DeepSeek V4.1 Flash vs Gemini 2.5 Pro
DeepSeek V4.1 Flash leads Gemini 2.5 Pro on coding, 62.5 to 19.1 on the Vibe Coding Index, and DeepSeek V4.1 Flash costs 12.8x less per output token ($0.78 vs $10.00 per million). Updated October 21, 2026; 62 benchmarks have a result for both.
vs boards won of 62 shared
DeepSeek V4.1 Flash or Gemini 2.5 Pro: which to pick
Pick DeepSeek V4.1 Flash for coding: it scores 62.5 on the Vibe Coding Index against 19.1 for Gemini 2.5 Pro.
Pick Gemini 2.5 Pro for work measured by MedCode (Vals AI) or LMArena Text: science, where it leads.
Model ADeepSeek V4.1 FlashDeepSeek62.5/1005 independent benchmarksNo cost per solved taskModel BGemini 2.5 ProGoogle19.1/1003 independent benchmarks$0.64 per solved task
- Shared results
- 62
- DeepSeek V4.1 Flash only
- 39
- Gemini 2.5 Pro only
- 28
- Like-for-like categories
- 7 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
DeepSeek V4.1 Flash scores 62.5 against 19.1.
5 and 3 benchmarks behind the scores
Intelligence
AA Intelligence Index
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash leads, 24.7 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
DeepSeek V4.1 Flash leads, 1309 against 1167.
Same board, both measured
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
DeepSeek V4.1 Flash $0.0006, 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 62.5 from 5 benchmarks, Gemini 2.5 Pro 19.1 from 3 benchmarks.
- Widest category
- Coding, 39.1 points apart on average over 7 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): DeepSeek V4.1 Flash 84.7% against Gemini 2.5 Pro 0.4%, 84.3 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4.1 Flash $0.78 against Gemini 2.5 Pro $10.00 per million output tokens, 12.8x 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 | Bars | Gemini 2.5 Pro | Ties |
|---|---|---|---|---|
| Coding | 7 | 0 | ||
| Intelligence | 2 | 0 | ||
| Reasoning | 9 | 0 | ||
| Math | 5 | 0 | ||
| Knowledge | 2 | 3 | ||
| Web design | 6 | 0 | ||
| General | 18 | 10 | ||
| All shared boards | 49 | 13 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index70
| Benchmark | DeepSeek V4.1 Flash | Gemini 2.5 Pro | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 53.6% | Only Gemini 2.5 Pro measured |
| LiveBench coding | 90.2% | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena WebDev | 1618 | 1226 | DeepSeek V4.1 Flash |
| 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) | 19.7% | Not measured | Only DeepSeek V4.1 Flash measured |
| Vibe Code Bench (Vals AI) | 84.7% | 0.4% | DeepSeek V4.1 Flash |
| 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 | 80.1% | Only Gemini 2.5 Pro measured |
| SciCode | 51.9% | 46.3% | DeepSeek V4.1 Flash |
| 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 |
| FrontierCode | 43.9% | Not measured | Only DeepSeek V4.1 Flash 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 | 1092.3 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: coding | 1502 | 1452 | DeepSeek V4.1 Flash |
| LMArena Text: software and IT | 1496 | 1459 | DeepSeek V4.1 Flash |
| LMArena WebDev: React | 1628 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena WebDev: HTML | 1612 | 1235 | DeepSeek V4.1 Flash |
| LMArena WebDev: from an image | 1606 | 1285 | DeepSeek V4.1 Flash |
| Neither model has a result on the other 11 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V4.1 Flash | Gemini 2.5 Pro | Leader |
|---|---|---|---|
| AA Intelligence Index | 24.7 | 16.1 | DeepSeek V4.1 Flash |
