DeepSeek V4 Flash Vision Exp vs GPT-5.2
Updated October 21, 2026. 4 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
DeepSeek V4 Flash Vision Exp scores 37.6 and GPT-5.2 58.9 on the Vibe Coding Index, but no coding benchmark measured both, so the gap is directional, not a ranking.
Pick DeepSeek V4 Flash Vision Exp when output tokens cost 21.6x less, or when the context window is larger, 1.05M against 400K tokens; pick GPT-5.2 when what matters is Humanity's Last Exam (Artificial Analysis run) or Long context reasoning (AA-LCR), where it leads.
- Shared results
- 4
- DeepSeek V4 Flash Vision Exp only
- 10
- GPT-5.2 only
- 107
- Like-for-like categories
- 3 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
No call
37.6 against 58.9, from different benchmarks with none in common.
Directional only
Intelligence
AA Intelligence Index
DeepSeek V4 Flash Vision Exp
DeepSeek V4 Flash Vision Exp leads, 34.8 against 30.4.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only DeepSeek V4 Flash Vision Exp has a AA Agentic Index result, 47.5.
Directional only
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
No call
Only GPT-5.2 has a Design Arena: websites result, 1196.
Directional only
Long documents
Listed context window
DeepSeek V4 Flash Vision Exp
DeepSeek V4 Flash Vision Exp takes 1.05M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V4 Flash Vision Exp
DeepSeek V4 Flash Vision Exp $0.0005, 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 Flash Vision Exp 37.6 from 1 benchmark, GPT-5.2 58.9 from 2 benchmarks.
- Widest category
- Intelligence, 4.4 points apart on average over 1 shared benchmark.
- Widest single gap
- AA Intelligence Index: DeepSeek V4 Flash Vision Exp 34.8 against GPT-5.2 30.4, 4.4 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4 Flash Vision Exp $0.6468 against GPT-5.2 $14.00 per million output tokens, 21.6x apart.
- Context window
- DeepSeek V4 Flash Vision Exp 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 Flash Vision Exp | Bars | GPT-5.2 | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| Reasoning | 1 | 1 | ||
| General | 0 | 1 | ||
| All shared boards | 2 | 2 |
CodingDirectional onlyFour of these boards feed the Vibe Coding Index18 boards, none shared
| Benchmark | DeepSeek V4 Flash Vision Exp | 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 | 37.6% | Not measured | Only DeepSeek V4 Flash Vision Exp measured |
| LMArena WebDev | Not measured | 1417 | Only GPT-5.2 measured |
| AA Coding Index | 65.0 | Not measured | Only DeepSeek V4 Flash Vision Exp measured |
| 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 |
| Vibe Code Bench (Vals AI) | Not measured | 53.5% | Only GPT-5.2 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 89.4% | Only GPT-5.2 measured |
| SciCode | 49.7% | Not measured | Only DeepSeek V4 Flash Vision Exp 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 |
| GSO-Bench | Not measured | 27.5% | Only GPT-5.2 measured |
| WeirdML | Not measured | 72.2% | Only GPT-5.2 measured |
| ALE-Bench | Not measured | 1293.5 | Only GPT-5.2 measured |
| LMArena Text: coding | Not measured | 1442 | Only GPT-5.2 measured |
| LMArena Text: software and IT | Not measured | 1434 | Only GPT-5.2 measured |
| LMArena WebDev: HTML | Not measured | 1427 | Only GPT-5.2 measured |
| Neither model has a result on the other 20 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4 Flash Vision Exp | GPT-5.2 | Leader |
|---|---|---|---|
| AA Intelligence Index | 34.8 | 30.4 | DeepSeek V4 Flash Vision Exp |
| Epoch Capabilities Index | Not measured | 153.7 | Only GPT-5.2 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index15 boards, none shared
| Benchmark | DeepSeek V4 Flash Vision Exp | GPT-5.2 | Leader |
|---|---|---|---|
| AA Agentic Index | 47.5 | Not measured | Only DeepSeek V4 Flash Vision Exp measured |
| 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 | 1094 | Only GPT-5.2 measured |
| Design Arena: agents building Android apps | Not measured | 1064 | Only GPT-5.2 measured |
| Design Arena: agents building Godot games | Not measured | 1142 | Only GPT-5.2 measured |
| LiveBench research | 69.2% | Not measured | Only DeepSeek V4 Flash Vision Exp measured |
