DeepSeek V3.2 Exp vs DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 leads DeepSeek V3.2 Exp on the Artificial Analysis Intelligence Index, 36.0 to 16.6, and DeepSeek V3.2 Exp costs 4.8x less per output token ($0.41 vs $1.98 per million). Updated October 22, 2026; 35 benchmarks have a result for both.
DeepSeek V3.2 Exp or DeepSeek V4 Pro 0813: which to pick
Only DeepSeek V4 Pro 0813 has a coding score, 56.1 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 4.8x cheaper ($0.41 vs $1.98 per million) and work measured by LMArena Text: medicine and healthcare or LMArena Text: Spanish, where it leads.
- Shared results
- 35
- DeepSeek V3.2 Exp only
- 24
- DeepSeek V4 Pro 0813 only
- 43
- 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 Pro 0813 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 leads, 36.0 against 16.6.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only DeepSeek V4 Pro 0813 has a AA Agentic Index result, 41.3.
Directional only
Reasoning
GPQA Diamond
No call
Only DeepSeek V4 Pro 0813 has a GPQA Diamond result, 92.4%.
Directional only
Knowledge
MMLU Pro
No call
Only DeepSeek V4 Pro 0813 has a MMLU Pro result, 87.0%.
Directional only
Web design
Design Arena: websites
No call
Only DeepSeek V3.2 Exp has a Design Arena: websites result, 1178.
Directional only
General
Vals Index
No call
Only DeepSeek V4 Pro 0813 has a Vals Index result, 53.0%.
Directional only
Long documents
Listed context window
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 takes 1.05M tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V3.2 Exp
DeepSeek V3.2 Exp $0.0005, DeepSeek V4 Pro 0813 $0.0016 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only DeepSeek V4 Pro 0813 has the number, $0.10.
Directional only
At a glance
- Coding index
- DeepSeek V3.2 Exp not benchmarked, DeepSeek V4 Pro 0813 56.1 from 5 benchmarks.
- Widest category
- Coding, 25.5 points apart on average over 5 shared benchmarks.
- Widest single gap
- LMArena WebDev: HTML: DeepSeek V3.2 Exp 1281 against DeepSeek V4 Pro 0813 1566, 47.4 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.2 Exp $0.41 against DeepSeek V4 Pro 0813 $1.98 per million output tokens, 4.8x apart.
- Context window
- DeepSeek V3.2 Exp takes 164K tokens, DeepSeek V4 Pro 0813 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 Pro 0813 | Ties |
|---|---|---|---|---|
| Coding | 0 | 5 | ||
| Intelligence | 0 | 2 | ||
| Reasoning | 0 | 5 | ||
| Math | 0 | 2 | ||
| Knowledge | 1 | 3 | ||
| General | 1 | 16 | ||
| All shared boards | 2 | 33 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index05
| Benchmark | DeepSeek V3.2 Exp | DeepSeek V4 Pro 0813 | Leader |
|---|---|---|---|
| Aider polyglot | 74.2% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena WebDev | 1272 | 1582 | DeepSeek V4 Pro 0813 |
| AA Coding Index | Not measured | 68.8 | Only DeepSeek V4 Pro 0813 measured |
| LiveCodeBench (Vals AI) | Not measured | 87.5% | Only DeepSeek V4 Pro 0813 measured |
| SWE-bench Verified (Vals AI) | Not measured | 96.4% | Only DeepSeek V4 Pro 0813 measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 14.1% | Only DeepSeek V4 Pro 0813 measured |
| Vibe Code Bench (Vals AI) | Not measured | 82.3% | Only DeepSeek V4 Pro 0813 measured |
| Code migration (Vals AI) | Not measured | 41.5% | Only DeepSeek V4 Pro 0813 measured |
| ProgramBench (Vals AI) | Not measured | 0.0% | Only DeepSeek V4 Pro 0813 measured |
| IOI (Vals AI) | Not measured | 51.6% | Only DeepSeek V4 Pro 0813 measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 54.7% | Only DeepSeek V4 Pro 0813 measured |
| Vibe Code Bench 1-100 (Vals AI) | Not measured | 17.5% | Only DeepSeek V4 Pro 0813 measured |
| SkillsBench (Vals AI) | Not measured | 53.8% | Only DeepSeek V4 Pro 0813 measured |
| CyberBench (Vals AI) | Not measured | 72.9% | Only DeepSeek V4 Pro 0813 measured |
| LiveCodeBench (Artificial Analysis run) | 78.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| SciCode | Not measured | 51.0% | Only DeepSeek V4 Pro 0813 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 31.1% | Not measured | Only DeepSeek V3.2 Exp measured |
| FrontierCode | Not measured | 28.5% | Only DeepSeek V4 Pro 0813 measured |
