DeepSeek V3.2 Exp vs GPT-5 Mini
Updated October 21, 2026. 51 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick GPT-5 Mini for coding: it scores 59.8 on the Vibe Coding Index against 55.0 for DeepSeek V3.2 Exp, though on 1 benchmark against 2.
Pick DeepSeek V3.2 Exp when output tokens cost 4.9x less, or when what matters is LMArena Text: coding or LMArena Text: software and IT, where it leads.
- Shared results
- 51
- DeepSeek V3.2 Exp only
- 6
- GPT-5 Mini only
- 47
- Like-for-like categories
- 8 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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, four public coding benchmarks
GPT-5 Mini
GPT-5 Mini scores 59.8 against 55.0.
2 and 1 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-5 Mini
GPT-5 Mini leads, 20.6 against 16.6.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GPT-5 Mini has a GPQA Diamond result, 80.3%.
Directional only
Math
AIME
No call
Only GPT-5 Mini has a AIME result, 91.5%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5 Mini has a MMLU Pro result, 82.2%.
Directional only
Web design
Design Arena: websites
DeepSeek V3.2 Exp
DeepSeek V3.2 Exp leads, 1179 against 1125.
Same board, both measured
Long documents
Listed context window
GPT-5 Mini
GPT-5 Mini takes 400K tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V3.2 Exp
DeepSeek V3.2 Exp $0.0005, GPT-5 Mini $0.0013 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5 Mini has the number, $0.08.
Directional only
At a glance
- Coding index
- DeepSeek V3.2 Exp 55.0 from 2 benchmarks, GPT-5 Mini 59.8 from 1 benchmark.
- Widest category
- Agentic work, 37.1 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: DeepSeek V3.2 Exp 33.9% against GPT-5 Mini 71.1%, 37.1 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.2 Exp $0.41 against GPT-5 Mini $2.00 per million output tokens, 4.9x apart.
- Context window
- DeepSeek V3.2 Exp takes 164K tokens, GPT-5 Mini 400K.
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 | GPT-5 Mini | Ties |
|---|---|---|---|---|
| Coding | 2 | 3 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 3 | 2 | ||
| Math | 2 | 2 | ||
| Knowledge | 5 | 0 | ||
| Web design | 6 | 1 | ||
| General | 18 | 3 | 1 tie | |
| All shared boards | 36 | 14 | 1 tie |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index23
| Benchmark | DeepSeek V3.2 Exp | GPT-5 Mini | Leader |
|---|---|---|---|
| Aider polyglot | 74.2% | Not measured | Only DeepSeek V3.2 Exp measured |
| SWE-bench Verified | Not measured | 59.8% | Only GPT-5 Mini measured |
| SWE-bench Multilingual | Not measured | 39.7% | Only GPT-5 Mini measured |
| LMArena WebDev | 1272 | Not measured | Only DeepSeek V3.2 Exp measured |
| LiveCodeBench (Vals AI) | Not measured | 86.6% | Only GPT-5 Mini measured |
| SWE-bench Verified (Vals AI) | Not measured | 60.8% | Only GPT-5 Mini measured |
| Vibe Code Bench (Vals AI) | Not measured | 14.2% | Only GPT-5 Mini measured |
| LiveCodeBench (Artificial Analysis run) | 78.9% | 83.8% | GPT-5 Mini |
| SciCode | Not measured | 39.0% | Only GPT-5 Mini measured |
| Terminal-Bench Hard (Artificial Analysis run) | 31.1% | 33.3% | GPT-5 Mini |
| Terminal-Bench | Not measured | 34.8% | Only GPT-5 Mini measured |
| WeirdML | 39.5% | 52.7% | GPT-5 Mini |
| LMArena Text: coding | 1438 | 1406 | DeepSeek V3.2 Exp |
| LMArena Text: software and IT | 1438 | 1394 | DeepSeek V3.2 Exp |
| LMArena WebDev: HTML | 1281 | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 23 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V3.2 Exp | GPT-5 Mini | Leader |
|---|---|---|---|
| AA Intelligence Index | 16.6 | 20.6 | GPT-5 Mini |
| Epoch Capabilities Index | 144.9 | 145.3 | GPT-5 Mini |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3.2 Exp | GPT-5 Mini | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 33.9% | 71.1% | GPT-5 Mini |
| TheAgentCompany | 42.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| Vending-Bench 2 | Not measured | -$31 | Only GPT-5 Mini measured |
