DeepSeek V4.1 Flash vs Gemma 3 27B
DeepSeek V4.1 Flash leads Gemma 3 27B on the Artificial Analysis Intelligence Index, 24.7 to 4.9, and Gemma 3 27B costs 2.6x less per output token ($0.30 vs $0.78 per million). Updated October 21, 2026; 45 benchmarks have a result for both.
vs boards won of 45 shared
DeepSeek V4.1 Flash or Gemma 3 27B: which to pick
Only DeepSeek V4.1 Flash has a coding score, 62.5 on the Vibe Coding Index; Gemma 3 27B has none, so the two cannot be ranked on coding.
Pick Gemma 3 27B for output tokens 2.6x cheaper ($0.30 vs $0.78 per million).
Model ADeepSeek V4.1 FlashDeepSeek62.5/1005 independent benchmarksNo cost per solved taskModel BGemma 3 27BGoogleNot benchmarkedNo coding benchmarkNo cost per solved task
- Shared results
- 45
- DeepSeek V4.1 Flash only
- 56
- Gemma 3 27B only
- 21
- Like-for-like categories
- 6 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Only DeepSeek V4.1 Flash has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash leads, 24.7 against 4.9.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Gemma 3 27B has a AA Agentic Index result, 0.1.
Directional only
Web design
Design Arena: websites
No call
Only DeepSeek V4.1 Flash has a Design Arena: websites result, 1309.
Directional only
Long documents
Listed context window
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash takes 1.05M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Gemma 3 27B
DeepSeek V4.1 Flash $0.0006, Gemma 3 27B $0.0002 per turn.
List prices
At a glance
- Coding index
- DeepSeek V4.1 Flash 62.5 from 5 benchmarks, Gemma 3 27B not benchmarked.
- Widest category
- Math, 43.2 points apart on average over 3 shared benchmarks.
- Widest single gap
- Long context reasoning (AA-LCR): DeepSeek V4.1 Flash 84.0% against Gemma 3 27B 7.3%, 76.7 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4.1 Flash $0.78 against Gemma 3 27B $0.30 per million output tokens, 2.6x apart.
- Context window
- DeepSeek V4.1 Flash takes 1.05M tokens, Gemma 3 27B 131K.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | DeepSeek V4.1 Flash | Bars | Gemma 3 27B | Ties |
|---|---|---|---|---|
| Coding | 3 | 0 | ||
| Intelligence | 2 | 0 | ||
| Reasoning | 7 | 0 | ||
| Math | 3 | 0 | ||
| Knowledge | 4 | 0 | ||
| General | 26 | 0 | ||
| All shared boards | 45 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
| Benchmark | DeepSeek V4.1 Flash | Gemma 3 27B | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 4.9% | Only Gemma 3 27B measured |
| LiveBench coding | 90.2% | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena WebDev | 1618 | Not measured | Only DeepSeek V4.1 Flash measured |
| AA Coding Index | Not measured | 10.1 | Only Gemma 3 27B measured |
| Terminal-Bench 4 (Vals AI) | 19.7% | Not measured | Only DeepSeek V4.1 Flash measured |
| Vibe Code Bench (Vals AI) | 84.7% | Not measured | Only DeepSeek V4.1 Flash measured |
| Code migration (Vals AI) | 45.6% | Not measured | Only DeepSeek V4.1 Flash measured |
| ProgramBench (Vals AI) | 0.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| IOI (Vals AI) | 40.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| Terminal-Bench 2.1 (Vals AI) | 74.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| SWE-bench Multimodal (Vals AI) | 46.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| Vibe Code Bench 1-100 (Vals AI) | 16.4% | Not measured | Only DeepSeek V4.1 Flash measured |
| SkillsBench (Vals AI) | 69.8% | Not measured | Only DeepSeek V4.1 Flash measured |
| CyberBench (Vals AI) | 73.7% | Not measured | Only DeepSeek V4.1 Flash measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 13.7% | Only Gemma 3 27B measured |