| Epoch Capabilities Index | 155.9 | 145.1 | DeepSeek V4.1 Flash |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index18 boards, none shared
| Benchmark | DeepSeek V4.1 Flash | Gemini 2.5 Pro | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 1.6 | Only Gemini 2.5 Pro 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 | 54.1% | Only Gemini 2.5 Pro measured |
| APEX-Agents | 39.5% | Not measured | Only DeepSeek V4.1 Flash 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 23 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index90
| Benchmark | DeepSeek V4.1 Flash | Gemini 2.5 Pro | 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 | 84.4% | Only Gemini 2.5 Pro measured |
| Humanity's Last Exam (Artificial Analysis run) | 39.2% | 22.5% | DeepSeek V4.1 Flash |
| GPQA Diamond (Epoch AI run) | 89.8% | 85.3% | DeepSeek V4.1 Flash |
| ARC-AGI | 94.5% | 41.0% | DeepSeek V4.1 Flash |
| ARC-AGI-2 | 72.9% | 4.9% | DeepSeek V4.1 Flash |
| 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 | 1455 | DeepSeek V4.1 Flash |
| LMArena Text: expert | 1501 | 1452 | DeepSeek V4.1 Flash |
| LMArena Text: hard prompts in English | 1488 | 1453 | DeepSeek V4.1 Flash |
| LMArena Vision: diagrams | 1297 | 1274 | DeepSeek V4.1 Flash |
| LMArena Vision: homework | 1307 | 1281 | DeepSeek V4.1 Flash |
| Kagi LLM Benchmark | Not measured | 70.3% | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 7 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | DeepSeek V4.1 Flash | 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 | 98.3% | 84.7% | DeepSeek V4.1 Flash |
| FrontierMath, tiers 1 to 3 | 67.4% | 24.6% | DeepSeek V4.1 Flash |
| FrontierMath, tier 4 | 26.8% | 0.0% | DeepSeek V4.1 Flash |
| ProofBench | 54.0% | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: math | 1478 | 1450 | DeepSeek V4.1 Flash |
| LMArena Text: mathematical work | 1485 | 1454 | DeepSeek V4.1 Flash |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index23
| Benchmark | DeepSeek V4.1 Flash | Gemini 2.5 Pro | Leader |
|---|---|---|---|
| LegalBench (Vals AI) | 83.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| 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) | 41.2% | 50.6% | Gemini 2.5 Pro |
| MMLU Pro (Artificial Analysis run) | Not measured | 86.2% | Only Gemini 2.5 Pro measured |
| SimpleQA Verified | 42.9% | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: science | 1469 | 1480 | Gemini 2.5 Pro |
| LMArena Text: medicine and healthcare | 1484 | 1476 | DeepSeek V4.1 Flash |
| LMArena Text: legal and government | 1443 | 1480 | Gemini 2.5 Pro |
| LMArena Text: business and finance | 1461 | 1439 | DeepSeek V4.1 Flash |
| FACTS Benchmark Suite | Not measured | 61.5% | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 6 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | DeepSeek V4.1 Flash | Gemini 2.5 Pro | Leader |
|---|---|---|---|
| Design Arena: websites | 1309 | 1167 | DeepSeek V4.1 Flash |
| Design Arena: UI components | 1315 | 1140 | DeepSeek V4.1 Flash |
| Design Arena: game dev | 1330 | 1111 | DeepSeek V4.1 Flash |
| Design Arena: data viz | 1290 | 1221 | DeepSeek V4.1 Flash |
| Design Arena: 3D | 1356 | 1081 | DeepSeek V4.1 Flash |
| Design Arena: all code categories | 1320 | 1152 | DeepSeek V4.1 Flash |
| 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 index1810
| Benchmark | DeepSeek V4.1 Flash | Gemini 2.5 Pro | 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% | 41.9% | DeepSeek V4.1 Flash |
| MedScribe (Vals AI) | 85.5% | 73.5% | DeepSeek V4.1 Flash |
| IFBench | Not measured | 48.7% | Only Gemini 2.5 Pro measured |
