DeepSeek V4.1 Flash vs GPT-5.2
DeepSeek V4.1 Flash leads GPT-5.2 on coding, 62.5 to 55.9 on the Vibe Coding Index, and DeepSeek V4.1 Flash costs 16.7x less per output token ($0.84 vs $14.00 per million). Updated October 22, 2026; 69 benchmarks have a result for both.
vs boards won of 69 shared, 1 tied
DeepSeek V4.1 Flash or GPT-5.2: which to pick
Pick DeepSeek V4.1 Flash for coding: it scores 62.5 on the Vibe Coding Index against 55.9 for GPT-5.2.
Pick GPT-5.2 for work measured by ALE-Bench or AA Intelligence Index, where it leads.
Model ADeepSeek V4.1 FlashDeepSeek62.5/1005 independent benchmarksNo cost per solved taskModel BGPT-5.2OpenAI55.9/1003 independent benchmarks$1.91 per solved task
- Shared results
- 69
- DeepSeek V4.1 Flash only
- 32
- GPT-5.2 only
- 45
- 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
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash scores 62.5 against 55.9.
5 and 3 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-5.2
GPT-5.2 leads, 30.4 against 24.7.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GPT-5.2 has a GPQA Diamond result, 91.7%.
Directional only
Math
AIME
No call
Only GPT-5.2 has a AIME result, 96.9%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5.2 has a MMLU Pro result, 86.2%.
Directional only
Web design
Design Arena: websites
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash leads, 1308 against 1195.
Same board, both measured
Long documents
Listed context window
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash takes 1.05M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash $0.0006, GPT-5.2 $0.0088 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.2 has the number, $1.91.
Directional only
At a glance
- Coding index
- DeepSeek V4.1 Flash 62.5 from 5 benchmarks, GPT-5.2 55.9 from 3 benchmarks.
- Widest category
- Coding, 19.3 points apart on average over 6 shared benchmarks.
- Widest single gap
- ProofBench: DeepSeek V4.1 Flash 54.0% against GPT-5.2 15.0%, 39.0 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4.1 Flash $0.84 against GPT-5.2 $14.00 per million output tokens, 16.7x apart.
- Context window
- DeepSeek V4.1 Flash takes 1.05M tokens, GPT-5.2 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 V4.1 Flash | Bars | GPT-5.2 | Ties |
|---|---|---|---|---|
| Coding | 5 | 1 | ||
| Intelligence | 1 | 1 | ||
| Reasoning | 9 | 1 | 1 tie | |
| Math | 4 | 2 | ||
| Knowledge | 6 | 1 | ||
| Web design | 8 | 0 | ||
| General | 26 | 3 | ||
| All shared boards | 59 | 9 | 1 tie |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index51
| Benchmark | DeepSeek V4.1 Flash | GPT-5.2 | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 72.8% | Only GPT-5.2 measured |
| SWE-bench Multilingual | Not measured | 66.7% | Only GPT-5.2 measured |
| LiveBench coding | 90.2% | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena WebDev | 1618 | 1417 | DeepSeek V4.1 Flash |
| LiveCodeBench (Vals AI) | Not measured | 85.4% | Only GPT-5.2 measured |
| SWE-bench Verified (Vals AI) | Not measured | 75.8% | Only GPT-5.2 measured |
| Terminal-Bench 4 (Vals AI) | 19.7% | Not measured | Only DeepSeek V4.1 Flash measured |
| Vibe Code Bench (Vals AI) | 84.7% | 53.5% | DeepSeek V4.1 Flash |
| Code migration (Vals AI) | 45.6% | Not measured | Only DeepSeek V4.1 Flash measured |
| ProgramBench (Vals AI) | 0.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| IOI (Vals AI) | 40.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| Terminal-Bench 2.1 (Vals AI) | 74.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| SWE-bench Multimodal (Vals AI) | 46.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| Vibe Code Bench 1-100 (Vals AI) | 16.4% | Not measured | Only DeepSeek V4.1 Flash measured |
| SkillsBench (Vals AI) | 69.8% | Not measured | Only DeepSeek V4.1 Flash measured |
| CyberBench (Vals AI) | 73.7% | Not measured | Only DeepSeek V4.1 Flash measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 89.4% | Only GPT-5.2 measured |
