DeepSeek V3.2 Exp vs DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 leads DeepSeek V3.2 Exp on the Artificial Analysis Intelligence Index, 39.5 to 16.6, and DeepSeek V3.2 Exp costs 1.5x less per output token ($0.41 vs $0.60 per million). Updated October 22, 2026; 11 benchmarks have a result for both.
DeepSeek V3.2 Exp 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; DeepSeek V3.2 Exp has none, so the two cannot be ranked on coding.
Pick DeepSeek V3.2 Exp for output tokens 1.5x cheaper ($0.41 vs $0.60 per million).
- Shared results
- 11
- DeepSeek V3.2 Exp only
- 48
- DeepSeek V4.1 Flash 1017 only
- 28
- Like-for-like categories
- 5 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 16.6.
Same board, both measured
Web design
Design Arena: websites
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 leads, 1311 against 1178.
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
DeepSeek V3.2 Exp $0.0005, DeepSeek V4.1 Flash 1017 $0.0004 per turn.
List prices
At a glance
- Coding index
- DeepSeek V3.2 Exp not benchmarked, DeepSeek V4.1 Flash 1017 70.3 from 4 benchmarks.
- Widest category
- Coding, 54.6 points apart on average over 2 shared benchmarks.
- Widest single gap
- LMArena WebDev: DeepSeek V3.2 Exp 1272 against DeepSeek V4.1 Flash 1017 1664, 56.1 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.2 Exp $0.41 against DeepSeek V4.1 Flash 1017 $0.60 per million output tokens, 1.5x apart.
- Context window
- DeepSeek V3.2 Exp 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 | DeepSeek V3.2 Exp | Bars | DeepSeek V4.1 Flash 1017 | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 1 | ||
| Web design | 0 | 6 | ||
| General | 0 | 1 | ||
| All shared boards | 0 | 11 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | DeepSeek V3.2 Exp | DeepSeek V4.1 Flash 1017 | Leader |
|---|---|---|---|
| Aider polyglot | 74.2% | Not measured | Only DeepSeek V3.2 Exp measured |
| LiveBench coding | Not measured | 96.0% | Only DeepSeek V4.1 Flash 1017 measured |
| LMArena WebDev | 1272 | 1664 | DeepSeek V4.1 Flash 1017 |
| 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) | 78.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| SciCode | Not measured | 55.9% | Only DeepSeek V4.1 Flash 1017 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 31.1% | Not measured | Only DeepSeek V3.2 Exp measured |
| WeirdML | 39.5% | Not measured | Only DeepSeek V3.2 Exp measured |
| ALE-Bench | Not measured | 1622.0 | Only DeepSeek V4.1 Flash 1017 measured |
| LMArena Text: coding | 1438 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: software and IT | 1438 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena WebDev: React | Not measured | 1667 | Only DeepSeek V4.1 Flash 1017 measured |
| LMArena WebDev: HTML | 1281 | 1687 | 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 index01
| Benchmark | DeepSeek V3.2 Exp | DeepSeek V4.1 Flash 1017 | Leader |
|---|---|---|---|
| AA Intelligence Index | 16.6 | 39.5 | DeepSeek V4.1 Flash 1017 |
| Epoch Capabilities Index | 144.9 | Not measured | Only DeepSeek V3.2 Exp measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index12 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | 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 | 33.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| TheAgentCompany | 42.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| Berkeley Function Calling Leaderboard (BFCL) | 56.7% | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 29 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3.2 Exp | DeepSeek V4.1 Flash 1017 | Leader |
|---|---|---|---|
| LiveBench reasoning | Not measured | 89.1% | Only DeepSeek V4.1 Flash 1017 measured |
| GPQA Diamond (Artificial Analysis run) | 79.7% | Not measured | Only DeepSeek V3.2 Exp measured |
| Humanity's Last Exam (Artificial Analysis run) | 14.9% | 43.3% | DeepSeek V4.1 Flash 1017 |
| GPQA Diamond (Epoch AI run) | Not measured | 93.2% | Only DeepSeek V4.1 Flash 1017 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 | 1429 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: expert | 1422 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: hard prompts in English | 1444 | Not measured | Only DeepSeek V3.2 Exp measured |
| Kagi LLM Benchmark | 52.2% | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 12 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | DeepSeek V4.1 Flash 1017 | Leader |
|---|---|---|---|
| AA Math Index | 87.7 | Not measured | Only DeepSeek V3.2 Exp measured |
| AIME 2025 (Artificial Analysis run) | 87.7% | Not measured | Only DeepSeek V3.2 Exp measured |
| OTIS Mock AIME | Not measured | 99.4% | Only DeepSeek V4.1 Flash 1017 measured |
