DeepSeek V4.1 Flash 1017 vs Gemini 2.5 Pro
DeepSeek V4.1 Flash 1017 leads Gemini 2.5 Pro on coding, 70.3 to 19.1 on the Vibe Coding Index, and DeepSeek V4.1 Flash 1017 costs 16.7x less per output token ($0.60 vs $10.00 per million). Updated October 22, 2026; 17 benchmarks have a result for both.
DeepSeek V4.1 Flash 1017 or Gemini 2.5 Pro: which to pick
Pick DeepSeek V4.1 Flash 1017 for coding: it scores 70.3 on the Vibe Coding Index against 19.1 for Gemini 2.5 Pro.
Gemini 2.5 Pro is neither cheaper nor larger in context, and leads no benchmark both models were measured on.
- Shared results
- 17
- DeepSeek V4.1 Flash 1017 only
- 22
- Gemini 2.5 Pro only
- 73
- 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
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 scores 70.3 against 19.1.
4 and 3 benchmarks behind the scores
Intelligence
AA Intelligence Index
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 leads, 39.5 against 16.1.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Gemini 2.5 Pro has a AA Agentic Index result, 1.6.
Directional only
Web design
Design Arena: websites
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 leads, 1311 against 1167.
Same board, both measured
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
No call
Both take 1.05M tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 $0.0004, Gemini 2.5 Pro $0.0063 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Gemini 2.5 Pro has the number, $0.64.
Directional only
At a glance
- Coding index
- DeepSeek V4.1 Flash 1017 70.3 from 4 benchmarks, Gemini 2.5 Pro 19.1 from 3 benchmarks.
- Widest category
- Coding, 55.1 points apart on average over 4 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): DeepSeek V4.1 Flash 1017 87.7% against Gemini 2.5 Pro 0.4%, 87.3 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4.1 Flash 1017 $0.60 against Gemini 2.5 Pro $10.00 per million output tokens, 16.7x apart.
- Context window
- Both take 1.05M tokens.
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 | Gemini 2.5 Pro | Ties |
|---|---|---|---|---|
| Coding | 4 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| Math | 3 | 0 | ||
| Web design | 6 | 0 | ||
| General | 1 | 0 | ||
| All shared boards | 17 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
| Benchmark | DeepSeek V4.1 Flash 1017 | Gemini 2.5 Pro | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 53.6% | Only Gemini 2.5 Pro measured |
| LiveBench coding | 96.0% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LMArena WebDev | 1664 | 1226 | DeepSeek V4.1 Flash 1017 |
| AA Coding Index | Not measured | 33.3 | Only Gemini 2.5 Pro measured |
| SWE-bench Verified (Vals AI) | Not measured | 54.4% | Only Gemini 2.5 Pro measured |
| Terminal-Bench 4 (Vals AI) | 31.8% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Vibe Code Bench (Vals AI) | 87.7% | 0.4% | DeepSeek V4.1 Flash 1017 |
| Code migration (Vals AI) | 44.2% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 80.1% | Only Gemini 2.5 Pro measured |
| SciCode | 55.9% | 46.3% | DeepSeek V4.1 Flash 1017 |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 26.5% | Only Gemini 2.5 Pro measured |
| Terminal-Bench | Not measured | 32.6% | Only Gemini 2.5 Pro measured |
| GSO-Bench | Not measured | 3.9% | Only Gemini 2.5 Pro measured |
| WeirdML | Not measured | 54.0% | Only Gemini 2.5 Pro measured |
| ALE-Bench | 1622.0 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LMArena Text: coding | Not measured | 1452 | Only Gemini 2.5 Pro measured |
| LMArena Text: software and IT | Not measured | 1459 | Only Gemini 2.5 Pro measured |
| LMArena WebDev: React | 1667 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LMArena WebDev: HTML | 1687 | 1235 | DeepSeek V4.1 Flash 1017 |
| LMArena WebDev: from an image | Not measured | 1285 | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 19 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4.1 Flash 1017 | Gemini 2.5 Pro | Leader |
|---|---|---|---|
| AA Intelligence Index | 39.5 | 16.1 | DeepSeek V4.1 Flash 1017 |
| Epoch Capabilities Index | Not measured | 145.1 | Only Gemini 2.5 Pro measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index15 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | Gemini 2.5 Pro | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 1.6 | Only Gemini 2.5 Pro measured |
