DeepSeek V3.2 Exp vs GPT-6.1 Sol
Updated October 21, 2026. 38 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-6.1 Sol for coding: it scores 88.7 on the Vibe Coding Index against 55.0 for DeepSeek V3.2 Exp.
Pick DeepSeek V3.2 Exp when output tokens cost 24.4x less, or when what matters is LMArena Text: medicine and healthcare or LMArena Text: Spanish, where it leads.
- Shared results
- 38
- DeepSeek V3.2 Exp only
- 19
- GPT-6.1 Sol only
- 57
- Like-for-like categories
- 7 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-6.1 Sol
GPT-6.1 Sol scores 88.7 against 55.0.
2 benchmarks behind each score
Intelligence
AA Intelligence Index
GPT-6.1 Sol
GPT-6.1 Sol leads, 51.8 against 16.6.
Same board, both measured
Web design
Design Arena: websites
GPT-6.1 Sol
GPT-6.1 Sol leads, 1321 against 1179.
Same board, both measured
General
Vals Index
No call
Only GPT-6.1 Sol has a Vals Index result, 63.9%.
Directional only
Long documents
Listed context window
GPT-6.1 Sol
GPT-6.1 Sol 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, GPT-6.1 Sol $0.0070 per turn.
List prices
At a glance
- Coding index
- DeepSeek V3.2 Exp 55.0 from 2 benchmarks, GPT-6.1 Sol 88.7 from 2 benchmarks.
- Widest category
- Coding, 33.7 points apart on average over 4 shared benchmarks.
- Widest single gap
- LMArena WebDev: DeepSeek V3.2 Exp 1272 against GPT-6.1 Sol 1753, 68.8 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.2 Exp $0.41 against GPT-6.1 Sol $10.00 per million output tokens, 24.4x apart.
- Context window
- DeepSeek V3.2 Exp takes 164K tokens, GPT-6.1 Sol 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 | GPT-6.1 Sol | Ties |
|---|---|---|---|---|
| Coding | 0 | 4 | ||
| Intelligence | 0 | 2 | ||
| Reasoning | 0 | 4 | ||
| Math | 0 | 2 | ||
| Knowledge | 1 | 3 | ||
| Web design | 0 | 6 | ||
| General | 1 | 15 | ||
| All shared boards | 2 | 36 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index04
| Benchmark | DeepSeek V3.2 Exp | GPT-6.1 Sol | Leader |
|---|---|---|---|
| Aider polyglot | 74.2% | Not measured | Only DeepSeek V3.2 Exp measured |
| LiveBench coding | Not measured | 96.0% | Only GPT-6.1 Sol measured |
| LMArena WebDev | 1272 | 1753 | GPT-6.1 Sol |
| Terminal-Bench 4 (Vals AI) | Not measured | 55.0% | Only GPT-6.1 Sol measured |
| Vibe Code Bench (Vals AI) | Not measured | 88.9% | Only GPT-6.1 Sol measured |
| Code migration (Vals AI) | Not measured | 65.1% | Only GPT-6.1 Sol measured |
| ProgramBench (Vals AI) | Not measured | 3.0% | Only GPT-6.1 Sol measured |
| IOI (Vals AI) | Not measured | 96.9% | Only GPT-6.1 Sol measured |
| SWE-bench Multimodal (Vals AI) | Not measured | 43.5% | Only GPT-6.1 Sol measured |
| CyberBench (Vals AI) | Not measured | 39.3% | Only GPT-6.1 Sol measured |
| LiveCodeBench (Artificial Analysis run) | 78.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| SciCode | Not measured | 55.8% | Only GPT-6.1 Sol measured |
| Terminal-Bench Hard (Artificial Analysis run) | 31.1% | Not measured | Only DeepSeek V3.2 Exp measured |
| FrontierCode | Not measured | 50.2% | Only GPT-6.1 Sol measured |
| WeirdML | 39.5% | Not measured | Only DeepSeek V3.2 Exp measured |
| ALE-Bench | Not measured | 2743.8 | Only GPT-6.1 Sol measured |
| LMArena Text: coding | 1438 | 1478 | GPT-6.1 Sol |
| LMArena Text: software and IT | 1438 | 1476 | GPT-6.1 Sol |
| LMArena WebDev: React | Not measured | 1754 | Only GPT-6.1 Sol measured |
| LMArena WebDev: HTML | 1281 | 1800 | GPT-6.1 Sol |
| LMArena WebDev: from an image | Not measured | 1702 | Only GPT-6.1 Sol measured |
| Neither model has a result on the other 17 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V3.2 Exp | GPT-6.1 Sol | Leader |
|---|---|---|---|
| AA Intelligence Index | 16.6 | 51.8 | GPT-6.1 Sol |
| Epoch Capabilities Index | 144.9 | 168.4 | GPT-6.1 Sol |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index19 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | GPT-6.1 Sol | Leader |
|---|---|---|---|
