# Gemini 2.5 Flash-Lite vs Qwen2.5 7B Instruct

> Gemini 2.5 Flash-Lite is the stronger model overall, scoring 36.4 to 28.6 on the Noometry Index.

- Canonical page: https://noometry.com/compare/gemini-2-5-flash-lite-vs-qwen2-5-7b-instruct
- Last updated: 2026-10-10
- Shared benchmarks: 8

## Summary

- They share 8 benchmarks with published results for both. Gemini 2.5 Flash-Lite scores higher in 7 categories and Qwen2.5 7B Instruct in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where Gemini 2.5 Flash-Lite leads 35.5 to 11.8.
- The biggest single-benchmark swing is Omni-MATH: 48% for Gemini 2.5 Flash-Lite and 29.4% for Qwen2.5 7B Instruct.
- Gemini 2.5 Flash-Lite is cheaper at $0.10 / $0.40 per million input/output tokens, against $0.17 / $0.70 for Qwen2.5 7B Instruct.
- Gemini 2.5 Flash-Lite accepts more context: 1.05M tokens versus 131K.
- Qwen2.5 7B Instruct has downloadable open weights; the other is API-only.

## Snapshot

| | Gemini 2.5 Flash-Lite | Qwen2.5 7B Instruct |
|---|---|---|
| Provider | Google | Alibaba (Qwen) |
| Noometry Index | 36.4 | 28.6 |
| Rank | 208 | 314 |
| Context | 1.05M | 131K |
| Input $/M | $0.10 | $0.17 |
| Output $/M | $0.40 | $0.70 |
| Weights | Proprietary | Open |

## Coding

- Gemini 2.5 Flash-Lite: 38.0 (#167)
- Qwen2.5 7B Instruct: 35.9 (#204)

| Benchmark | Gemini 2.5 Flash-Lite | Qwen2.5 7B Instruct |
|---|---|---|
| WeirdML | 35.2% | — |
| BigCodeBench Instruct | — | 37.6% |
| LMArena Coding | 1373 | — |
| BigCodeBench Complete | — | 46.1% |
| ALE-Bench | 325.9 | — |

## Agentic & Tool Use

- Gemini 2.5 Flash-Lite: 27.8 (#89)
- Qwen2.5 7B Instruct: 23.5 (#117)

| Benchmark | Gemini 2.5 Flash-Lite | Qwen2.5 7B Instruct |
|---|---|---|
| Berkeley Function Calling Leaderboard | 36.9% | — |
| BALROG | — | 7.8% |

## Reasoning

- Gemini 2.5 Flash-Lite: 21.5 (#196)
- Qwen2.5 7B Instruct: 14.3 (#317)

| Benchmark | Gemini 2.5 Flash-Lite | Qwen2.5 7B Instruct |
|---|---|---|
| DTBench | 62.8% | 47.7% |
| LMCA | 18.1% | 6.4% |
| Epoch Capabilities Index | 133.93 | 118.44 |
| Kagi LLM Benchmark | 40.5% | — |
| Chess Puzzles | — | 0% |
| LMArena Hard Prompts | 1377 | — |

## Math

- Gemini 2.5 Flash-Lite: 35.5 (#144)
- Qwen2.5 7B Instruct: 11.8 (#300)

| Benchmark | Gemini 2.5 Flash-Lite | Qwen2.5 7B Instruct |
|---|---|---|
| Omni-MATH | 48% | 29.4% |
| OTIS Mock AIME 2024-2025 | — | 2.5% |
| LMArena Math | 1373 | — |

## Knowledge

- Gemini 2.5 Flash-Lite: 32.1 (#206)
- Qwen2.5 7B Instruct: 17.0 (#281)

| Benchmark | Gemini 2.5 Flash-Lite | Qwen2.5 7B Instruct |
|---|---|---|
| MMLU-Pro | 53.7% | 53.9% |
| GPQA (HELM) | 30.9% | 34.1% |
| GPQA Diamond | — | 35.5% |
| Vectara Hallucination Rate | 3.3% | — |
| LMArena Expert | 1373 | — |
| MMLU | — | 72.9% |

## Multimodal

- Gemini 2.5 Flash-Lite: 28.6 (#109)
- Qwen2.5 7B Instruct: —

| Benchmark | Gemini 2.5 Flash-Lite | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Vision | 1198 | — |
| VPCT | 30% | — |

## Multilingual

- Gemini 2.5 Flash-Lite: 49.5 (#129)
- Qwen2.5 7B Instruct: —

| Benchmark | Gemini 2.5 Flash-Lite | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Non-English | 1369 | — |
| LMArena Chinese | 1404 | — |
| LMArena French | 1388 | — |
| LMArena German | 1390 | — |
| LMArena Japanese | 1359 | — |
| LMArena Korean | 1358 | — |
| LMArena Russian | 1373 | — |
| LMArena Spanish | 1396 | — |

## Instruction Following

- Gemini 2.5 Flash-Lite: 70.2 (#163)
- Qwen2.5 7B Instruct: 63.4 (#226)

| Benchmark | Gemini 2.5 Flash-Lite | Qwen2.5 7B Instruct |
|---|---|---|
| IFEval | 81% | 74.1% |
| LMArena Instruction Following | 1366 | — |

## Long Context

- Gemini 2.5 Flash-Lite: 33.3 (#257)
- Qwen2.5 7B Instruct: —

| Benchmark | Gemini 2.5 Flash-Lite | Qwen2.5 7B Instruct |
|---|---|---|
| Fiction.LiveBench | 47.2% | — |
| LMArena Longer Query | 1373 | — |

## Writing & Preference

- Gemini 2.5 Flash-Lite: 57.0 (#130)
- Qwen2.5 7B Instruct: 48.8 (#194)

| Benchmark | Gemini 2.5 Flash-Lite | Qwen2.5 7B Instruct |
|---|---|---|
| WildBench | 81.8% | 73.1% |
| LMArena Text | 1379 | — |
| LMArena Creative Writing | 1366 | — |
| LMArena Multi-Turn | 1367 | — |

## FAQ

### Is Gemini 2.5 Flash-Lite better than Qwen2.5 7B Instruct?

Gemini 2.5 Flash-Lite is the stronger model overall, scoring 36.4 to 28.6 on the Noometry Index.

### Which is cheaper, Gemini 2.5 Flash-Lite or Qwen2.5 7B Instruct?

Gemini 2.5 Flash-Lite is cheaper. It lists at $0.10 per million input tokens and $0.40 per million output tokens; Qwen2.5 7B Instruct lists at $0.17 and $0.70.

### Is Gemini 2.5 Flash-Lite or Qwen2.5 7B Instruct better for coding?

Gemini 2.5 Flash-Lite scores higher on coding benchmarks: 38.0 versus 35.9 in the Noometry coding category.

### Which has the bigger context window?

Gemini 2.5 Flash-Lite does, with 1.05M tokens against 131K.

### How many benchmarks do Gemini 2.5 Flash-Lite and Qwen2.5 7B Instruct share?

8 benchmarks have published results for both models. Gemini 2.5 Flash-Lite has 33 scored results on Noometry and Qwen2.5 7B Instruct has 15.