| SciCode | 51.9% | 23.3% | DeepSeek V4.1 Flash |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 3.8% | Only Gemma 3 27B measured |
| FrontierCode | 43.9% | Not measured | Only DeepSeek V4.1 Flash measured |
| ALE-Bench | 1092.3 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: coding | 1502 | 1322 | DeepSeek V4.1 Flash |
| LMArena Text: software and IT | 1496 | 1354 | DeepSeek V4.1 Flash |
| LMArena WebDev: React | 1628 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena WebDev: HTML | 1612 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena WebDev: from an image | 1606 | Not measured | Only DeepSeek V4.1 Flash measured |
| SciCode | Not measured | 0.0% | Only Gemma 3 27B measured |
| Neither model has a result on the other 14 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V4.1 Flash | Gemma 3 27B | Leader |
|---|---|---|---|
| AA Intelligence Index | 24.7 | 4.9 | DeepSeek V4.1 Flash |
| Epoch Capabilities Index | 155.9 | 130.0 | DeepSeek V4.1 Flash |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index15 boards, none shared
| Benchmark | DeepSeek V4.1 Flash | Gemma 3 27B | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 0.1 | Only Gemma 3 27B measured |
| LiveBench research | 86.8% | Not measured | Only DeepSeek V4.1 Flash measured |
| LiveBench browser | 84.0% | Not measured | Only DeepSeek V4.1 Flash measured |
| LiveBench long agentic | 40.1% | Not measured | Only DeepSeek V4.1 Flash measured |
| SREBench (Vals AI) | 0.8% | Not measured | Only DeepSeek V4.1 Flash measured |
| Finance Agent (Vals AI) | 53.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| Tax Agent Bench (Vals AI) | 26.6% | Not measured | Only DeepSeek V4.1 Flash measured |
| Legal Research Bench (Vals AI) | 41.4% | Not measured | Only DeepSeek V4.1 Flash measured |
| Excel Modeling Benchmark (Vals AI) | 57.2% | Not measured | Only DeepSeek V4.1 Flash measured |
| BioMysteryBench (Vals AI) | 67.8% | Not measured | Only DeepSeek V4.1 Flash measured |
| Terminal-Bench-Science (Vals AI) | 0.0% | Not measured | Only DeepSeek V4.1 Flash measured |
| Vals Science Index | 29.6% | Not measured | Only DeepSeek V4.1 Flash measured |
| τ²-Bench Telecom | Not measured | 10.5% | Only Gemma 3 27B measured |
| APEX-Agents | 39.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 29.5% | Only Gemma 3 27B measured |
| Neither model has a result on the other 26 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index70
| Benchmark | DeepSeek V4.1 Flash | Gemma 3 27B | Leader |
|---|---|---|---|
| LiveBench reasoning | 86.7% | Not measured | Only DeepSeek V4.1 Flash measured |
| MysteryMechanism (Vals AI) | 21.2% | Not measured | Only DeepSeek V4.1 Flash measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 42.8% | Only Gemma 3 27B measured |
| Humanity's Last Exam (Artificial Analysis run) | 39.2% | 4.4% | DeepSeek V4.1 Flash |
| GPQA Diamond (Epoch AI run) | 89.8% | 47.7% | DeepSeek V4.1 Flash |
| ARC-AGI | 94.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| ARC-AGI-2 | 72.9% | Not measured | Only DeepSeek V4.1 Flash measured |
| Chess puzzles | 48.0% | Not measured | Only DeepSeek V4.1 Flash measured |
| Mystery game puzzles | 43.0% | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: hard prompts | 1483 | 1340 | DeepSeek V4.1 Flash |
| LMArena Text: expert | 1501 | 1304 | DeepSeek V4.1 Flash |
| LMArena Text: hard prompts in English | 1488 | 1353 | DeepSeek V4.1 Flash |
| LMArena Vision: diagrams | 1297 | 1156 | DeepSeek V4.1 Flash |
| LMArena Vision: homework | 1307 | 1160 | DeepSeek V4.1 Flash |
| Kagi LLM Benchmark | Not measured | 35.1% | Only Gemma 3 27B measured |