| Neither model has a result on the other 36 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index32
| Benchmark | DeepSeek V3.2 Exp | GPT-5 Mini | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 80.3% | Only GPT-5 Mini measured |
| MMMU Pro (Vals AI) | Not measured | 78.9% | Only GPT-5 Mini measured |
| GPQA Diamond (Artificial Analysis run) | 79.7% | 82.8% | GPT-5 Mini |
| Humanity's Last Exam (Artificial Analysis run) | 14.9% | 21.5% | GPT-5 Mini |
| GPQA Diamond (Epoch AI run) | Not measured | 75.0% | Only GPT-5 Mini measured |
| ARC-AGI | Not measured | 54.3% | Only GPT-5 Mini measured |
| ARC-AGI-2 | Not measured | 4.4% | Only GPT-5 Mini measured |
| Chess puzzles | Not measured | 24.0% | Only GPT-5 Mini measured |
| Mystery game puzzles | Not measured | 10.0% | Only GPT-5 Mini measured |
| VPCT | Not measured | 40.2% | Only GPT-5 Mini measured |
| LMArena Text: hard prompts | 1429 | 1380 | DeepSeek V3.2 Exp |
| LMArena Text: expert | 1422 | 1379 | DeepSeek V3.2 Exp |
| LMArena Text: hard prompts in English | 1444 | 1383 | DeepSeek V3.2 Exp |
| LMArena Vision: diagrams | Not measured | 1202 | Only GPT-5 Mini measured |
| LMArena Vision: homework | Not measured | 1210 | Only GPT-5 Mini measured |
| Neither model has a result on the other 6 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index22
| Benchmark | DeepSeek V3.2 Exp | GPT-5 Mini | Leader |
|---|---|---|---|
| AIME | Not measured | 91.5% | Only GPT-5 Mini measured |
| MATH 500 (Vals AI) | Not measured | 94.8% | Only GPT-5 Mini measured |
| MGSM (Vals AI) | Not measured | 92.6% | Only GPT-5 Mini measured |
| AA Math Index | 87.7 | 90.7 | GPT-5 Mini |
| AIME 2025 (Artificial Analysis run) | 87.7% | 90.7% | GPT-5 Mini |
| OTIS Mock AIME | Not measured | 86.7% | Only GPT-5 Mini measured |
| FrontierMath, tiers 1 to 3 | Not measured | 46.7% | Only GPT-5 Mini measured |
| FrontierMath, tier 4 | Not measured | 12.2% | Only GPT-5 Mini measured |
| ProofBench | Not measured | 9.0% | Only GPT-5 Mini measured |
| MATH level 5 | Not measured | 97.8% | Only GPT-5 Mini measured |
| LMArena Text: math | 1422 | 1378 | DeepSeek V3.2 Exp |
| LMArena Text: mathematical work | 1433 | 1380 | DeepSeek V3.2 Exp |
| Neither model has a result on the other 1 board here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | DeepSeek V3.2 Exp | GPT-5 Mini | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 82.2% | Only GPT-5 Mini measured |
| LegalBench (Vals AI) | Not measured | 81.8% | Only GPT-5 Mini measured |
| CaseLaw (Vals AI) | Not measured | 68.5% | Only GPT-5 Mini measured |
| CorpFin (Vals AI) | Not measured | 60.2% | Only GPT-5 Mini measured |
| TaxEval (Vals AI) | Not measured | 75.2% | Only GPT-5 Mini measured |
| MortgageTax (Vals AI) | Not measured | 66.9% | Only GPT-5 Mini measured |
| MedQA (Vals AI) | Not measured | 96.1% | Only GPT-5 Mini measured |
| MedCode (Vals AI) | Not measured | 43.0% | Only GPT-5 Mini measured |
| MMLU Pro (Artificial Analysis run) | 85.0% | 83.7% | DeepSeek V3.2 Exp |
| SimpleQA Verified | Not measured | 21.6% | Only GPT-5 Mini measured |
| LMArena Text: science | 1443 | 1371 | DeepSeek V3.2 Exp |
| LMArena Text: medicine and healthcare | 1468 | 1380 | DeepSeek V3.2 Exp |
| LMArena Text: legal and government | 1419 | 1375 | DeepSeek V3.2 Exp |
| LMArena Text: business and finance | 1412 | 1358 | DeepSeek V3.2 Exp |
| Neither model has a result on the other 3 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index61
| Benchmark | DeepSeek V3.2 Exp | GPT-5 Mini | Leader |
|---|---|---|---|
| Design Arena: websites | 1179 | 1125 | DeepSeek V3.2 Exp |
| Design Arena: UI components | 1166 | 1106 | DeepSeek V3.2 Exp |
| Design Arena: game dev | 1145 | 1130 | DeepSeek V3.2 Exp |
| Design Arena: data viz | 1152 | 1131 | DeepSeek V3.2 Exp |
| Design Arena: 3D | 1148 | 1037 | DeepSeek V3.2 Exp |
| Design Arena: all code categories | 1171 | 1115 | DeepSeek V3.2 Exp |
