R1 0528 vs DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 leads R1 0528 on the Artificial Analysis Intelligence Index, 39.5 to 13.1, and DeepSeek V4.1 Flash 1017 costs 3.6x less per output token ($0.60 vs $2.18 per million). Updated October 22, 2026; 12 benchmarks have a result for both.
R1 0528 or DeepSeek V4.1 Flash 1017: which to pick
Only DeepSeek V4.1 Flash 1017 has a coding score, 70.3 on the Vibe Coding Index; R1 0528 has none, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
- Shared results
- 12
- R1 0528 only
- 48
- DeepSeek V4.1 Flash 1017 only
- 27
- 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 1017 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 leads, 39.5 against 13.1.
Same board, both measured
Web design
Design Arena: websites
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 leads, 1311 against 1149.
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
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 takes 1.05M tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V4.1 Flash 1017
R1 0528 $0.0016, DeepSeek V4.1 Flash 1017 $0.0004 per turn.
List prices
At a glance
- Coding index
- R1 0528 not benchmarked, DeepSeek V4.1 Flash 1017 70.3 from 4 benchmarks.
- Widest category
- Math, 33.1 points apart on average over 1 shared benchmark.
- Widest single gap
- OTIS Mock AIME: R1 0528 66.4% against DeepSeek V4.1 Flash 1017 99.4%, 33.1 points apart on a 0 to 100 scale.
- Output price
- R1 0528 $2.18 against DeepSeek V4.1 Flash 1017 $0.60 per million output tokens, 3.6x apart.
- Context window
- R1 0528 takes 164K tokens, DeepSeek V4.1 Flash 1017 1.05M.
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 | R1 0528 | Bars | DeepSeek V4.1 Flash 1017 | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 2 | ||
| Math | 0 | 1 | ||
| Web design | 0 | 6 | ||
| General | 0 | 1 | ||
| All shared boards | 0 | 12 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | R1 0528 | DeepSeek V4.1 Flash 1017 | Leader |
|---|---|---|---|
| Aider polyglot | 71.4% | Not measured | Only R1 0528 measured |
| LiveBench coding | Not measured | 96.0% | Only DeepSeek V4.1 Flash 1017 measured |
| LMArena WebDev | Not measured | 1664 | Only DeepSeek V4.1 Flash 1017 measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 31.8% | Only DeepSeek V4.1 Flash 1017 measured |
| Vibe Code Bench (Vals AI) | Not measured | 87.7% | Only DeepSeek V4.1 Flash 1017 measured |
| Code migration (Vals AI) | Not measured | 44.2% | Only DeepSeek V4.1 Flash 1017 measured |
| LiveCodeBench (Artificial Analysis run) | 77.0% | Not measured | Only R1 0528 measured |
| SciCode | Not measured | 55.9% | Only DeepSeek V4.1 Flash 1017 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 15.9% | Not measured | Only R1 0528 measured |
| WeirdML | 41.6% | Not measured | Only R1 0528 measured |
| ALE-Bench | 804.1 | 1622.0 | DeepSeek V4.1 Flash 1017 |
| LMArena Text: coding | 1427 | Not measured | Only R1 0528 measured |
| LMArena Text: software and IT | 1435 | Not measured | Only R1 0528 measured |
| LMArena WebDev: React | Not measured | 1667 | Only DeepSeek V4.1 Flash 1017 measured |
| LMArena WebDev: HTML | Not measured | 1687 | Only DeepSeek V4.1 Flash 1017 measured |
| Neither model has a result on the other 24 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | R1 0528 | DeepSeek V4.1 Flash 1017 | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.1 | 39.5 | DeepSeek V4.1 Flash 1017 |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index13 boards, none shared
| Benchmark | R1 0528 | DeepSeek V4.1 Flash 1017 | Leader |
|---|---|---|---|
| Design Arena: spreadsheets | Not measured | 1245 | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench research | Not measured | 79.5% | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench browser | Not measured | 76.0% | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench long agentic | Not measured | 50.9% | Only DeepSeek V4.1 Flash 1017 measured |