| WeirdML | 39.5% | 66.2% | DeepSeek V4 Pro 0813 |
| ALE-Bench | Not measured | 1403.2 | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: coding | 1438 | 1469 | DeepSeek V4 Pro 0813 |
| LMArena Text: software and IT | 1438 | 1462 | DeepSeek V4 Pro 0813 |
| LMArena WebDev: React | Not measured | 1588 | Only DeepSeek V4 Pro 0813 measured |
| LMArena WebDev: HTML | 1281 | 1566 | DeepSeek V4 Pro 0813 |
| Neither model has a result on the other 15 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V3.2 Exp | DeepSeek V4 Pro 0813 | Leader |
|---|---|---|---|
| AA Intelligence Index | 16.6 | 36.0 | DeepSeek V4 Pro 0813 |
| Epoch Capabilities Index | 144.9 | 155.6 | DeepSeek V4 Pro 0813 |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index12 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | DeepSeek V4 Pro 0813 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 41.3 | Only DeepSeek V4 Pro 0813 measured |
| SREBench (Vals AI) | Not measured | 1.9% | Only DeepSeek V4 Pro 0813 measured |
| Finance Agent (Vals AI) | Not measured | 50.4% | Only DeepSeek V4 Pro 0813 measured |
| Tax Agent Bench (Vals AI) | Not measured | 26.8% | Only DeepSeek V4 Pro 0813 measured |
| Legal Research Bench (Vals AI) | Not measured | 40.9% | Only DeepSeek V4 Pro 0813 measured |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 10.8% | Only DeepSeek V4 Pro 0813 measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 52.8% | Only DeepSeek V4 Pro 0813 measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 4.3% | Only DeepSeek V4 Pro 0813 measured |
| τ²-Bench Telecom | 33.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| APEX-Agents | Not measured | 47.3% | Only DeepSeek V4 Pro 0813 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 index05
| Benchmark | DeepSeek V3.2 Exp | DeepSeek V4 Pro 0813 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 92.4% | Only DeepSeek V4 Pro 0813 measured |
| GPQA Diamond (Artificial Analysis run) | 79.7% | 92.8% | DeepSeek V4 Pro 0813 |
| Humanity's Last Exam (Artificial Analysis run) | 14.9% | 41.0% | DeepSeek V4 Pro 0813 |
| GPQA Diamond (Epoch AI run) | Not measured | 91.7% | Only DeepSeek V4 Pro 0813 measured |
| ARC-AGI | Not measured | 90.5% | Only DeepSeek V4 Pro 0813 measured |
| ARC-AGI-2 | Not measured | 61.3% | Only DeepSeek V4 Pro 0813 measured |
| Chess puzzles | Not measured | 46.0% | Only DeepSeek V4 Pro 0813 measured |
| Mystery game puzzles | Not measured | 43.0% | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: hard prompts | 1429 | 1458 | DeepSeek V4 Pro 0813 |
| LMArena Text: expert | 1422 | 1459 | DeepSeek V4 Pro 0813 |
| LMArena Text: hard prompts in English | 1444 | 1463 | DeepSeek V4 Pro 0813 |
| Kagi LLM Benchmark | 52.2% | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 10 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V3.2 Exp | DeepSeek V4 Pro 0813 | 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 | 98.6% | Only DeepSeek V4 Pro 0813 measured |
| FrontierMath, tiers 1 to 3 | Not measured | 64.6% | Only DeepSeek V4 Pro 0813 measured |
| FrontierMath, tier 4 | Not measured | 26.8% | Only DeepSeek V4 Pro 0813 measured |
| ProofBench | Not measured | 50.0% | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: math | 1422 | 1441 | DeepSeek V4 Pro 0813 |
| LMArena Text: mathematical work | 1433 | 1443 | DeepSeek V4 Pro 0813 |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index13
| Benchmark | DeepSeek V3.2 Exp | DeepSeek V4 Pro 0813 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 87.0% | Only DeepSeek V4 Pro 0813 measured |
| LegalBench (Vals AI) | Not measured | 82.4% | Only DeepSeek V4 Pro 0813 measured |
| CorpFin (Vals AI) | Not measured | 65.4% | Only DeepSeek V4 Pro 0813 measured |
| TaxEval (Vals AI) | Not measured | 73.1% | Only DeepSeek V4 Pro 0813 measured |
| MedCode (Vals AI) | Not measured | 42.5% | Only DeepSeek V4 Pro 0813 measured |
| MMLU Pro (Artificial Analysis run) | 85.0% | Not measured | Only DeepSeek V3.2 Exp measured |
| SimpleQA Verified | Not measured | 52.9% | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: science | 1443 | 1457 | DeepSeek V4 Pro 0813 |
