# Qwen2.5 7B Instruct vs Qwen3 14B

> Qwen3 14B is the stronger model overall, scoring 35.5 to 29.0 on the Noometry Index. Qwen2.5 7B Instruct costs 2.0× less per token, which makes it the better buy when Qwen3 14B's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/qwen2-5-7b-instruct-vs-qwen3-14b
- Last updated: 2026-10-12
- Shared benchmarks: 6

## Summary

- They share 6 benchmarks with published results for both. Qwen2.5 7B Instruct scores higher in 0 categories and Qwen3 14B in 5 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3 14B leads 38.6 to 12.6.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 2.5% for Qwen2.5 7B Instruct and 66.4% for Qwen3 14B.
- Qwen2.5 7B Instruct is cheaper at $0.17 / $0.70 per million input/output tokens, against $0.35 / $1.40 for Qwen3 14B.

## Snapshot

| | Qwen2.5 7B Instruct | Qwen3 14B |
|---|---|---|
| Provider | Alibaba (Qwen) | Alibaba (Qwen) |
| Noometry Index | 29.0 | 35.5 |
| Rank | 320 | 225 |
| Context | 131K | 131K |
| Input $/M | $0.17 | $0.35 |
| Output $/M | $0.70 | $1.40 |
| Weights | Open | Open |

## Coding

- Qwen2.5 7B Instruct: 36.5 (#208)
- Qwen3 14B: 37.3 (#195)

| Benchmark | Qwen2.5 7B Instruct | Qwen3 14B |
|---|---|---|
| SciCode | — | 31.6% |
| BigCodeBench Instruct | 37.6% | — |
| BigCodeBench Complete | 46.1% | — |

## Agentic & Tool Use

- Qwen2.5 7B Instruct: 23.8 (#124)
- Qwen3 14B: 29.6 (#83)

| Benchmark | Qwen2.5 7B Instruct | Qwen3 14B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 41% |
| BALROG | 7.8% | — |

## Reasoning

- Qwen2.5 7B Instruct: 14.8 (#322)
- Qwen3 14B: 18.5 (#280)

| Benchmark | Qwen2.5 7B Instruct | Qwen3 14B |
|---|---|---|
| Chess Puzzles | 0% | 4% |
| DTBench | 47.7% | 64% |
| LMCA | 6.4% | 18.2% |
| Epoch Capabilities Index | 118.51 | 138.23 |
| Kagi LLM Benchmark | — | 49.1% |
| CritPt | — | 0% |

## Math

- Qwen2.5 7B Instruct: 12.6 (#306)
- Qwen3 14B: 38.6 (#133)

| Benchmark | Qwen2.5 7B Instruct | Qwen3 14B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 2.5% | 66.4% |
| Omni-MATH | 29.4% | — |

## Knowledge

- Qwen2.5 7B Instruct: 17.0 (#286)
- Qwen3 14B: 39.3 (#134)

| Benchmark | Qwen2.5 7B Instruct | Qwen3 14B |
|---|---|---|
| GPQA Diamond | 35.5% | 63.8% |
| MMLU-Pro | 53.9% | — |
| Vectara Hallucination Rate | — | 5.4% |
| GPQA (HELM) | 34.1% | — |
| MMLU | 72.9% | — |

## Instruction Following

- Qwen2.5 7B Instruct: 63.2 (#231)
- Qwen3 14B: —

| Benchmark | Qwen2.5 7B Instruct | Qwen3 14B |
|---|---|---|
| IFEval | 74.1% | — |

## Long Context

- Qwen2.5 7B Instruct: —
- Qwen3 14B: 38.1 (#204)

| Benchmark | Qwen2.5 7B Instruct | Qwen3 14B |
|---|---|---|
| Fiction.LiveBench | — | 62.5% |

## Writing & Preference

- Qwen2.5 7B Instruct: 48.8 (#195)
- Qwen3 14B: —

| Benchmark | Qwen2.5 7B Instruct | Qwen3 14B |
|---|---|---|
| WildBench | 73.1% | — |

## FAQ

### Is Qwen2.5 7B Instruct better than Qwen3 14B?

Qwen3 14B is the stronger model overall, scoring 35.5 to 29.0 on the Noometry Index. Qwen2.5 7B Instruct costs 2.0× less per token, which makes it the better buy when Qwen3 14B's lead doesn't matter for your workload.

### Which is cheaper, Qwen2.5 7B Instruct or Qwen3 14B?

Qwen2.5 7B Instruct is cheaper. It lists at $0.17 per million input tokens and $0.70 per million output tokens; Qwen3 14B lists at $0.35 and $1.40.

### Is Qwen2.5 7B Instruct or Qwen3 14B better for coding?

They score almost the same on coding (36.5 vs 37.3); test both on your own repository before choosing.

### Which has the bigger context window?

Both accept 131K tokens.

### How many benchmarks do Qwen2.5 7B Instruct and Qwen3 14B share?

6 benchmarks have published results for both models. Qwen2.5 7B Instruct has 15 scored results on Noometry and Qwen3 14B has 12.