| Finance Agent (Vals AI) | Not measured | 57.0% | Only DeepSeek V4.1 Flash 1017 measured |
| Tax Agent Bench (Vals AI) | Not measured | 44.4% | Only DeepSeek V4.1 Flash 1017 measured |
| Legal Research Bench (Vals AI) | Not measured | 45.2% | Only DeepSeek V4.1 Flash 1017 measured |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 14.2% | Only DeepSeek V4.1 Flash 1017 measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 61.8% | Only DeepSeek V4.1 Flash 1017 measured |
| τ²-Bench Telecom | 36.5% | Not measured | Only R1 0528 measured |
| METR time horizons | 53.8% | Not measured | Only R1 0528 measured |
| DeepResearch Bench | 35.1% | Not measured | Only R1 0528 measured |
| BrowseComp-Plus | 16.4% | Not measured | Only R1 0528 measured |
| Neither model has a result on the other 28 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | R1 0528 | DeepSeek V4.1 Flash 1017 | Leader |
|---|---|---|---|
| LiveBench reasoning | Not measured | 89.1% | Only DeepSeek V4.1 Flash 1017 measured |
| GPQA Diamond (Artificial Analysis run) | 81.3% | Not measured | Only R1 0528 measured |
| Humanity's Last Exam (Artificial Analysis run) | 15.8% | 43.3% | DeepSeek V4.1 Flash 1017 |
| GPQA Diamond (Epoch AI run) | 76.3% | 93.2% | DeepSeek V4.1 Flash 1017 |
| ARC-AGI | 21.2% | Not measured | Only R1 0528 measured |
| ARC-AGI-2 | 1.1% | Not measured | Only R1 0528 measured |
| SimpleBench | 40.8% | Not measured | Only R1 0528 measured |
| Chess puzzles | Not measured | 46.0% | Only DeepSeek V4.1 Flash 1017 measured |
| Mystery game puzzles | Not measured | 49.0% | Only DeepSeek V4.1 Flash 1017 measured |
| LMArena Text: hard prompts | 1415 | Not measured | Only R1 0528 measured |
| LMArena Text: expert | 1394 | Not measured | Only R1 0528 measured |
| LMArena Text: hard prompts in English | 1422 | Not measured | Only R1 0528 measured |
| Neither model has a result on the other 10 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | R1 0528 | DeepSeek V4.1 Flash 1017 | Leader |
|---|---|---|---|
| AA Math Index | 76.0 | Not measured | Only R1 0528 measured |
| AIME 2025 (Artificial Analysis run) | 76.0% | Not measured | Only R1 0528 measured |
| OTIS Mock AIME | 66.4% | 99.4% | DeepSeek V4.1 Flash 1017 |
| FrontierMath, tiers 1 to 3 | Not measured | 71.2% | Only DeepSeek V4.1 Flash 1017 measured |
| FrontierMath, tier 4 | Not measured | 46.3% | Only DeepSeek V4.1 Flash 1017 measured |
| MATH level 5 | 96.6% | Not measured | Only R1 0528 measured |
| LMArena Text: math | 1400 | Not measured | Only R1 0528 measured |
| LMArena Text: mathematical work | 1415 | Not measured | Only R1 0528 measured |
| Neither model has a result on the other 5 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | R1 0528 | DeepSeek V4.1 Flash 1017 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 84.9% | Not measured | Only R1 0528 measured |
| SimpleQA Verified | Not measured | 42.5% | Only DeepSeek V4.1 Flash 1017 measured |
| LMArena Text: science | 1430 | Not measured | Only R1 0528 measured |
| LMArena Text: medicine and healthcare | 1445 | Not measured | Only R1 0528 measured |
| LMArena Text: legal and government | 1419 | Not measured | Only R1 0528 measured |
| LMArena Text: business and finance | 1402 | Not measured | Only R1 0528 measured |
| Neither model has a result on the other 12 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | R1 0528 | DeepSeek V4.1 Flash 1017 | Leader |
|---|---|---|---|
| Design Arena: websites | 1149 | 1311 | DeepSeek V4.1 Flash 1017 |
| Design Arena: UI components | 1100 | 1303 | DeepSeek V4.1 Flash 1017 |
| Design Arena: game dev | 1097 | 1357 | DeepSeek V4.1 Flash 1017 |
