DeepSeek V3.1 vs R1 0528
DeepSeek V3.1 leads R1 0528 on the Artificial Analysis Intelligence Index, 13.7 to 13.1, and DeepSeek V3.1 costs 1.5x less per output token ($1.50 vs $2.18 per million). Updated October 22, 2026; 8 benchmarks have a result for both.
DeepSeek V3.1 or R1 0528: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick DeepSeek V3.1 for output tokens 1.5x cheaper ($1.50 vs $2.18 per million) and work measured by Design Arena: websites or Design Arena: UI components, where it leads.
- Shared results
- 8
- DeepSeek V3.1 only
- 3
- R1 0528 only
- 52
- Like-for-like categories
- 2 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
DeepSeek V3.1
DeepSeek V3.1 leads, 13.7 against 13.1.
Same board, both measured
Web design
Design Arena: websites
R1 0528
R1 0528 leads, 1149 against 1122.
Same board, both measured
Long documents
Listed context window
No call
Both take 164K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V3.1
DeepSeek V3.1 $0.0013, R1 0528 $0.0016 per turn.
List prices
At a glance
- Coding index
- DeepSeek V3.1 not benchmarked, R1 0528 not benchmarked.
- Widest category
- Web design, 4.8 points apart on average over 7 shared benchmarks.
- Widest single gap
- Design Arena: data viz: DeepSeek V3.1 1096 against R1 0528 1182, 10.8 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.1 $1.50 against R1 0528 $2.18 per million output tokens, 1.5x apart.
- Context window
- Both take 164K 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 V3.1 | Bars | R1 0528 | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| Web design | 0 | 7 | ||
| All shared boards | 1 | 7 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index7 boards, none shared
| Benchmark | DeepSeek V3.1 | R1 0528 | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 71.4% | Only R1 0528 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 77.0% | Only R1 0528 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 15.9% | Only R1 0528 measured |
| WeirdML | Not measured | 41.6% | Only R1 0528 measured |
| ALE-Bench | Not measured | 804.1 | Only R1 0528 measured |
| LMArena Text: coding | Not measured | 1427 | Only R1 0528 measured |
| LMArena Text: software and IT | Not measured | 1435 | Only R1 0528 measured |
| Neither model has a result on the other 32 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V3.1 | R1 0528 | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.7 | 13.1 | DeepSeek V3.1 |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | DeepSeek V3.1 | R1 0528 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 36.5% | Only R1 0528 measured |
| METR time horizons | Not measured | 53.8% | Only R1 0528 measured |
| DeepResearch Bench | Not measured | 35.1% | Only R1 0528 measured |
| BrowseComp-Plus | Not measured | 16.4% | Only R1 0528 measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
| Benchmark | DeepSeek V3.1 | R1 0528 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 81.3% | Only R1 0528 measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 15.8% | Only R1 0528 measured |
| GPQA Diamond (Epoch AI run) | Not measured | 76.3% | Only R1 0528 measured |
| ARC-AGI | Not measured | 21.2% | Only R1 0528 measured |
| ARC-AGI-2 | Not measured | 1.1% | Only R1 0528 measured |
| SimpleBench | Not measured | 40.8% | Only R1 0528 measured |
| LMArena Text: hard prompts | Not measured | 1415 | Only R1 0528 measured |
| LMArena Text: expert | Not measured | 1394 | Only R1 0528 measured |
| LMArena Text: hard prompts in English | Not measured | 1422 | Only R1 0528 measured |
| Kagi LLM Benchmark | 53.2% | Not measured | Only DeepSeek V3.1 measured |
| Neither model has a result on the other 12 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | DeepSeek V3.1 | R1 0528 | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 76.0 | Only R1 0528 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 76.0% | Only R1 0528 measured |
| OTIS Mock AIME | Not measured | 66.4% | Only R1 0528 measured |
| MATH level 5 | Not measured | 96.6% | Only R1 0528 measured |
| LMArena Text: math | Not measured | 1400 | Only R1 0528 measured |
| LMArena Text: mathematical work | Not measured | 1415 | Only R1 0528 measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | DeepSeek V3.1 | R1 0528 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 84.9% | Only R1 0528 measured |
