DeepSeek V4.1 Flash 1017 vs Gemma 4 31B
Updated October 21, 2026. 15 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick DeepSeek V4.1 Flash 1017 for coding: it scores 83.3 on the Vibe Coding Index against 38.3 for Gemma 4 31B.
Pick Gemma 4 31B when output tokens cost 1.5x less.
- Shared results
- 15
- DeepSeek V4.1 Flash 1017 only
- 17
- Gemma 4 31B only
- 53
- Like-for-like categories
- 6 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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, four public coding benchmarks
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 scores 83.3 against 38.3.
2 benchmarks behind each score
Intelligence
AA Intelligence Index
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 leads, 39.5 against 14.7.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Gemma 4 31B has a AA Agentic Index result, 4.2.
Directional only
Web design
Design Arena: websites
No call
Only DeepSeek V4.1 Flash 1017 has a Design Arena: websites result, 1312.
Directional only
General
Vals Index
No call
Only DeepSeek V4.1 Flash 1017 has a Vals Index result, 61.3%.
Directional only
Long documents
Listed context window
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Gemma 4 31B
DeepSeek V4.1 Flash 1017 $0.0004, Gemma 4 31B $0.0004 per turn.
List prices
At a glance
- Coding index
- DeepSeek V4.1 Flash 1017 83.3 from 2 benchmarks, Gemma 4 31B 38.3 from 2 benchmarks.
- Widest category
- Agentic work, 48.2 points apart on average over 3 shared benchmarks.
- Widest single gap
- LiveBench browser: DeepSeek V4.1 Flash 1017 76.0% against Gemma 4 31B 4.0%, 72.0 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4.1 Flash 1017 $0.60 against Gemma 4 31B $0.40 per million output tokens, 1.5x apart.
- Context window
- DeepSeek V4.1 Flash 1017 takes 1.05M tokens, Gemma 4 31B 262K.
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 1017 | Bars | Gemma 4 31B | Ties |
|---|---|---|---|---|
| Coding | 6 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 3 | 0 | ||
| Reasoning | 2 | 0 | ||
| Web design | 1 | 0 | ||
| General | 2 | 0 | ||
| All shared boards | 15 | 0 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index60
| Benchmark | DeepSeek V4.1 Flash 1017 | Gemma 4 31B | Leader |
|---|---|---|---|
| LiveBench coding | 96.0% | 49.0% | DeepSeek V4.1 Flash 1017 |
| LMArena WebDev | 1664 | 1367 | DeepSeek V4.1 Flash 1017 |
| AA Coding Index | Not measured | 43.4 | Only Gemma 4 31B measured |
| Terminal-Bench 4 (Vals AI) | 31.8% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Vibe Code Bench (Vals AI) | 87.7% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Code migration (Vals AI) | 44.2% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| SciCode | 55.9% | 45.5% | DeepSeek V4.1 Flash 1017 |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 36.4% | Only Gemma 4 31B measured |
| WeirdML | Not measured | 52.3% | Only Gemma 4 31B measured |
| ALE-Bench | 1622.0 | 925.5 | DeepSeek V4.1 Flash 1017 |
| LMArena Text: coding | Not measured | 1459 | Only Gemma 4 31B measured |
| LMArena Text: software and IT | Not measured | 1463 | Only Gemma 4 31B measured |
| LMArena WebDev: React | 1667 | 1359 | DeepSeek V4.1 Flash 1017 |
| LMArena WebDev: HTML | 1687 | 1380 | DeepSeek V4.1 Flash 1017 |
| Neither model has a result on the other 24 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4.1 Flash 1017 | Gemma 4 31B | Leader |
|---|---|---|---|
| AA Intelligence Index | 39.5 | 14.7 | DeepSeek V4.1 Flash 1017 |
| Epoch Capabilities Index | Not measured | 143.0 | Only Gemma 4 31B measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | DeepSeek V4.1 Flash 1017 | Gemma 4 31B | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 4.2 | Only Gemma 4 31B measured |