| Long context reasoning (AA-LCR) | 84.0% | 69.0% | DeepSeek V4.1 Flash |
| DTBench | 89.9% | 82.4% | DeepSeek V4.1 Flash |
| LMCA | 47.0% | 34.8% | DeepSeek V4.1 Flash |
| Creative writing (Lech Mazur) | Not measured | 83.8% | Only Gemini 2.5 Pro 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 | 1458 | DeepSeek V4.1 Flash |
| LMArena Text: instruction following | 1473 | 1437 | DeepSeek V4.1 Flash |
| LMArena Text: creative writing | 1437 | 1454 | Gemini 2.5 Pro |
| LMArena Vision | 1278 | 1263 | DeepSeek V4.1 Flash |
| LMArena Text: English | 1473 | 1458 | DeepSeek V4.1 Flash |
| LMArena Text: non-English | 1449 | 1452 | Gemini 2.5 Pro |
| LMArena Text: Chinese | 1501 | 1507 | Gemini 2.5 Pro |
| LMArena Text: German | 1470 | 1487 | Gemini 2.5 Pro |
| LMArena Text: Japanese | 1432 | 1461 | Gemini 2.5 Pro |
| LMArena Text: Korean | 1436 | 1434 | DeepSeek V4.1 Flash |
| LMArena Text: French | 1465 | 1473 | Gemini 2.5 Pro |
| LMArena Text: Spanish | 1459 | 1474 | Gemini 2.5 Pro |
| LMArena Text: Russian | 1467 | 1461 | DeepSeek V4.1 Flash |
| LMArena Text: Polish | 1457 | 1479 | Gemini 2.5 Pro |
| LMArena Text: multi-turn | 1461 | 1453 | DeepSeek V4.1 Flash |
| LMArena Text: longer queries | 1473 | 1449 | DeepSeek V4.1 Flash |
| LMArena Text: writing and language | 1450 | 1454 | Gemini 2.5 Pro |
| LMArena Text: entertainment and media | 1445 | 1441 | DeepSeek V4.1 Flash |
| LMArena Vision: English | 1288 | 1261 | DeepSeek V4.1 Flash |
| LMArena Vision: Chinese | 1327 | 1300 | DeepSeek V4.1 Flash |
| LMArena Vision: reading text | 1282 | 1270 | DeepSeek V4.1 Flash |
| LMArena Vision: humor | 1260 | 1262 | Gemini 2.5 Pro |
| 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 | 1275 | 1265 | DeepSeek V4.1 Flash |
| LMArena Document | Not measured | 1421 | Only Gemini 2.5 Pro measured |
| 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 | 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$0.0006Fits in one request
Gemini 2.5 Pro$0.0063Fits in one request
DeepSeek V4.1 Flash costs less
Repository review
50K input + 3K output tokens
DeepSeek V4.1 Flash$0.013Fits in one request
Gemini 2.5 Pro$0.092Fits in one request
DeepSeek V4.1 Flash costs less
Agent loop
200K input + 10K output tokens
DeepSeek V4.1 Flash$0.050Fits in one request
Gemini 2.5 Pro$0.350Fits in one request
DeepSeek V4.1 Flash 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 Gemini 2.5 Pro |
|---|---|---|
| Maker | DeepSeek | |
| Weights | Open weights | No public weights listed |
| Released | September 10, 2026 | June 17, 2025 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $0.21 | $1.25 |
| Output $/Mtok | $0.78 | $10.00 |
| Coding index | 62.5 | 19.1 |
| Independent measurements behind it | 5 | 3 |
| Cost per solved task | Not computable | $0.64 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4.1 Flash better than Gemini 2.5 Pro for coding?
Pick DeepSeek V4.1 Flash for coding: it scores 62.5 on the Vibe Coding Index against 19.1 for Gemini 2.5 Pro. Pick Gemini 2.5 Pro for work measured by MedCode (Vals AI) or LMArena Text: science, where it leads.
Which is cheaper, DeepSeek V4.1 Flash or Gemini 2.5 Pro?
DeepSeek V4.1 Flash costs $0.21 per million input tokens and $0.78 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 or Gemini 2.5 Pro?
Neither: both take 1.05M tokens.
How many benchmarks is the DeepSeek V4.1 Flash vs Gemini 2.5 Pro comparison based on?
62 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 Gemini 2.5 Pro's on 3. Where only one model has a result, the page says so instead of naming a winner.