| Neither model has a result on the other 7 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | DeepSeek V4.1 Flash | Gemma 3 27B | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 20.7 | Only Gemma 3 27B measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 20.7% | Only Gemma 3 27B measured |
| OTIS Mock AIME | 98.3% | 22.5% | DeepSeek V4.1 Flash |
| FrontierMath, tiers 1 to 3 | 67.4% | Not measured | Only DeepSeek V4.1 Flash measured |
| FrontierMath, tier 4 | 26.8% | Not measured | Only DeepSeek V4.1 Flash measured |
| ProofBench | 54.0% | Not measured | Only DeepSeek V4.1 Flash measured |
| MATH level 5 | Not measured | 74.0% | Only Gemma 3 27B measured |
| LMArena Text: math | 1478 | 1312 | DeepSeek V4.1 Flash |
| LMArena Text: mathematical work | 1485 | 1327 | DeepSeek V4.1 Flash |
| Neither model has a result on the other 4 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | DeepSeek V4.1 Flash | Gemma 3 27B | Leader |
|---|---|---|---|
| LegalBench (Vals AI) | 83.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| MedCode (Vals AI) | 41.2% | Not measured | Only DeepSeek V4.1 Flash measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 66.9% | Only Gemma 3 27B measured |
| SimpleQA Verified | 42.9% | Not measured | Only DeepSeek V4.1 Flash measured |
| GeoBench | Not measured | 52.0% | Only Gemma 3 27B measured |
| LMArena Text: science | 1469 | 1383 | DeepSeek V4.1 Flash |
| LMArena Text: medicine and healthcare | 1484 | 1391 | DeepSeek V4.1 Flash |
| LMArena Text: legal and government | 1443 | 1375 | DeepSeek V4.1 Flash |
| LMArena Text: business and finance | 1461 | 1360 | DeepSeek V4.1 Flash |
| FACTS Benchmark Suite | Not measured | 29.1% | Only Gemma 3 27B measured |
| Neither model has a result on the other 8 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | DeepSeek V4.1 Flash | Gemma 3 27B | Leader |
|---|---|---|---|
| Design Arena: websites | 1309 | Not measured | Only DeepSeek V4.1 Flash measured |
| Design Arena: UI components | 1315 | Not measured | Only DeepSeek V4.1 Flash measured |
| Design Arena: game dev | 1330 | Not measured | Only DeepSeek V4.1 Flash measured |
| Design Arena: data viz | 1290 | Not measured | Only DeepSeek V4.1 Flash measured |
| Design Arena: 3D | 1356 | Not measured | Only DeepSeek V4.1 Flash measured |
| Design Arena: all code categories | 1320 | Not measured | Only DeepSeek V4.1 Flash measured |
| Design Arena: SVG | 1244 | Not measured | Only DeepSeek V4.1 Flash measured |
| Design Arena: ASCII art | 1265 | Not measured | Only DeepSeek V4.1 Flash measured |
| LiveBench design | 79.2% | Not measured | Only DeepSeek V4.1 Flash measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index260
| Benchmark | DeepSeek V4.1 Flash | Gemma 3 27B | Leader |
|---|---|---|---|
| LiveBench data analysis | 79.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| Public Benefits Bench (Vals AI) | 64.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| SAGE (Vals AI) | 47.9% | Not measured | Only DeepSeek V4.1 Flash measured |
| MedScribe (Vals AI) | 85.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| IFBench | Not measured | 31.8% | Only Gemma 3 27B measured |
| Long context reasoning (AA-LCR) | 84.0% | 7.3% | DeepSeek V4.1 Flash |
| DTBench | 89.9% | 52.5% | DeepSeek V4.1 Flash |
| LMCA | 47.0% | 12.3% | DeepSeek V4.1 Flash |
| Fiction.LiveBench | Not measured | 33.3% | Only Gemma 3 27B measured |
| Creative writing (Lech Mazur) | Not measured | 79.9% | Only Gemma 3 27B measured |
| Surface Evolver Bench | 46.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| Furniture assembly | 34.2% | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text | 1464 | 1358 | DeepSeek V4.1 Flash |
