# DeepSeek V4.1 Flash vs Gemma 3 27B

> DeepSeek V4.1 Flash is the stronger model overall, scoring 50.3 to 30.5 on the Noometry Index. Gemma 3 27B costs 2.6× less per token, which makes it the better buy when DeepSeek V4.1 Flash's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/deepseek-v4-1-flash-vs-gemma-3-27b
- Last updated: 2026-10-10
- Shared benchmarks: 20

## Summary

- They share 20 benchmarks with published results for both. DeepSeek V4.1 Flash scores higher in 8 categories and Gemma 3 27B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in coding, where DeepSeek V4.1 Flash leads 52.8 to 22.1.
- The biggest single-benchmark swing is DTBench: 89.9% for DeepSeek V4.1 Flash and 52.5% for Gemma 3 27B.
- Gemma 3 27B is cheaper at $0.08 / $0.16 per million input/output tokens, against $0.15 / $0.60 for DeepSeek V4.1 Flash.
- DeepSeek V4.1 Flash accepts more context: 1M tokens versus 131K.

## Snapshot

| | DeepSeek V4.1 Flash | Gemma 3 27B |
|---|---|---|
| Provider | DeepSeek | Google |
| Noometry Index | 50.3 | 30.5 |
| Rank | 40 | 277 |
| Context | 1M | 131K |
| Input $/M | $0.15 | $0.08 |
| Output $/M | $0.60 | $0.16 |
| Weights | Open | Open |

## Coding

- DeepSeek V4.1 Flash: 52.8 (#29)
- Gemma 3 27B: 22.1 (#329)

| Benchmark | DeepSeek V4.1 Flash | Gemma 3 27B |
|---|---|---|
| SciCode | 51.9% | 21.2% |
| LMArena Coding | 1511 | 1322 |
| Aider Polyglot | — | 4.9% |
| LMArena WebDev | 1620 | — |
| LiveBench Coding | — | 39.9% |
| ALE-Bench | 1,092 | — |

## Agentic & Tool Use

- DeepSeek V4.1 Flash: —
- Gemma 3 27B: 24.9 (#105)

| Benchmark | DeepSeek V4.1 Flash | Gemma 3 27B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 29.5% |

## Reasoning

- DeepSeek V4.1 Flash: 46.2 (#39)
- Gemma 3 27B: 16.3 (#297)

| Benchmark | DeepSeek V4.1 Flash | Gemma 3 27B |
|---|---|---|
| CritPt | 14.3% | 0% |
| LMArena Hard Prompts | 1485 | 1340 |
| DTBench | 89.9% | 52.5% |
| LMCA | 47% | 12.3% |
| Epoch Capabilities Index | 155.01 | 130.04 |
| Kagi LLM Benchmark | — | 40.4% |
| NYT Connections (extended) | 89.6% | — |
| Chess Puzzles | — | 0% |
| LiveBench Reasoning | — | 43.8% |
| LiveBench Data Analysis | — | 51.5% |
| Surface Evolver Bench | 46.3% | — |
| LiveBench | — | 50% |

## Math

- DeepSeek V4.1 Flash: 48.4 (#52)
- Gemma 3 27B: 24.4 (#261)

| Benchmark | DeepSeek V4.1 Flash | Gemma 3 27B |
|---|---|---|
| LMArena Math | 1493 | 1312 |
| OTIS Mock AIME 2024-2025 | — | 22.5% |
| ProofBench | 54% | — |
| LiveBench Math | — | 55.4% |
| MATH Level 5 | — | 74% |

## Knowledge

- DeepSeek V4.1 Flash: 41.6 (#97)
- Gemma 3 27B: 25.4 (#256)

| Benchmark | DeepSeek V4.1 Flash | Gemma 3 27B |
|---|---|---|
| LMArena Expert | 1505 | 1303 |
| GPQA Diamond | — | 47.7% |
| Confabulations | — | 40.3% |
| Vectara Hallucination Rate | — | 7.4% |

## Multimodal

- DeepSeek V4.1 Flash: —
- Gemma 3 27B: 32.0 (#94)

| Benchmark | DeepSeek V4.1 Flash | Gemma 3 27B |
|---|---|---|
| LMArena Vision | — | 1165 |
| GeoBench | — | 52% |

## Multilingual

- DeepSeek V4.1 Flash: 55.5 (#29)
- Gemma 3 27B: 47.0 (#150)

| Benchmark | DeepSeek V4.1 Flash | Gemma 3 27B |
|---|---|---|
| LMArena Non-English | 1452 | 1334 |
| LMArena Chinese | 1506 | 1347 |
| LMArena French | 1448 | 1368 |
| LMArena Russian | 1473 | 1349 |
| LMArena Spanish | 1499 | 1348 |
| LMArena German | — | 1362 |
| LMArena Japanese | — | 1287 |
| LMArena Korean | — | 1308 |

## Instruction Following

- DeepSeek V4.1 Flash: 77.6 (#24)
- Gemma 3 27B: 70.8 (#155)

| Benchmark | DeepSeek V4.1 Flash | Gemma 3 27B |
|---|---|---|
| LMArena Instruction Following | 1475 | 1321 |
| LiveBench Instruction Following | — | 74.9% |

## Long Context

- DeepSeek V4.1 Flash: 45.2 (#46)
- Gemma 3 27B: 27.6 (#288)

| Benchmark | DeepSeek V4.1 Flash | Gemma 3 27B |
|---|---|---|
| LMArena Longer Query | 1476 | 1333 |
| Fiction.LiveBench | — | 33.3% |

## Writing & Preference

- DeepSeek V4.1 Flash: 65.8 (#43)
- Gemma 3 27B: 52.7 (#161)

| Benchmark | DeepSeek V4.1 Flash | Gemma 3 27B |
|---|---|---|
| LMArena Text | 1465 | 1358 |
| LMArena Creative Writing | 1436 | 1346 |
| EQ-Bench Creative Writing | 1540 | 1266 |
| LMArena Multi-Turn | 1460 | 1344 |
| Short-Story Creative Writing | — | 79.9% |
| LiveBench Language | — | 34.6% |

## FAQ

### Is DeepSeek V4.1 Flash better than Gemma 3 27B?

DeepSeek V4.1 Flash is the stronger model overall, scoring 50.3 to 30.5 on the Noometry Index. Gemma 3 27B costs 2.6× less per token, which makes it the better buy when DeepSeek V4.1 Flash's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek V4.1 Flash or Gemma 3 27B?

Gemma 3 27B is cheaper. It lists at $0.08 per million input tokens and $0.16 per million output tokens; DeepSeek V4.1 Flash lists at $0.15 and $0.60.

### Is DeepSeek V4.1 Flash or Gemma 3 27B better for coding?

DeepSeek V4.1 Flash scores higher on coding benchmarks: 52.8 versus 22.1 in the Noometry coding category.

### Which has the bigger context window?

DeepSeek V4.1 Flash does, with 1M tokens against 131K.

### How many benchmarks do DeepSeek V4.1 Flash and Gemma 3 27B share?

20 benchmarks have published results for both models. DeepSeek V4.1 Flash has 25 scored results on Noometry and Gemma 3 27B has 43.