| Design Arena: spreadsheets | 1238 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench research | 79.5% | 44.4% | DeepSeek V4.1 Flash 1017 |
| LiveBench browser | 76.0% | 4.0% | DeepSeek V4.1 Flash 1017 |
| LiveBench long agentic | 50.9% | 13.3% | DeepSeek V4.1 Flash 1017 |
| Finance Agent (Vals AI) | 57.0% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Tax Agent Bench (Vals AI) | 44.4% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Legal Research Bench (Vals AI) | 45.2% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Harvey's Legal Agent Benchmark (Vals AI) | 14.2% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Excel Modeling Benchmark (Vals AI) | 61.8% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| τ²-Bench Telecom | Not measured | 65.5% | Only Gemma 4 31B measured |
| Neither model has a result on the other 28 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V4.1 Flash 1017 | Gemma 4 31B | Leader |
|---|---|---|---|
| LiveBench reasoning | 89.1% | 68.3% | DeepSeek V4.1 Flash 1017 |
| GPQA Diamond (Artificial Analysis run) | Not measured | 85.7% | Only Gemma 4 31B measured |
| Humanity's Last Exam (Artificial Analysis run) | 43.3% | 23.6% | DeepSeek V4.1 Flash 1017 |
| GPQA Diamond (Epoch AI run) | Not measured | 75.8% | Only Gemma 4 31B measured |
| LMArena Text: hard prompts | Not measured | 1448 | Only Gemma 4 31B measured |
| LMArena Text: expert | Not measured | 1464 | Only Gemma 4 31B measured |
| LMArena Text: hard prompts in English | Not measured | 1456 | Only Gemma 4 31B measured |
| LMArena Vision: diagrams | Not measured | 1283 | Only Gemma 4 31B measured |
| LMArena Vision: homework | Not measured | 1310 | Only Gemma 4 31B measured |
| Neither model has a result on the other 12 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | Gemma 4 31B | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 73.3% | Only Gemma 4 31B measured |
| LMArena Text: math | Not measured | 1463 | Only Gemma 4 31B measured |
| LMArena Text: mathematical work | Not measured | 1465 | Only Gemma 4 31B measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | Gemma 4 31B | Leader |
|---|---|---|---|
| CaseLaw (Vals AI) | Not measured | 52.6% | Only Gemma 4 31B measured |
| MortgageTax (Vals AI) | Not measured | 61.4% | Only Gemma 4 31B measured |
| SimpleQA Verified | Not measured | 10.4% | Only Gemma 4 31B measured |
| LMArena Text: science | Not measured | 1457 | Only Gemma 4 31B measured |
| LMArena Text: medicine and healthcare | Not measured | 1451 | Only Gemma 4 31B measured |
| LMArena Text: legal and government | Not measured | 1438 | Only Gemma 4 31B measured |
| LMArena Text: business and finance | Not measured | 1430 | Only Gemma 4 31B measured |
| Neither model has a result on the other 10 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4.1 Flash 1017 | Gemma 4 31B | Leader |
|---|---|---|---|
| Design Arena: websites | 1312 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: UI components | 1305 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: game dev | 1365 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: data viz | 1330 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: 3D | 1368 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: all code categories | 1335 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: ASCII art | 1244 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench design | 83.4% | 58.4% | DeepSeek V4.1 Flash 1017 |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V4.1 Flash 1017 | Gemma 4 31B | Leader |
|---|---|---|---|
| Vals Index | 61.3% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench data analysis | 79.5% | 53.3% | DeepSeek V4.1 Flash 1017 |
| SAGE (Vals AI) | Not measured | 55.0% | Only Gemma 4 31B measured |
| IFBench | Not measured | 75.6% | Only Gemma 4 31B measured |
