoring 35.0 to 29.7 on the Noometry Index.
Last verified . 4 shared benchmarks.
Alibaba (Qwen)Summary
- They share 4 benchmarks with published results for both. Qwen2.5 32B Instruct scores higher in 2 categories and Qwen3 14B in 2 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3 14B leads 37.0 to 15.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 7.4% for Qwen2.5 32B Instruct and 66.4% for Qwen3 14B.
- Qwen3 14B is cheaper at $0.35 / $1.40 per million input/output tokens, against $0.70 / $2.80 for Qwen2.5 32B Instruct.
Side by side
| Qwen2.5 32B Instruct | Qwen3 14B | |
|---|---|---|
| Provider | Alibaba (Qwen) | Alibaba (Qwen) |
| Noometry Index | 29.7 | 35.0 |
| Released | 2024-09 | 2025-04 |
| Weights | Open | Open |
| Context window | 131K | 131K |
| Max output | 8K | 8K |
| Input $ / M tokens | $0.70 | $0.35 |
| Output $ / M tokens | $2.80 | $1.40 |
| Results tracked | 7 | 12 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Qwen2.5 32B Instruct leads
Qwen2.5 32B Instruct: 38.1 (#165), Qwen3 14B: 36.7 (#191)
| Benchmark | Qwen2.5 32B Instruct | Qwen3 14B |
|---|---|---|
| SciCode | — | 31.6% |
| BigCodeBench Instruct | 45% | — |
| BigCodeBench Complete | 52.3% | — |
Agentic & Tool Use Not comparable
Qwen2.5 32B Instruct: —, Qwen3 14B: 29.4 (#78)
| Benchmark | Qwen2.5 32B Instruct | Qwen3 14B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 41% |
Reasoning Too close to call
Qwen2.5 32B Instruct: 18.5 (#262), Qwen3 14B: 18.1 (#272)
| Benchmark | Qwen2.5 32B Instruct | Qwen3 14B |
|---|---|---|
| Chess Puzzles | 0% | 4% |
| Epoch Capabilities Index | 128.52 | 138.23 |
| Kagi LLM Benchmark | — | 49.1% |
| CritPt | — | 0% |
| DTBench | — | 64% |
| LMCA | — | 18.2% |
Math Qwen3 14B leads
Qwen2.5 32B Instruct: 15.3 (#289), Qwen3 14B: 37.0 (#102)
| Benchmark | Qwen2.5 32B Instruct | Qwen3 14B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 7.4% | 66.4% |
| MATH Level 5 | 56.1% | — |
Knowledge Qwen3 14B leads
Qwen2.5 32B Instruct: 24.9 (#259), Qwen3 14B: 39.2 (#124)
| Benchmark | Qwen2.5 32B Instruct | Qwen3 14B |
|---|---|---|
| GPQA Diamond | 46.1% | 63.8% |
| Vectara Hallucination Rate | — | 5.4% |
Long Context Not comparable
Qwen2.5 32B Instruct: —, Qwen3 14B: 38.0 (#201)
| Benchmark | Qwen2.5 32B Instruct | Qwen3 14B |
|---|---|---|
| Fiction.LiveBench | — | 62.5% |
Frequently asked questions
Is Qwen2.5 32B Instruct better than Qwen3 14B?
Qwen3 14B is the stronger model overall, scoring 35.0 to 29.7 on the Noometry Index.
Which is cheaper, Qwen2.5 32B Instruct or Qwen3 14B?
Qwen3 14B is cheaper. It lists at $0.35 per million input tokens and $1.40 per million output tokens; Qwen2.5 32B Instruct lists at $0.70 and $2.80.
Is Qwen2.5 32B Instruct or Qwen3 14B better for coding?
Qwen2.5 32B Instruct scores higher on coding benchmarks: 38.1 versus 36.7 in the Noometry coding category.
Which has the bigger context window?
Both accept 131K tokens.
How many benchmarks do Qwen2.5 32B Instruct and Qwen3 14B share?
4 benchmarks have published results for both models. Qwen2.5 32B Instruct has 7 scored results on Noometry and Qwen3 14B has 12.