| Design Arena: spreadsheets | 1245 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench research | 79.5% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench browser | 76.0% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench long agentic | 50.9% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| 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 | 54.1% | Only Gemini 2.5 Pro measured |
| GDPval | Not measured | 23.3% | Only Gemini 2.5 Pro measured |
| DeepResearch Bench | Not measured | 41.5% | Only Gemini 2.5 Pro measured |
| Vending-Bench 2 | Not measured | $574 | Only Gemini 2.5 Pro measured |
| BrowseComp-Plus | Not measured | 29.5% | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 26 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V4.1 Flash 1017 | Gemini 2.5 Pro | Leader |
|---|---|---|---|
| LiveBench reasoning | 89.1% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 84.4% | Only Gemini 2.5 Pro measured |
| Humanity's Last Exam (Artificial Analysis run) | 43.3% | 22.5% | DeepSeek V4.1 Flash 1017 |
| GPQA Diamond (Epoch AI run) | 93.2% | 85.3% | DeepSeek V4.1 Flash 1017 |
| ARC-AGI | Not measured | 41.0% | Only Gemini 2.5 Pro measured |
| ARC-AGI-2 | Not measured | 4.9% | Only Gemini 2.5 Pro measured |
| Chess puzzles | 46.0% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Mystery game puzzles | 49.0% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LMArena Text: hard prompts | Not measured | 1455 | Only Gemini 2.5 Pro measured |
| LMArena Text: expert | Not measured | 1452 | Only Gemini 2.5 Pro measured |
| LMArena Text: hard prompts in English | Not measured | 1453 | Only Gemini 2.5 Pro measured |
| LMArena Vision: diagrams | Not measured | 1274 | Only Gemini 2.5 Pro measured |
| LMArena Vision: homework | Not measured | 1281 | Only Gemini 2.5 Pro measured |
| Kagi LLM Benchmark | Not measured | 70.3% | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 8 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | DeepSeek V4.1 Flash 1017 | Gemini 2.5 Pro | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 87.7 | Only Gemini 2.5 Pro measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 87.7% | Only Gemini 2.5 Pro measured |
| OTIS Mock AIME | 99.4% | 84.7% | DeepSeek V4.1 Flash 1017 |
| FrontierMath, tiers 1 to 3 | 71.2% | 24.6% | DeepSeek V4.1 Flash 1017 |
| FrontierMath, tier 4 | 46.3% | 0.0% | DeepSeek V4.1 Flash 1017 |
| LMArena Text: math | Not measured | 1450 | Only Gemini 2.5 Pro measured |
| LMArena Text: mathematical work | Not measured | 1454 | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 6 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | Gemini 2.5 Pro | Leader |
|---|---|---|---|
| CaseLaw (Vals AI) | Not measured | 63.9% | Only Gemini 2.5 Pro measured |
| CorpFin (Vals AI) | Not measured | 60.8% | Only Gemini 2.5 Pro measured |
| MortgageTax (Vals AI) | Not measured | 68.9% | Only Gemini 2.5 Pro measured |
| MedCode (Vals AI) | Not measured | 50.6% | Only Gemini 2.5 Pro measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 86.2% | Only Gemini 2.5 Pro measured |
| SimpleQA Verified | 42.5% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LMArena Text: science | Not measured | 1480 | Only Gemini 2.5 Pro measured |
| LMArena Text: medicine and healthcare | Not measured | 1476 | Only Gemini 2.5 Pro measured |
| LMArena Text: legal and government | Not measured | 1480 | Only Gemini 2.5 Pro measured |
| LMArena Text: business and finance | Not measured | 1439 | Only Gemini 2.5 Pro measured |
| FACTS Benchmark Suite | Not measured | 61.5% | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 7 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | DeepSeek V4.1 Flash 1017 | Gemini 2.5 Pro | Leader |
|---|---|---|---|
| Design Arena: websites | 1311 | 1167 | DeepSeek V4.1 Flash 1017 |
| Design Arena: UI components | 1303 | 1137 | DeepSeek V4.1 Flash 1017 |
| Design Arena: game dev | 1357 | 1111 | DeepSeek V4.1 Flash 1017 |
| Design Arena: data viz | 1330 | 1222 | DeepSeek V4.1 Flash 1017 |
| Design Arena: 3D | 1358 | 1080 | DeepSeek V4.1 Flash 1017 |
| Design Arena: all code categories | 1332 | 1152 | DeepSeek V4.1 Flash 1017 |
| Design Arena: ASCII art | 1242 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench design | 83.4% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4.1 Flash 1017 | Gemini 2.5 Pro | Leader |
|---|---|---|---|