| 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 |
| LMArena Text: math | 1422 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: mathematical work | 1433 | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 6 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | DeepSeek V4.1 Flash 1017 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 85.0% | Not measured | Only DeepSeek V3.2 Exp measured |
| SimpleQA Verified | Not measured | 42.5% | Only DeepSeek V4.1 Flash 1017 measured |
| LMArena Text: science | 1443 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: medicine and healthcare | 1468 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: legal and government | 1419 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: business and finance | 1412 | Not measured | Only DeepSeek V3.2 Exp 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 | DeepSeek V3.2 Exp | DeepSeek V4.1 Flash 1017 | Leader |
|---|---|---|---|
| Design Arena: websites | 1178 | 1311 | DeepSeek V4.1 Flash 1017 |
| Design Arena: UI components | 1164 | 1303 | DeepSeek V4.1 Flash 1017 |
| Design Arena: game dev | 1144 | 1357 | DeepSeek V4.1 Flash 1017 |
| Design Arena: data viz | 1152 | 1330 | DeepSeek V4.1 Flash 1017 |
| Design Arena: 3D | 1148 | 1358 | DeepSeek V4.1 Flash 1017 |
| Design Arena: all code categories | 1170 | 1332 | DeepSeek V4.1 Flash 1017 |
| Design Arena: SVG | 1031 | Not measured | Only DeepSeek V3.2 Exp 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 | DeepSeek V3.2 Exp | 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 | 54.1% | Not measured | Only DeepSeek V3.2 Exp measured |
| Long context reasoning (AA-LCR) | 72.3% | 82.7% | DeepSeek V4.1 Flash 1017 |
| DTBench | 87.7% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMCA | 29.1% | Not measured | Only DeepSeek V3.2 Exp measured |
| Fiction.LiveBench | 83.3% | Not measured | Only DeepSeek V3.2 Exp measured |
| CL-bench | 13.2% | Not measured | Only DeepSeek V3.2 Exp measured |
| CL-bench Life | 9.5% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text | 1425 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: instruction following | 1401 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: creative writing | 1404 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: English | 1441 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: non-English | 1404 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: Chinese | 1461 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: German | 1440 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: Japanese | 1331 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: Korean | 1372 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: French | 1434 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: Spanish | 1441 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: Russian | 1412 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: Polish | 1435 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: multi-turn | 1422 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: longer queries | 1418 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: writing and language | 1407 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: entertainment and media | 1407 | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 20 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | 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 V3.2 Exp | DeepSeek V4.1 Flash 1017 |
|---|---|---|
| Maker | DeepSeek | DeepSeek |
| Weights | Open weights | Open weights |
| Released | September 29, 2025 | October 17, 2026 |
| Context window | 164K | 1.05M |
| Input $/Mtok | $0.27 | $0.15 |
| Output $/Mtok | $0.41 | $0.60 |
| Coding index | Not benchmarked | 70.3 |
| Independent measurements behind it | 1 | 4 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.2 Exp 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; DeepSeek V3.2 Exp has none, so the two cannot be ranked on coding. Pick DeepSeek V3.2 Exp for output tokens 1.5x cheaper ($0.41 vs $0.60 per million).
Which is cheaper, DeepSeek V3.2 Exp or DeepSeek V4.1 Flash 1017?
DeepSeek V3.2 Exp costs $0.27 per million input tokens and $0.41 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, DeepSeek V3.2 Exp or DeepSeek V4.1 Flash 1017?
DeepSeek V4.1 Flash 1017, with 1.05M tokens against 164K.
How many benchmarks is the DeepSeek V3.2 Exp vs DeepSeek V4.1 Flash 1017 comparison based on?
11 benchmarks have a result for both models as of October 22, 2026. DeepSeek V3.2 Exp's coding index rests on 1 independent benchmark 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.