| Design Arena: agents building mobile apps | Not measured | 1190 | Only GPT-6.1 Sol measured |
| Design Arena: spreadsheets | Not measured | 1250 | Only GPT-6.1 Sol measured |
| LiveBench research | Not measured | 83.5% | Only GPT-6.1 Sol measured |
| LiveBench browser | Not measured | 88.0% | Only GPT-6.1 Sol measured |
| LiveBench long agentic | Not measured | 73.0% | Only GPT-6.1 Sol measured |
| SREBench (Vals AI) | Not measured | 50.8% | Only GPT-6.1 Sol measured |
| Finance Agent (Vals AI) | Not measured | 52.0% | Only GPT-6.1 Sol measured |
| Tax Agent Bench (Vals AI) | Not measured | 21.8% | Only GPT-6.1 Sol measured |
| Legal Research Bench (Vals AI) | Not measured | 38.5% | Only GPT-6.1 Sol measured |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 6.3% | Only GPT-6.1 Sol measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 70.8% | Only GPT-6.1 Sol measured |
| BioMysteryBench (Vals AI) | Not measured | 79.6% | Only GPT-6.1 Sol measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 52.9% | Only GPT-6.1 Sol measured |
| Vals Science Index | Not measured | 59.6% | Only GPT-6.1 Sol measured |
| Robo-bench (Vals AI) | Not measured | 28.3% | Only GPT-6.1 Sol measured |
| τ²-Bench Telecom | 33.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| APEX-Agents | Not measured | 60.0% | Only GPT-6.1 Sol measured |
| TheAgentCompany | 42.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| SpireClimb | Not measured | 24.7% | Only GPT-6.1 Sol measured |
| Neither model has a result on the other 20 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | DeepSeek V3.2 Exp | GPT-6.1 Sol | Leader |
|---|---|---|---|
| LiveBench reasoning | Not measured | 92.6% | Only GPT-6.1 Sol measured |
| MysteryMechanism (Vals AI) | Not measured | 46.4% | Only GPT-6.1 Sol 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% | 52.9% | GPT-6.1 Sol |
| GPQA Diamond (Epoch AI run) | Not measured | 95.4% | Only GPT-6.1 Sol measured |
| ARC-AGI | Not measured | 98.5% | Only GPT-6.1 Sol measured |
| ARC-AGI-2 | Not measured | 94.2% | Only GPT-6.1 Sol measured |
| Chess puzzles | Not measured | 68.0% | Only GPT-6.1 Sol measured |
| Mystery game puzzles | Not measured | 80.0% | Only GPT-6.1 Sol measured |
| EBR-bench | Not measured | 54.3% | Only GPT-6.1 Sol measured |
| LMArena Text: hard prompts | 1429 | 1463 | GPT-6.1 Sol |
| LMArena Text: expert | 1422 | 1489 | GPT-6.1 Sol |
| LMArena Text: hard prompts in English | 1444 | 1465 | GPT-6.1 Sol |
| LMArena Vision: diagrams | Not measured | 1306 | Only GPT-6.1 Sol measured |
| LMArena Vision: homework | Not measured | 1356 | Only GPT-6.1 Sol measured |
| Neither model has a result on the other 6 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V3.2 Exp | GPT-6.1 Sol | 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 | 100.0% | Only GPT-6.1 Sol measured |
| FrontierMath, tiers 1 to 3 | Not measured | 93.7% | Only GPT-6.1 Sol measured |
| FrontierMath, tier 4 | Not measured | 100.0% | Only GPT-6.1 Sol measured |
| ProofBench | Not measured | 99.0% | Only GPT-6.1 Sol measured |
| LMArena Text: math | 1422 | 1489 | GPT-6.1 Sol |
| LMArena Text: mathematical work | 1433 | 1513 | GPT-6.1 Sol |
| 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 | GPT-6.1 Sol | Leader |
|---|---|---|---|
| MedCode (Vals AI) | Not measured | 48.8% | Only GPT-6.1 Sol measured |
| MMLU Pro (Artificial Analysis run) | 85.0% | Not measured | Only DeepSeek V3.2 Exp measured |
| SimpleQA Verified | Not measured | 73.9% | Only GPT-6.1 Sol measured |
| LMArena Text: science | 1443 | 1444 | GPT-6.1 Sol |
| LMArena Text: medicine and healthcare | 1468 | 1433 | DeepSeek V3.2 Exp |
| LMArena Text: legal and government | 1419 | 1471 | GPT-6.1 Sol |
| LMArena Text: business and finance | 1412 | 1445 | GPT-6.1 Sol |
| Neither model has a result on the other 10 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | DeepSeek V3.2 Exp | GPT-6.1 Sol | Leader |
|---|---|---|---|
| Design Arena: websites | 1179 | 1321 | GPT-6.1 Sol |
