DeepSeek V3.2 Exp vs Solar Pro 4
Solar Pro 4 leads DeepSeek V3.2 Exp on the Artificial Analysis Intelligence Index, 28.2 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; 40 benchmarks have a result for both.
DeepSeek V3.2 Exp or Solar Pro 4: which to pick
Neither model has a coding score yet, 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) and work measured by LMArena WebDev or LMArena WebDev: HTML, where it leads.
- Shared results
- 40
- DeepSeek V3.2 Exp only
- 19
- Solar Pro 4 only
- 5
- 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
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
Solar Pro 4
Solar Pro 4 leads, 28.2 against 16.6.
Same board, both measured
Web design
Design Arena: websites
DeepSeek V3.2 Exp
DeepSeek V3.2 Exp leads, 1179 against 1176.
Same board, both measured
Long documents
Listed context window
Solar Pro 4
Solar Pro 4 takes 524K tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Solar Pro 4
DeepSeek V3.2 Exp $0.0005, Solar Pro 4 $0.0004 per turn.
List prices
At a glance
- Coding index
- DeepSeek V3.2 Exp not benchmarked, Solar Pro 4 not benchmarked.
- Widest category
- Intelligence, 11.6 points apart on average over 1 shared benchmark.
- Widest single gap
- LMArena Text: Polish: DeepSeek V3.2 Exp 1435 against Solar Pro 4 1304, 21.8 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.2 Exp $0.41 against Solar Pro 4 $0.60 per million output tokens, 1.5x apart.
- Context window
- DeepSeek V3.2 Exp takes 164K tokens, Solar Pro 4 524K.
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 | Solar Pro 4 | Ties |
|---|---|---|---|---|
| Coding | 2 | 2 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 2 | 3 | ||
| Math | 2 | 0 | ||
| Knowledge | 4 | 0 | ||
| Web design | 2 | 4 | ||
| General | 17 | 1 | ||
| All shared boards | 29 | 11 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index22
| Benchmark | DeepSeek V3.2 Exp | Solar Pro 4 | Leader |
|---|---|---|---|
| Aider polyglot | 74.2% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena WebDev | 1272 | 1371 | Solar Pro 4 |
| AA Coding Index | Not measured | 52.7 | Only Solar Pro 4 measured |
| LiveCodeBench (Artificial Analysis run) | 78.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| SciCode | Not measured | 48.0% | Only Solar Pro 4 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 | 179.3 | Only Solar Pro 4 measured |
| LMArena Text: coding | 1438 | 1438 | DeepSeek V3.2 Exp |
| LMArena Text: software and IT | 1438 | 1431 | DeepSeek V3.2 Exp |
| LMArena WebDev: React | Not measured | 1363 | Only Solar Pro 4 measured |
| LMArena WebDev: HTML | 1281 | 1371 | Solar Pro 4 |
| Neither model has a result on the other 27 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3.2 Exp | Solar Pro 4 | Leader |
|---|---|---|---|
| AA Intelligence Index | 16.6 | 28.2 | Solar Pro 4 |
| Epoch Capabilities Index | 144.9 | Not measured | Only DeepSeek V3.2 Exp measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | Solar Pro 4 | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | Not measured | 1105 | Only Solar Pro 4 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 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index23
| Benchmark | DeepSeek V3.2 Exp | Solar Pro 4 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 79.7% | 89.1% | Solar Pro 4 |
| Humanity's Last Exam (Artificial Analysis run) | 14.9% | 29.8% | Solar Pro 4 |
| LMArena Text: hard prompts | 1429 | 1400 | DeepSeek V3.2 Exp |
| LMArena Text: expert | 1422 | 1428 | Solar Pro 4 |
| LMArena Text: hard prompts in English | 1444 | 1427 | DeepSeek V3.2 Exp |
| Kagi LLM Benchmark | 52.2% | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 16 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V3.2 Exp | Solar Pro 4 | 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 |
| LMArena Text: math | 1422 | 1421 | DeepSeek V3.2 Exp |