| Design Arena: SVG | 1031 | 1091 | GPT-5 Mini |
| Design Arena: ASCII art | Not measured | 1124 | Only GPT-5 Mini measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index183
| Benchmark | DeepSeek V3.2 Exp | GPT-5 Mini | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 43.0% | Only GPT-5 Mini measured |
| MedScribe (Vals AI) | Not measured | 80.6% | Only GPT-5 Mini measured |
| IFBench | 54.1% | 75.4% | GPT-5 Mini |
| Long context reasoning (AA-LCR) | 72.3% | 72.3% | Tie |
| DTBench | 87.7% | 80.5% | DeepSeek V3.2 Exp |
| LMCA | 29.1% | 34.2% | GPT-5 Mini |
| Fiction.LiveBench | 83.3% | 69.4% | DeepSeek V3.2 Exp |
| Creative writing (Lech Mazur) | Not measured | 83.1% | Only GPT-5 Mini 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 | 1373 | DeepSeek V3.2 Exp |
| LMArena Text: instruction following | 1401 | 1357 | DeepSeek V3.2 Exp |
| LMArena Text: creative writing | 1404 | 1325 | DeepSeek V3.2 Exp |
| LMArena Vision | Not measured | 1202 | Only GPT-5 Mini measured |
| LMArena Text: English | 1441 | 1379 | DeepSeek V3.2 Exp |
| LMArena Text: non-English | 1404 | 1363 | DeepSeek V3.2 Exp |
| LMArena Text: Chinese | 1461 | 1386 | DeepSeek V3.2 Exp |
| LMArena Text: German | 1440 | 1366 | DeepSeek V3.2 Exp |
| LMArena Text: Japanese | 1331 | 1341 | GPT-5 Mini |
| LMArena Text: Korean | 1372 | 1308 | DeepSeek V3.2 Exp |
| LMArena Text: French | 1434 | 1386 | DeepSeek V3.2 Exp |
| LMArena Text: Spanish | 1441 | 1354 | DeepSeek V3.2 Exp |
| LMArena Text: Russian | 1412 | 1362 | DeepSeek V3.2 Exp |
| LMArena Text: Polish | 1435 | 1378 | DeepSeek V3.2 Exp |
| LMArena Text: multi-turn | 1422 | 1363 | DeepSeek V3.2 Exp |
| LMArena Text: longer queries | 1418 | 1355 | DeepSeek V3.2 Exp |
| LMArena Text: writing and language | 1407 | 1344 | DeepSeek V3.2 Exp |
| LMArena Text: entertainment and media | 1407 | 1340 | DeepSeek V3.2 Exp |
| LMArena Vision: English | Not measured | 1216 | Only GPT-5 Mini measured |
| LMArena Vision: Chinese | Not measured | 1214 | Only GPT-5 Mini measured |
| LMArena Vision: reading text | Not measured | 1199 | Only GPT-5 Mini measured |
| LMArena Vision: humor | Not measured | 1205 | Only GPT-5 Mini measured |
| LMArena Vision: captioning | Not measured | 1217 | Only GPT-5 Mini measured |
| LMArena Vision: recognizing things | Not measured | 1226 | Only GPT-5 Mini measured |
| LMArena Vision: creative writing | Not measured | 1181 | Only GPT-5 Mini measured |
| Neither model has a result on the other 11 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | GPT-5 Mini | 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 | OpenAI GPT-5 Mini |
|---|---|---|
| Maker | DeepSeek | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | September 29, 2025 | August 7, 2025 |
| Context window | 164K | 400K |
| Input $/Mtok | $0.27 | $0.25 |
| Output $/Mtok | $0.41 | $2.00 |
| Coding index | 55.0 | 59.8 |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | Not computable | $0.08 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.2 Exp better than GPT-5 Mini for coding?
Pick GPT-5 Mini for coding: it scores 59.8 on the Vibe Coding Index against 55.0 for DeepSeek V3.2 Exp, though on 1 benchmark against 2. Pick DeepSeek V3.2 Exp when output tokens cost 4.9x less, or when what matters is LMArena Text: coding or LMArena Text: software and IT, where it leads.
Which is cheaper, DeepSeek V3.2 Exp or GPT-5 Mini?
DeepSeek V3.2 Exp costs $0.27 per million input tokens and $0.41 per million output tokens; GPT-5 Mini costs $0.25 and $2.00. Cost per solved task is known for GPT-5 Mini only.
Which has the bigger context window, DeepSeek V3.2 Exp or GPT-5 Mini?
GPT-5 Mini, with 400K tokens against 164K.
How many benchmarks is the DeepSeek V3.2 Exp vs GPT-5 Mini comparison based on?
51 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.2 Exp's coding index rests on 2 independent benchmarks and GPT-5 Mini's on 1. Where only one model has a result, the page says so instead of naming a winner.