| SciCode | 51.9% | Not measured | Only DeepSeek V4.1 Flash measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 47.0% | Only GPT-5.2 measured |
| Terminal-Bench | Not measured | 64.9% | Only GPT-5.2 measured |
| FrontierCode | 43.9% | Not measured | Only DeepSeek V4.1 Flash measured |
| GSO-Bench | Not measured | 27.5% | Only GPT-5.2 measured |
| WeirdML | Not measured | 72.2% | Only GPT-5.2 measured |
| ALE-Bench | 1092.3 | 1293.5 | GPT-5.2 |
| LMArena Text: coding | 1502 | 1442 | DeepSeek V4.1 Flash |
| LMArena Text: software and IT | 1496 | 1434 | DeepSeek V4.1 Flash |
| LMArena WebDev: React | 1628 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena WebDev: HTML | 1612 | 1427 | DeepSeek V4.1 Flash |
| LMArena WebDev: from an image | 1606 | Not measured | Only DeepSeek V4.1 Flash measured |
| Neither model has a result on the other 10 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | DeepSeek V4.1 Flash | GPT-5.2 | Leader |
|---|---|---|---|
| AA Intelligence Index | 24.7 | 30.4 | GPT-5.2 |
| Epoch Capabilities Index | 155.9 | 153.7 | DeepSeek V4.1 Flash |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index24 boards, none shared
| Benchmark | DeepSeek V4.1 Flash | GPT-5.2 | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | Not measured | 1086 | Only GPT-5.2 measured |
| Design Arena: agents building full-stack apps | Not measured | 1043 | Only GPT-5.2 measured |
| Design Arena: agents building mobile apps | Not measured | 1093 | Only GPT-5.2 measured |
| Design Arena: agents building Android apps | Not measured | 1059 | Only GPT-5.2 measured |
| Design Arena: agents building Godot games | Not measured | 1142 | Only GPT-5.2 measured |
| LiveBench research | 86.8% | Not measured | Only DeepSeek V4.1 Flash measured |
| LiveBench browser | 84.0% | Not measured | Only DeepSeek V4.1 Flash measured |
| LiveBench long agentic | 40.1% | Not measured | Only DeepSeek V4.1 Flash measured |
| SREBench (Vals AI) | 0.8% | Not measured | Only DeepSeek V4.1 Flash measured |
| Finance Agent (Vals AI) | 53.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| Tax Agent Bench (Vals AI) | 26.6% | Not measured | Only DeepSeek V4.1 Flash measured |
| Legal Research Bench (Vals AI) | 41.4% | Not measured | Only DeepSeek V4.1 Flash measured |
| Excel Modeling Benchmark (Vals AI) | 57.2% | Not measured | Only DeepSeek V4.1 Flash measured |
| BioMysteryBench (Vals AI) | 67.8% | Not measured | Only DeepSeek V4.1 Flash measured |
| Terminal-Bench-Science (Vals AI) | 0.0% | Not measured | Only DeepSeek V4.1 Flash measured |
| Vals Science Index | 29.6% | Not measured | Only DeepSeek V4.1 Flash measured |
| τ²-Bench Telecom | Not measured | 84.8% | Only GPT-5.2 measured |
| APEX-Agents | 39.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| METR time horizons | Not measured | 75.3% | Only GPT-5.2 measured |
| Remote Labor Index | Not measured | 2.5% | Only GPT-5.2 measured |
| GDPval | Not measured | 49.7% | Only GPT-5.2 measured |
| DeepResearch Bench | Not measured | 41.1% | Only GPT-5.2 measured |
| Vending-Bench 2 | Not measured | $3,591 | Only GPT-5.2 measured |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 55.9% | Only GPT-5.2 measured |
| Neither model has a result on the other 17 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index91
| Benchmark | DeepSeek V4.1 Flash | GPT-5.2 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 91.7% | Only GPT-5.2 measured |
| LiveBench reasoning | 86.7% | Not measured | Only DeepSeek V4.1 Flash measured |
| MysteryMechanism (Vals AI) | 21.2% | Not measured | Only DeepSeek V4.1 Flash measured |
| MMMU Pro (Vals AI) | Not measured | 86.7% | Only GPT-5.2 measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 90.3% | Only GPT-5.2 measured |
| Humanity's Last Exam (Artificial Analysis run) | 39.2% | 37.7% | DeepSeek V4.1 Flash |
| GPQA Diamond (Epoch AI run) | 89.8% | 91.4% | GPT-5.2 |
| ARC-AGI | 94.5% | 86.2% | DeepSeek V4.1 Flash |