| LMArena Text: mathematical work | 1433 | 1427 | DeepSeek V3.2 Exp |
| Neither model has a result on the other 9 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | DeepSeek V3.2 Exp | Solar Pro 4 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 85.0% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: science | 1443 | 1404 | DeepSeek V3.2 Exp |
| LMArena Text: medicine and healthcare | 1468 | 1374 | DeepSeek V3.2 Exp |
| LMArena Text: legal and government | 1419 | 1383 | DeepSeek V3.2 Exp |
| LMArena Text: business and finance | 1412 | 1381 | DeepSeek V3.2 Exp |
| Neither model has a result on the other 13 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index24
| Benchmark | DeepSeek V3.2 Exp | Solar Pro 4 | Leader |
|---|---|---|---|
| Design Arena: websites | 1179 | 1176 | DeepSeek V3.2 Exp |
| Design Arena: UI components | 1164 | 1147 | DeepSeek V3.2 Exp |
| Design Arena: game dev | 1144 | 1169 | Solar Pro 4 |
| Design Arena: data viz | 1152 | 1178 | Solar Pro 4 |
| Design Arena: 3D | 1148 | 1178 | Solar Pro 4 |
| Design Arena: all code categories | 1170 | 1181 | Solar Pro 4 |
| 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 index171
| Benchmark | DeepSeek V3.2 Exp | Solar Pro 4 | Leader |
|---|---|---|---|
| IFBench | 54.1% | Not measured | Only DeepSeek V3.2 Exp measured |
| Long context reasoning (AA-LCR) | 72.3% | 70.7% | DeepSeek V3.2 Exp |
| 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 | 1388 | DeepSeek V3.2 Exp |
| LMArena Text: instruction following | 1401 | 1375 | DeepSeek V3.2 Exp |
| LMArena Text: creative writing | 1404 | 1317 | DeepSeek V3.2 Exp |
| LMArena Text: English | 1441 | 1414 | DeepSeek V3.2 Exp |
| LMArena Text: non-English | 1404 | 1363 | DeepSeek V3.2 Exp |
| LMArena Text: Chinese | 1461 | 1422 | DeepSeek V3.2 Exp |
| LMArena Text: German | 1440 | 1358 | DeepSeek V3.2 Exp |
| LMArena Text: Japanese | 1331 | 1320 | DeepSeek V3.2 Exp |
| LMArena Text: Korean | 1372 | 1383 | Solar Pro 4 |
| LMArena Text: French | 1434 | 1400 | DeepSeek V3.2 Exp |
| LMArena Text: Spanish | 1441 | 1409 | DeepSeek V3.2 Exp |
| LMArena Text: Russian | 1412 | 1357 | DeepSeek V3.2 Exp |
| LMArena Text: Polish | 1435 | 1304 | DeepSeek V3.2 Exp |
| LMArena Text: multi-turn | 1422 | 1385 | DeepSeek V3.2 Exp |
| LMArena Text: longer queries | 1418 | 1382 | DeepSeek V3.2 Exp |
| LMArena Text: writing and language | 1407 | 1344 | DeepSeek V3.2 Exp |
| LMArena Text: entertainment and media | 1407 | 1332 | DeepSeek V3.2 Exp |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | Solar Pro 4 | 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
Solar Pro 4 costs less
Repository review
50K input + 3K output tokens
Solar Pro 4 costs less
Agent loop
200K input + 10K output tokens
Solar Pro 4 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 | Upstage Solar Pro 4 |
|---|---|---|
| Maker | DeepSeek | Upstage |
| Weights | Open weights | No public weights listed |
| Released | September 29, 2025 | August 10, 2026 |
| Context window | 164K | 524K |
| Input $/Mtok | $0.27 | $0.15 |
| Output $/Mtok | $0.41 | $0.60 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.2 Exp better than Solar Pro 4 for coding?
Neither model has a coding score yet, 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) and work measured by LMArena WebDev or LMArena WebDev: HTML, where it leads.
Which is cheaper, DeepSeek V3.2 Exp or Solar Pro 4?
DeepSeek V3.2 Exp costs $0.27 per million input tokens and $0.41 per million output tokens; Solar Pro 4 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 Solar Pro 4?
Solar Pro 4, with 524K tokens against 164K.
How many benchmarks is the DeepSeek V3.2 Exp vs Solar Pro 4 comparison based on?
40 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 Solar Pro 4's on 1. Where only one model has a result, the page says so instead of naming a winner.