| Design Arena: UI components | 1166 | 1321 | GPT-6.1 Sol |
| Design Arena: game dev | 1145 | 1389 | GPT-6.1 Sol |
| Design Arena: data viz | 1152 | 1278 | GPT-6.1 Sol |
| Design Arena: 3D | 1148 | 1461 | GPT-6.1 Sol |
| Design Arena: all code categories | 1171 | 1345 | GPT-6.1 Sol |
| Design Arena: SVG | 1031 | Not measured | Only DeepSeek V3.2 Exp measured |
| LiveBench design | Not measured | 82.1% | Only GPT-6.1 Sol measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index115
| Benchmark | DeepSeek V3.2 Exp | GPT-6.1 Sol | Leader |
|---|---|---|---|
| Vals Index | Not measured | 63.9% | Only GPT-6.1 Sol measured |
| LiveBench data analysis | Not measured | 82.7% | Only GPT-6.1 Sol measured |
| Public Benefits Bench (Vals AI) | Not measured | 59.3% | Only GPT-6.1 Sol measured |
| SAGE (Vals AI) | Not measured | 46.5% | Only GPT-6.1 Sol measured |
| MedScribe (Vals AI) | Not measured | 86.5% | Only GPT-6.1 Sol measured |
| IFBench | 54.1% | Not measured | Only DeepSeek V3.2 Exp measured |
| Long context reasoning (AA-LCR) | 72.3% | 84.0% | GPT-6.1 Sol |
| DTBench | 87.7% | 96.5% | GPT-6.1 Sol |
| LMCA | 29.1% | 62.8% | GPT-6.1 Sol |
| Fiction.LiveBench | 83.3% | Not measured | Only DeepSeek V3.2 Exp measured |
| CL-bench | 13.2% | Not measured | Only DeepSeek V3.2 Exp measured |
| Furniture assembly | Not measured | 80.0% | Only GPT-6.1 Sol measured |
| CL-bench Life | 9.5% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text | 1425 | 1443 | GPT-6.1 Sol |
| LMArena Text: instruction following | 1401 | 1466 | GPT-6.1 Sol |
| LMArena Text: creative writing | 1404 | 1425 | GPT-6.1 Sol |
| LMArena Vision | Not measured | 1288 | Only GPT-6.1 Sol measured |
| LMArena Text: English | 1441 | 1450 | GPT-6.1 Sol |
| LMArena Text: non-English | 1404 | 1433 | GPT-6.1 Sol |
| LMArena Text: Chinese | 1461 | 1487 | GPT-6.1 Sol |
| 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 | 1460 | GPT-6.1 Sol |
| LMArena Text: Spanish | 1441 | 1439 | DeepSeek V3.2 Exp |
| LMArena Text: Russian | 1412 | 1457 | GPT-6.1 Sol |
| LMArena Text: Polish | 1435 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: multi-turn | 1422 | 1442 | GPT-6.1 Sol |
| LMArena Text: longer queries | 1418 | 1458 | GPT-6.1 Sol |
| LMArena Text: writing and language | 1407 | 1444 | GPT-6.1 Sol |
| LMArena Text: entertainment and media | 1407 | 1426 | GPT-6.1 Sol |
| LMArena Vision: English | Not measured | 1274 | Only GPT-6.1 Sol measured |
| LMArena Vision: reading text | Not measured | 1289 | Only GPT-6.1 Sol measured |
| LMArena Vision: creative writing | Not measured | 1301 | Only GPT-6.1 Sol measured |
| Neither model has a result on the other 12 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | GPT-6.1 Sol | 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-6.1 Sol |
|---|---|---|
| Maker | DeepSeek | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | September 29, 2025 | September 29, 2026 |
| Context window | 164K | 1.05M |
| Input $/Mtok | $0.27 | $2.00 |
| Output $/Mtok | $0.41 | $10.00 |
| Coding index | 55.0 | 88.7 |
| Independent measurements behind it | 2 | 2 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.2 Exp better than GPT-6.1 Sol for coding?
Pick GPT-6.1 Sol for coding: it scores 88.7 on the Vibe Coding Index against 55.0 for DeepSeek V3.2 Exp. Pick DeepSeek V3.2 Exp when output tokens cost 24.4x less, or when what matters is LMArena Text: medicine and healthcare or LMArena Text: Spanish, where it leads.
Which is cheaper, DeepSeek V3.2 Exp or GPT-6.1 Sol?
DeepSeek V3.2 Exp costs $0.27 per million input tokens and $0.41 per million output tokens; GPT-6.1 Sol costs $2.00 and $10.00. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V3.2 Exp or GPT-6.1 Sol?
GPT-6.1 Sol, with 1.05M tokens against 164K.
How many benchmarks is the DeepSeek V3.2 Exp vs GPT-6.1 Sol comparison based on?
38 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-6.1 Sol's on 2. Where only one model has a result, the page says so instead of naming a winner.