| ARC-AGI-2 | 72.9% | 52.9% | DeepSeek V4.1 Flash |
| SimpleBench | Not measured | 45.8% | Only GPT-5.2 measured |
| Chess puzzles | 48.0% | 48.0% | Tie |
| Mystery game puzzles | 43.0% | 23.0% | DeepSeek V4.1 Flash |
| EBR-bench | Not measured | 23.0% | Only GPT-5.2 measured |
| VPCT | Not measured | 84.0% | Only GPT-5.2 measured |
| LMArena Text: hard prompts | 1483 | 1428 | DeepSeek V4.1 Flash |
| LMArena Text: expert | 1501 | 1445 | DeepSeek V4.1 Flash |
| LMArena Text: hard prompts in English | 1488 | 1431 | DeepSeek V4.1 Flash |
| LMArena Vision: diagrams | 1297 | 1265 | DeepSeek V4.1 Flash |
| LMArena Vision: homework | 1307 | 1276 | DeepSeek V4.1 Flash |
| Kagi LLM Benchmark | Not measured | 73.3% | Only GPT-5.2 measured |
| Neither model has a result on the other 2 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index42
| Benchmark | DeepSeek V4.1 Flash | GPT-5.2 | Leader |
|---|---|---|---|
| AIME | Not measured | 96.9% | Only GPT-5.2 measured |
| MGSM (Vals AI) | Not measured | 94.0% | Only GPT-5.2 measured |
| AA Math Index | Not measured | 99.0 | Only GPT-5.2 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 99.0% | Only GPT-5.2 measured |
| OTIS Mock AIME | 98.3% | 96.1% | DeepSeek V4.1 Flash |
| FrontierMath, tiers 1 to 3 | 67.4% | 67.4% | GPT-5.2 |
| FrontierMath, tier 4 | 26.8% | 31.7% | GPT-5.2 |
| ProofBench | 54.0% | 15.0% | DeepSeek V4.1 Flash |
| LMArena Text: math | 1478 | 1440 | DeepSeek V4.1 Flash |
| LMArena Text: mathematical work | 1485 | 1442 | DeepSeek V4.1 Flash |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index61
| Benchmark | DeepSeek V4.1 Flash | GPT-5.2 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 86.2% | Only GPT-5.2 measured |
| LegalBench (Vals AI) | 83.3% | 82.8% | DeepSeek V4.1 Flash |
| CaseLaw (Vals AI) | Not measured | 66.0% | Only GPT-5.2 measured |
| CorpFin (Vals AI) | Not measured | 65.9% | Only GPT-5.2 measured |
| TaxEval (Vals AI) | Not measured | 75.8% | Only GPT-5.2 measured |
| MortgageTax (Vals AI) | Not measured | 67.1% | Only GPT-5.2 measured |
| MedQA (Vals AI) | Not measured | 94.1% | Only GPT-5.2 measured |
| MedCode (Vals AI) | 41.2% | 49.8% | GPT-5.2 |
| MMLU Pro (Artificial Analysis run) | Not measured | 87.4% | Only GPT-5.2 measured |
| SimpleQA Verified | 42.9% | 37.1% | DeepSeek V4.1 Flash |
| LMArena Text: science | 1469 | 1425 | DeepSeek V4.1 Flash |
| LMArena Text: medicine and healthcare | 1484 | 1425 | DeepSeek V4.1 Flash |
| LMArena Text: legal and government | 1443 | 1427 | DeepSeek V4.1 Flash |
| LMArena Text: business and finance | 1461 | 1414 | DeepSeek V4.1 Flash |
| FACTS Benchmark Suite | Not measured | 48.5% | Only GPT-5.2 measured |
| Neither model has a result on the other 3 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | DeepSeek V4.1 Flash | GPT-5.2 | Leader |
|---|---|---|---|
| Design Arena: websites | 1308 | 1195 | DeepSeek V4.1 Flash |
| Design Arena: UI components | 1312 | 1190 | DeepSeek V4.1 Flash |
| Design Arena: game dev | 1330 | 1195 | DeepSeek V4.1 Flash |
| Design Arena: data viz | 1291 | 1202 | DeepSeek V4.1 Flash |
| Design Arena: 3D | 1355 | 1079 | DeepSeek V4.1 Flash |
| Design Arena: all code categories | 1320 | 1170 | DeepSeek V4.1 Flash |
| Design Arena: SVG | 1245 | 1139 | DeepSeek V4.1 Flash |
| Design Arena: ASCII art | 1265 | 1152 | DeepSeek V4.1 Flash |
| LiveBench design | 79.2% | Not measured | Only DeepSeek V4.1 Flash measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index263
| Benchmark | DeepSeek V4.1 Flash | GPT-5.2 | Leader |
|---|---|---|---|
| LiveBench data analysis | 79.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| Public Benefits Bench (Vals AI) | 64.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| SAGE (Vals AI) | 47.9% | 49.3% | GPT-5.2 |
| MedScribe (Vals AI) | 85.5% | 84.4% | DeepSeek V4.1 Flash |
| IFBench | Not measured | 75.4% | Only GPT-5.2 measured |
| Long context reasoning (AA-LCR) | 84.0% | 82.7% | DeepSeek V4.1 Flash |