| Long context reasoning (AA-LCR) | 82.7% | 69.7% | DeepSeek V4.1 Flash 1017 |
| DTBench | Not measured | 82.7% | Only Gemma 4 31B measured |
| LMCA | Not measured | 39.3% | Only Gemma 4 31B measured |
| Surface Evolver Bench | Not measured | 30.6% | Only Gemma 4 31B measured |
| LMArena Text | Not measured | 1443 | Only Gemma 4 31B measured |
| LMArena Text: instruction following | Not measured | 1433 | Only Gemma 4 31B measured |
| LMArena Text: creative writing | Not measured | 1417 | Only Gemma 4 31B measured |
| LMArena Vision | Not measured | 1276 | Only Gemma 4 31B measured |
| LMArena Text: English | Not measured | 1451 | Only Gemma 4 31B measured |
| LMArena Text: non-English | Not measured | 1430 | Only Gemma 4 31B measured |
| LMArena Text: Chinese | Not measured | 1475 | Only Gemma 4 31B measured |
| LMArena Text: French | Not measured | 1436 | Only Gemma 4 31B measured |
| LMArena Text: Spanish | Not measured | 1442 | Only Gemma 4 31B measured |
| LMArena Text: Russian | Not measured | 1460 | Only Gemma 4 31B measured |
| LMArena Text: Polish | Not measured | 1433 | Only Gemma 4 31B measured |
| LMArena Text: multi-turn | Not measured | 1452 | Only Gemma 4 31B measured |
| LMArena Text: longer queries | Not measured | 1446 | Only Gemma 4 31B measured |
| LMArena Text: writing and language | Not measured | 1432 | Only Gemma 4 31B measured |
| LMArena Text: entertainment and media | Not measured | 1405 | Only Gemma 4 31B measured |
| LMArena Vision: English | Not measured | 1271 | Only Gemma 4 31B measured |
| LMArena Vision: Chinese | Not measured | 1326 | Only Gemma 4 31B measured |
| LMArena Vision: reading text | Not measured | 1282 | Only Gemma 4 31B measured |
| LMArena Vision: humor | Not measured | 1280 | Only Gemma 4 31B measured |
| LMArena Vision: captioning | Not measured | 1218 | Only Gemma 4 31B measured |
| LMArena Vision: recognizing things | Not measured | 1226 | Only Gemma 4 31B measured |
| LMArena Vision: creative writing | Not measured | 1285 | Only Gemma 4 31B measured |
| LMArena Document | Not measured | 1425 | Only Gemma 4 31B measured |
| Neither model has a result on the other 15 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | Gemma 4 31B | 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
Gemma 4 31B costs less
Repository review
50K input + 3K output tokens
DeepSeek V4.1 Flash 1017 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V4.1 Flash 1017 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 1017 | Google Gemma 4 31B |
|---|---|---|
| Maker | DeepSeek | |
| Weights | Open weights | Open weights |
| Released | October 17, 2026 | April 2, 2026 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $0.15 | $0.20 |
| Output $/Mtok | $0.60 | $0.40 |
| Coding index | 83.3 | 38.3 |
| Independent measurements behind it | 2 | 2 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4.1 Flash 1017 better than Gemma 4 31B for coding?
Pick DeepSeek V4.1 Flash 1017 for coding: it scores 83.3 on the Vibe Coding Index against 38.3 for Gemma 4 31B. Pick Gemma 4 31B when output tokens cost 1.5x less.
Which is cheaper, DeepSeek V4.1 Flash 1017 or Gemma 4 31B?
DeepSeek V4.1 Flash 1017 costs $0.15 per million input tokens and $0.60 per million output tokens; Gemma 4 31B costs $0.20 and $0.40. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V4.1 Flash 1017 or Gemma 4 31B?
DeepSeek V4.1 Flash 1017, with 1.05M tokens against 262K.
How many benchmarks is the DeepSeek V4.1 Flash 1017 vs Gemma 4 31B comparison based on?
15 benchmarks have a result for both models as of October 21, 2026. DeepSeek V4.1 Flash 1017's coding index rests on 2 independent benchmarks and Gemma 4 31B's on 2. Where only one model has a result, the page says so instead of naming a winner.