| LMArena Text: science | Not measured | 1430 | Only R1 0528 measured |
| LMArena Text: medicine and healthcare | Not measured | 1445 | Only R1 0528 measured |
| LMArena Text: legal and government | Not measured | 1419 | Only R1 0528 measured |
| LMArena Text: business and finance | Not measured | 1402 | Only R1 0528 measured |
| Neither model has a result on the other 13 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index07
| Benchmark | DeepSeek V3.1 | R1 0528 | Leader |
|---|---|---|---|
| Design Arena: websites | 1122 | 1149 | R1 0528 |
| Design Arena: UI components | 1085 | 1100 | R1 0528 |
| Design Arena: game dev | 1084 | 1097 | R1 0528 |
| Design Arena: data viz | 1096 | 1182 | R1 0528 |
| Design Arena: 3D | 1077 | 1110 | R1 0528 |
| Design Arena: all code categories | 1112 | 1138 | R1 0528 |
| Design Arena: SVG | 967 | 1038 | R1 0528 |
| Neither model has a result on the other 2 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index23 boards, none shared
| Benchmark | DeepSeek V3.1 | R1 0528 | Leader |
|---|---|---|---|
| IFBench | Not measured | 39.6% | Only R1 0528 measured |
| Long context reasoning (AA-LCR) | Not measured | 55.7% | Only R1 0528 measured |
| DTBench | 82.7% | Not measured | Only DeepSeek V3.1 measured |
| LMCA | 24.3% | Not measured | Only DeepSeek V3.1 measured |
| Fiction.LiveBench | Not measured | 75.0% | Only R1 0528 measured |
| Creative writing (Lech Mazur) | Not measured | 81.9% | Only R1 0528 measured |
| LMArena Text | Not measured | 1428 | Only R1 0528 measured |
| LMArena Text: instruction following | Not measured | 1382 | Only R1 0528 measured |
| LMArena Text: creative writing | Not measured | 1405 | Only R1 0528 measured |
| LMArena Text: English | Not measured | 1437 | Only R1 0528 measured |
| LMArena Text: non-English | Not measured | 1412 | Only R1 0528 measured |
| LMArena Text: Chinese | Not measured | 1442 | Only R1 0528 measured |
| LMArena Text: German | Not measured | 1404 | Only R1 0528 measured |
| LMArena Text: Japanese | Not measured | 1391 | Only R1 0528 measured |
| LMArena Text: Korean | Not measured | 1360 | Only R1 0528 measured |
| LMArena Text: French | Not measured | 1417 | Only R1 0528 measured |
| LMArena Text: Spanish | Not measured | 1411 | Only R1 0528 measured |
| LMArena Text: Russian | Not measured | 1423 | Only R1 0528 measured |
| LMArena Text: Polish | Not measured | 1428 | Only R1 0528 measured |
| LMArena Text: multi-turn | Not measured | 1405 | Only R1 0528 measured |
| LMArena Text: longer queries | Not measured | 1391 | Only R1 0528 measured |
| LMArena Text: writing and language | Not measured | 1402 | Only R1 0528 measured |
| LMArena Text: entertainment and media | Not measured | 1400 | 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 | DeepSeek V3.1 | R1 0528 | 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 V3.1 costs less
Repository review
50K input + 3K output tokens
R1 0528 costs less
Agent loop
200K input + 10K output tokens
R1 0528 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 V3.1 | DeepSeek R1 0528 |
|---|---|---|
| Maker | DeepSeek | DeepSeek |
| Weights | Open weights | Open weights |
| Released | August 21, 2025 | May 28, 2025 |
| Context window | 164K | 164K |
| Input $/Mtok | $0.55 | $0.50 |
| Output $/Mtok | $1.50 | $2.18 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.1 better than R1 0528 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick DeepSeek V3.1 for output tokens 1.5x cheaper ($1.50 vs $2.18 per million) and work measured by Design Arena: websites or Design Arena: UI components, where it leads.
Which is cheaper, DeepSeek V3.1 or R1 0528?
DeepSeek V3.1 costs $0.55 per million input tokens and $1.50 per million output tokens; R1 0528 costs $0.50 and $2.18. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V3.1 or R1 0528?
Neither: both take 164K tokens.
How many benchmarks is the DeepSeek V3.1 vs R1 0528 comparison based on?
8 benchmarks have a result for both models as of October 22, 2026. DeepSeek V3.1's coding index rests on 0 independent benchmarks and R1 0528's on 0. Where only one model has a result, the page says so instead of naming a winner.