| DTBench | 89.9% | 90.9% | GPT-5.2 |
| LMCA | 47.0% | 43.9% | DeepSeek V4.1 Flash |
| CL-bench | Not measured | 18.2% | Only GPT-5.2 measured |
| Surface Evolver Bench | 46.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| Furniture assembly | 34.2% | 38.3% | GPT-5.2 |
| LMArena Text | 1464 | 1416 | DeepSeek V4.1 Flash |
| LMArena Text: instruction following | 1473 | 1409 | DeepSeek V4.1 Flash |
| LMArena Text: creative writing | 1437 | 1376 | DeepSeek V4.1 Flash |
| LMArena Vision | 1278 | 1246 | DeepSeek V4.1 Flash |
| LMArena Text: English | 1473 | 1422 | DeepSeek V4.1 Flash |
| LMArena Text: non-English | 1449 | 1405 | DeepSeek V4.1 Flash |
| LMArena Text: Chinese | 1501 | 1447 | DeepSeek V4.1 Flash |
| LMArena Text: German | 1470 | 1444 | DeepSeek V4.1 Flash |
| LMArena Text: Japanese | 1432 | 1420 | DeepSeek V4.1 Flash |
| LMArena Text: Korean | 1436 | 1374 | DeepSeek V4.1 Flash |
| LMArena Text: French | 1465 | 1454 | DeepSeek V4.1 Flash |
| LMArena Text: Spanish | 1459 | 1413 | DeepSeek V4.1 Flash |
| LMArena Text: Russian | 1467 | 1415 | DeepSeek V4.1 Flash |
| LMArena Text: Polish | 1457 | 1426 | DeepSeek V4.1 Flash |
| LMArena Text: multi-turn | 1461 | 1419 | DeepSeek V4.1 Flash |
| LMArena Text: longer queries | 1473 | 1413 | DeepSeek V4.1 Flash |
| LMArena Text: writing and language | 1450 | 1392 | DeepSeek V4.1 Flash |
| LMArena Text: entertainment and media | 1445 | 1378 | DeepSeek V4.1 Flash |
| LMArena Vision: English | 1288 | 1243 | DeepSeek V4.1 Flash |
| LMArena Vision: Chinese | 1327 | 1305 | DeepSeek V4.1 Flash |
| LMArena Vision: reading text | 1282 | 1257 | DeepSeek V4.1 Flash |
| LMArena Vision: humor | 1260 | 1227 | DeepSeek V4.1 Flash |
| LMArena Vision: captioning | Not measured | 1267 | Only GPT-5.2 measured |
| LMArena Vision: recognizing things | Not measured | 1232 | Only GPT-5.2 measured |
| LMArena Vision: creative writing | 1275 | 1225 | DeepSeek V4.1 Flash |
| LMArena Document | Not measured | 1405 | Only GPT-5.2 measured |
| Neither model has a result on the other 9 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4.1 Flash | GPT-5.2 | 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$0.0006Fits in one request
GPT-5.2$0.0088Fits in one request
DeepSeek V4.1 Flash costs less
Repository review
50K input + 3K output tokens
DeepSeek V4.1 Flash$0.014Fits in one request
GPT-5.2$0.130Fits in one request
DeepSeek V4.1 Flash costs less
Agent loop
200K input + 10K output tokens
DeepSeek V4.1 Flash$0.053Fits in one request
GPT-5.2$0.490Fits in one request
DeepSeek V4.1 Flash 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 V4.1 Flash | OpenAI GPT-5.2 |
|---|---|---|
| Maker | DeepSeek | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | September 10, 2026 | December 10, 2025 |
| Context window | 1.05M | 400K |
| Input $/Mtok | $0.2225 | $1.75 |
| Output $/Mtok | $0.84 | $14.00 |
| Coding index | 62.5 | 55.9 |
| Independent measurements behind it | 5 | 3 |
| Cost per solved task | Not computable | $1.91 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4.1 Flash better than GPT-5.2 for coding?
Pick DeepSeek V4.1 Flash for coding: it scores 62.5 on the Vibe Coding Index against 55.9 for GPT-5.2. Pick GPT-5.2 for work measured by ALE-Bench or AA Intelligence Index, where it leads.
Which is cheaper, DeepSeek V4.1 Flash or GPT-5.2?
DeepSeek V4.1 Flash costs $0.2225 per million input tokens and $0.84 per million output tokens; GPT-5.2 costs $1.75 and $14.00. Cost per solved task is known for GPT-5.2 only.
Which has the bigger context window, DeepSeek V4.1 Flash or GPT-5.2?
DeepSeek V4.1 Flash, with 1.05M tokens against 400K.
How many benchmarks is the DeepSeek V4.1 Flash vs GPT-5.2 comparison based on?
69 benchmarks have a result for both models as of October 22, 2026. DeepSeek V4.1 Flash's coding index rests on 5 independent benchmarks and GPT-5.2's on 3. Where only one model has a result, the page says so instead of naming a winner.