| Design Arena: data viz | 1182 | 1330 | DeepSeek V4.1 Flash 1017 |
| Design Arena: 3D | 1110 | 1358 | DeepSeek V4.1 Flash 1017 |
| Design Arena: all code categories | 1138 | 1332 | DeepSeek V4.1 Flash 1017 |
| Design Arena: SVG | 1038 | Not measured | Only R1 0528 measured |
| Design Arena: ASCII art | Not measured | 1242 | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench design | Not measured | 83.4% | Only DeepSeek V4.1 Flash 1017 measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | R1 0528 | DeepSeek V4.1 Flash 1017 | Leader |
|---|---|---|---|
| Vals Index | Not measured | 61.3% | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench data analysis | Not measured | 79.5% | Only DeepSeek V4.1 Flash 1017 measured |
| IFBench | 39.6% | Not measured | Only R1 0528 measured |
| Long context reasoning (AA-LCR) | 55.7% | 82.7% | DeepSeek V4.1 Flash 1017 |
| Fiction.LiveBench | 75.0% | Not measured | Only R1 0528 measured |
| Creative writing (Lech Mazur) | 81.9% | Not measured | Only R1 0528 measured |
| LMArena Text | 1428 | Not measured | Only R1 0528 measured |
| LMArena Text: instruction following | 1382 | Not measured | Only R1 0528 measured |
| LMArena Text: creative writing | 1405 | Not measured | Only R1 0528 measured |
| LMArena Text: English | 1437 | Not measured | Only R1 0528 measured |
| LMArena Text: non-English | 1412 | Not measured | Only R1 0528 measured |
| LMArena Text: Chinese | 1442 | Not measured | Only R1 0528 measured |
| LMArena Text: German | 1404 | Not measured | Only R1 0528 measured |
| LMArena Text: Japanese | 1391 | Not measured | Only R1 0528 measured |
| LMArena Text: Korean | 1360 | Not measured | Only R1 0528 measured |
| LMArena Text: French | 1417 | Not measured | Only R1 0528 measured |
| LMArena Text: Spanish | 1411 | Not measured | Only R1 0528 measured |
| LMArena Text: Russian | 1423 | Not measured | Only R1 0528 measured |
| LMArena Text: Polish | 1428 | Not measured | Only R1 0528 measured |
| LMArena Text: multi-turn | 1405 | Not measured | Only R1 0528 measured |
| LMArena Text: longer queries | 1391 | Not measured | Only R1 0528 measured |
| LMArena Text: writing and language | 1402 | Not measured | Only R1 0528 measured |
| LMArena Text: entertainment and media | 1400 | Not measured | Only R1 0528 measured |
| Neither model has a result on the other 23 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | R1 0528 | DeepSeek V4.1 Flash 1017 | 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 R1 0528 | DeepSeek V4.1 Flash 1017 |
|---|---|---|
| Maker | DeepSeek | DeepSeek |
| Weights | Open weights | Open weights |
| Released | May 28, 2025 | October 17, 2026 |
| Context window | 164K | 1.05M |
| Input $/Mtok | $0.50 | $0.15 |
| Output $/Mtok | $2.18 | $0.60 |
| Coding index | Not benchmarked | 70.3 |
| Independent measurements behind it | 0 | 4 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is R1 0528 better than DeepSeek V4.1 Flash 1017 for coding?
Only DeepSeek V4.1 Flash 1017 has a coding score, 70.3 on the Vibe Coding Index; R1 0528 has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, R1 0528 or DeepSeek V4.1 Flash 1017?
R1 0528 costs $0.50 per million input tokens and $2.18 per million output tokens; DeepSeek V4.1 Flash 1017 costs $0.15 and $0.60. Cost per solved task is known for neither of them.
Which has the bigger context window, R1 0528 or DeepSeek V4.1 Flash 1017?
DeepSeek V4.1 Flash 1017, with 1.05M tokens against 164K.
How many benchmarks is the R1 0528 vs DeepSeek V4.1 Flash 1017 comparison based on?
12 benchmarks have a result for both models as of October 22, 2026. R1 0528's coding index rests on 0 independent benchmarks and DeepSeek V4.1 Flash 1017's on 4. Where only one model has a result, the page says so instead of naming a winner.