| LMArena Text: medicine and healthcare | 1468 | 1446 | DeepSeek V3.2 Exp |
| LMArena Text: legal and government | 1419 | 1459 | DeepSeek V4 Pro 0813 |
| LMArena Text: business and finance | 1412 | 1442 | DeepSeek V4 Pro 0813 |
| Neither model has a result on the other 7 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | DeepSeek V4 Pro 0813 | Leader |
|---|---|---|---|
| Design Arena: websites | 1178 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: UI components | 1164 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: game dev | 1144 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: data viz | 1152 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: 3D | 1148 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: all code categories | 1170 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: SVG | 1031 | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index116
| Benchmark | DeepSeek V3.2 Exp | DeepSeek V4 Pro 0813 | Leader |
|---|---|---|---|
| Vals Index | Not measured | 53.0% | Only DeepSeek V4 Pro 0813 measured |
| MedScribe (Vals AI) | Not measured | 80.2% | Only DeepSeek V4 Pro 0813 measured |
| IFBench | 54.1% | Not measured | Only DeepSeek V3.2 Exp measured |
| Long context reasoning (AA-LCR) | 72.3% | 80.3% | DeepSeek V4 Pro 0813 |
| DTBench | 87.7% | 93.9% | DeepSeek V4 Pro 0813 |
| LMCA | 29.1% | 45.5% | DeepSeek V4 Pro 0813 |
| 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 | 1446 | DeepSeek V4 Pro 0813 |
| LMArena Text: instruction following | 1401 | 1448 | DeepSeek V4 Pro 0813 |
| LMArena Text: creative writing | 1404 | 1439 | DeepSeek V4 Pro 0813 |
| LMArena Text: English | 1441 | 1457 | DeepSeek V4 Pro 0813 |
| LMArena Text: non-English | 1404 | 1431 | DeepSeek V4 Pro 0813 |
| LMArena Text: Chinese | 1461 | 1481 | DeepSeek V4 Pro 0813 |
| 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 | 1449 | DeepSeek V4 Pro 0813 |
| LMArena Text: French | 1434 | 1446 | DeepSeek V4 Pro 0813 |
| LMArena Text: Spanish | 1441 | 1435 | DeepSeek V3.2 Exp |
| LMArena Text: Russian | 1412 | 1448 | DeepSeek V4 Pro 0813 |
| LMArena Text: Polish | 1435 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: multi-turn | 1422 | 1454 | DeepSeek V4 Pro 0813 |
| LMArena Text: longer queries | 1418 | 1458 | DeepSeek V4 Pro 0813 |
| LMArena Text: writing and language | 1407 | 1443 | DeepSeek V4 Pro 0813 |
| LMArena Text: entertainment and media | 1407 | 1424 | DeepSeek V4 Pro 0813 |
| 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 Pro 0813 | 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 V3.2 Exp costs less
Repository review
50K input + 3K output tokens
DeepSeek V3.2 Exp costs less
Agent loop
200K input + 10K output tokens
DeepSeek V3.2 Exp 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 Pro 0813 |
|---|---|---|
| Maker | DeepSeek | DeepSeek |
| Weights | Open weights | Open weights |
| Released | September 29, 2025 | August 12, 2026 |
| Context window | 164K | 1.05M |
| Input $/Mtok | $0.27 | $0.66 |
| Output $/Mtok | $0.41 | $1.98 |
| Coding index | Not benchmarked | 56.1 |
| Independent measurements behind it | 1 | 5 |
| Cost per solved task | Not computable | $0.10 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.2 Exp better than DeepSeek V4 Pro 0813 for coding?
Only DeepSeek V4 Pro 0813 has a coding score, 56.1 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 4.8x cheaper ($0.41 vs $1.98 per million) and work measured by LMArena Text: medicine and healthcare or LMArena Text: Spanish, where it leads.
Which is cheaper, DeepSeek V3.2 Exp or DeepSeek V4 Pro 0813?
DeepSeek V3.2 Exp costs $0.27 per million input tokens and $0.41 per million output tokens; DeepSeek V4 Pro 0813 costs $0.66 and $1.98. Cost per solved task is known for DeepSeek V4 Pro 0813 only.
Which has the bigger context window, DeepSeek V3.2 Exp or DeepSeek V4 Pro 0813?
DeepSeek V4 Pro 0813, with 1.05M tokens against 164K.
How many benchmarks is the DeepSeek V3.2 Exp vs DeepSeek V4 Pro 0813 comparison based on?
35 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 Pro 0813's on 5. Where only one model has a result, the page says so instead of naming a winner.