| Vals Index | 61.3% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench data analysis | 79.5% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| SAGE (Vals AI) | Not measured | 41.9% | Only Gemini 2.5 Pro measured |
| MedScribe (Vals AI) | Not measured | 73.5% | Only Gemini 2.5 Pro measured |
| IFBench | Not measured | 48.7% | Only Gemini 2.5 Pro measured |
| Long context reasoning (AA-LCR) | 82.7% | 69.0% | DeepSeek V4.1 Flash 1017 |
| DTBench | Not measured | 82.4% | Only Gemini 2.5 Pro measured |
| LMCA | Not measured | 34.8% | Only Gemini 2.5 Pro measured |
| Creative writing (Lech Mazur) | Not measured | 83.8% | Only Gemini 2.5 Pro measured |
| LMArena Text | Not measured | 1458 | Only Gemini 2.5 Pro measured |
| LMArena Text: instruction following | Not measured | 1437 | Only Gemini 2.5 Pro measured |
| LMArena Text: creative writing | Not measured | 1454 | Only Gemini 2.5 Pro measured |
| LMArena Vision | Not measured | 1263 | Only Gemini 2.5 Pro measured |
| LMArena Text: English | Not measured | 1458 | Only Gemini 2.5 Pro measured |
| LMArena Text: non-English | Not measured | 1452 | Only Gemini 2.5 Pro measured |
| LMArena Text: Chinese | Not measured | 1507 | Only Gemini 2.5 Pro measured |
| LMArena Text: German | Not measured | 1487 | Only Gemini 2.5 Pro measured |
| LMArena Text: Japanese | Not measured | 1461 | Only Gemini 2.5 Pro measured |
| LMArena Text: Korean | Not measured | 1434 | Only Gemini 2.5 Pro measured |
| LMArena Text: French | Not measured | 1473 | Only Gemini 2.5 Pro measured |
| LMArena Text: Spanish | Not measured | 1474 | Only Gemini 2.5 Pro measured |
| LMArena Text: Russian | Not measured | 1461 | Only Gemini 2.5 Pro measured |
| LMArena Text: Polish | Not measured | 1479 | Only Gemini 2.5 Pro measured |
| LMArena Text: multi-turn | Not measured | 1453 | Only Gemini 2.5 Pro measured |
| LMArena Text: longer queries | Not measured | 1449 | Only Gemini 2.5 Pro measured |
| LMArena Text: writing and language | Not measured | 1454 | Only Gemini 2.5 Pro measured |
| LMArena Text: entertainment and media | Not measured | 1441 | Only Gemini 2.5 Pro measured |
| LMArena Vision: English | Not measured | 1261 | Only Gemini 2.5 Pro measured |
| LMArena Vision: Chinese | Not measured | 1300 | Only Gemini 2.5 Pro measured |
| LMArena Vision: reading text | Not measured | 1270 | Only Gemini 2.5 Pro measured |
| LMArena Vision: humor | Not measured | 1262 | Only Gemini 2.5 Pro measured |
| LMArena Vision: captioning | Not measured | 1304 | Only Gemini 2.5 Pro measured |
| LMArena Vision: recognizing things | Not measured | 1283 | Only Gemini 2.5 Pro measured |
| LMArena Vision: creative writing | Not measured | 1265 | Only Gemini 2.5 Pro measured |
| LMArena Document | Not measured | 1421 | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 11 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | Gemini 2.5 Pro | 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 1017 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 Gemini 2.5 Pro |
|---|---|---|
| Maker | DeepSeek | |
| Weights | Open weights | No public weights listed |
| Released | October 17, 2026 | June 17, 2025 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $0.15 | $1.25 |
| Output $/Mtok | $0.60 | $10.00 |
| Coding index | 70.3 | 19.1 |
| Independent measurements behind it | 4 | 3 |
| Cost per solved task | Not computable | $0.64 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4.1 Flash 1017 better than Gemini 2.5 Pro for coding?
Pick DeepSeek V4.1 Flash 1017 for coding: it scores 70.3 on the Vibe Coding Index against 19.1 for Gemini 2.5 Pro. Gemini 2.5 Pro is neither cheaper nor larger in context, and leads no benchmark both models were measured on.
Which is cheaper, DeepSeek V4.1 Flash 1017 or Gemini 2.5 Pro?
DeepSeek V4.1 Flash 1017 costs $0.15 per million input tokens and $0.60 per million output tokens; Gemini 2.5 Pro costs $1.25 and $10.00. Cost per solved task is known for Gemini 2.5 Pro only.
Which has the bigger context window, DeepSeek V4.1 Flash 1017 or Gemini 2.5 Pro?
Neither: both take 1.05M tokens.
How many benchmarks is the DeepSeek V4.1 Flash 1017 vs Gemini 2.5 Pro comparison based on?
17 benchmarks have a result for both models as of October 22, 2026. DeepSeek V4.1 Flash 1017's coding index rests on 4 independent benchmarks and Gemini 2.5 Pro's on 3. Where only one model has a result, the page says so instead of naming a winner.