| LMArena Text: instruction following | 1473 | 1321 | DeepSeek V4.1 Flash |
| LMArena Text: creative writing | 1437 | 1345 | DeepSeek V4.1 Flash |
| LMArena Vision | 1278 | 1165 | DeepSeek V4.1 Flash |
| LMArena Text: English | 1473 | 1377 | DeepSeek V4.1 Flash |
| LMArena Text: non-English | 1449 | 1334 | DeepSeek V4.1 Flash |
| LMArena Text: Chinese | 1501 | 1346 | DeepSeek V4.1 Flash |
| LMArena Text: German | 1470 | 1362 | DeepSeek V4.1 Flash |
| LMArena Text: Japanese | 1432 | 1287 | DeepSeek V4.1 Flash |
| LMArena Text: Korean | 1436 | 1308 | DeepSeek V4.1 Flash |
| LMArena Text: French | 1465 | 1368 | DeepSeek V4.1 Flash |
| LMArena Text: Spanish | 1459 | 1349 | DeepSeek V4.1 Flash |
| LMArena Text: Russian | 1467 | 1349 | DeepSeek V4.1 Flash |
| LMArena Text: Polish | 1457 | 1345 | DeepSeek V4.1 Flash |
| LMArena Text: multi-turn | 1461 | 1345 | DeepSeek V4.1 Flash |
| LMArena Text: longer queries | 1473 | 1333 | DeepSeek V4.1 Flash |
| LMArena Text: writing and language | 1450 | 1347 | DeepSeek V4.1 Flash |
| LMArena Text: entertainment and media | 1445 | 1320 | DeepSeek V4.1 Flash |
| LMArena Vision: English | 1288 | 1188 | DeepSeek V4.1 Flash |
| LMArena Vision: Chinese | 1327 | 1164 | DeepSeek V4.1 Flash |
| LMArena Vision: reading text | 1282 | 1156 | DeepSeek V4.1 Flash |
| LMArena Vision: humor | 1260 | 1166 | DeepSeek V4.1 Flash |
| LMArena Vision: captioning | Not measured | 1151 | Only Gemma 3 27B measured |
| LMArena Vision: recognizing things | Not measured | 1196 | Only Gemma 3 27B measured |
| LMArena Vision: creative writing | 1275 | 1177 | DeepSeek V4.1 Flash |
| Neither model has a result on the other 9 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4.1 Flash | Gemma 3 27B | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
DeepSeek V4.1 Flash$0.0006Fits in one request
Gemma 3 27B$0.0002Fits in one request
Gemma 3 27B costs less
Repository review
50K input + 3K output tokens
DeepSeek V4.1 Flash$0.013Fits in one request
Gemma 3 27B$0.0049Fits in one request
Gemma 3 27B costs less
Agent loop
200K input + 10K output tokens
DeepSeek V4.1 Flash$0.050Fits in one request
Gemma 3 27B$0.019Exceeds the context window
Gemma 3 27B costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | DeepSeek V4.1 Flash | Google Gemma 3 27B |
|---|---|---|
| Maker | DeepSeek | |
| Weights | Open weights | Open weights |
| Released | September 10, 2026 | March 12, 2025 |
| Context window | 1.05M | 131K |
| Input $/Mtok | $0.21 | $0.08 |
| Output $/Mtok | $0.78 | $0.30 |
| Coding index | 62.5 | Not benchmarked |
| Independent measurements behind it | 5 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4.1 Flash better than Gemma 3 27B for coding?
Only DeepSeek V4.1 Flash has a coding score, 62.5 on the Vibe Coding Index; Gemma 3 27B has none, so the two cannot be ranked on coding. Pick Gemma 3 27B for output tokens 2.6x cheaper ($0.30 vs $0.78 per million).
Which is cheaper, DeepSeek V4.1 Flash or Gemma 3 27B?
DeepSeek V4.1 Flash costs $0.21 per million input tokens and $0.78 per million output tokens; Gemma 3 27B costs $0.08 and $0.30. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V4.1 Flash or Gemma 3 27B?
DeepSeek V4.1 Flash, with 1.05M tokens against 131K.
How many benchmarks is the DeepSeek V4.1 Flash vs Gemma 3 27B comparison based on?
45 benchmarks have a result for both models as of October 21, 2026. DeepSeek V4.1 Flash's coding index rests on 5 independent benchmarks and Gemma 3 27B's on 0. Where only one model has a result, the page says so instead of naming a winner.