| LiveBench browser | 64.0% | Not measured | Only DeepSeek V4 Flash Vision Exp measured |
| LiveBench long agentic | 39.0% | Not measured | Only DeepSeek V4 Flash Vision Exp measured |
| τ²-Bench Telecom | Not measured | 84.8% | Only GPT-5.2 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 |
| Neither model has a result on the other 24 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | DeepSeek V4 Flash Vision Exp | GPT-5.2 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 91.7% | Only GPT-5.2 measured |
| LiveBench reasoning | 85.4% | Not measured | Only DeepSeek V4 Flash Vision Exp measured |
| MMMU Pro (Vals AI) | Not measured | 86.7% | Only GPT-5.2 measured |
| GPQA Diamond (Artificial Analysis run) | 91.3% | 90.3% | DeepSeek V4 Flash Vision Exp |
| Humanity's Last Exam (Artificial Analysis run) | 34.5% | 37.7% | GPT-5.2 |
| GPQA Diamond (Epoch AI run) | Not measured | 91.4% | Only GPT-5.2 measured |
| ARC-AGI | Not measured | 86.2% | Only GPT-5.2 measured |
| ARC-AGI-2 | Not measured | 52.9% | Only GPT-5.2 measured |
| SimpleBench | Not measured | 45.8% | Only GPT-5.2 measured |
| Chess puzzles | Not measured | 48.0% | Only GPT-5.2 measured |
| Mystery game puzzles | Not measured | 23.0% | Only GPT-5.2 measured |
| 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 | Not measured | 1428 | Only GPT-5.2 measured |
| LMArena Text: expert | Not measured | 1445 | Only GPT-5.2 measured |
| LMArena Text: hard prompts in English | Not measured | 1431 | Only GPT-5.2 measured |
| LMArena Vision: diagrams | Not measured | 1265 | Only GPT-5.2 measured |
| LMArena Vision: homework | Not measured | 1276 | Only GPT-5.2 measured |
| Neither model has a result on the other 3 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
| Benchmark | DeepSeek V4 Flash Vision Exp | 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 | Not measured | 96.1% | Only GPT-5.2 measured |
| FrontierMath, tiers 1 to 3 | Not measured | 67.4% | Only GPT-5.2 measured |
| FrontierMath, tier 4 | Not measured | 31.7% | Only GPT-5.2 measured |
| ProofBench | Not measured | 15.0% | Only GPT-5.2 measured |
| LMArena Text: math | Not measured | 1440 | Only GPT-5.2 measured |
| LMArena Text: mathematical work | Not measured | 1442 | Only GPT-5.2 measured |
| Neither model has a result on the other 3 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index14 boards, none shared
| Benchmark | DeepSeek V4 Flash Vision Exp | GPT-5.2 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 86.2% | Only GPT-5.2 measured |
| LegalBench (Vals AI) | Not measured | 82.8% | Only GPT-5.2 measured |
| 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) | Not measured | 49.8% | Only GPT-5.2 measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 87.4% | Only GPT-5.2 measured |
| SimpleQA Verified | Not measured | 37.1% | Only GPT-5.2 measured |
| LMArena Text: science | Not measured | 1425 | Only GPT-5.2 measured |
| LMArena Text: medicine and healthcare | Not measured | 1425 | Only GPT-5.2 measured |
| LMArena Text: legal and government | Not measured | 1427 | Only GPT-5.2 measured |
| LMArena Text: business and finance | Not measured | 1414 | Only GPT-5.2 measured |
| Neither model has a result on the other 3 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | DeepSeek V4 Flash Vision Exp | GPT-5.2 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1196 | Only GPT-5.2 measured |
| Design Arena: UI components | Not measured | 1190 | Only GPT-5.2 measured |
| Design Arena: game dev | Not measured | 1196 | Only GPT-5.2 measured |
| Design Arena: data viz | Not measured | 1202 | Only GPT-5.2 measured |
| Design Arena: 3D | Not measured | 1079 | Only GPT-5.2 measured |
| Design Arena: all code categories | Not measured | 1171 | Only GPT-5.2 measured |
| Design Arena: SVG | Not measured | 1137 | Only GPT-5.2 measured |
| Design Arena: ASCII art | Not measured | 1153 | Only GPT-5.2 measured |
| LiveBench design | 70.7% | Not measured | Only DeepSeek V4 Flash Vision Exp measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V4 Flash Vision Exp | GPT-5.2 | Leader |
|---|---|---|---|
| LiveBench data analysis | 79.5% | Not measured | Only DeepSeek V4 Flash Vision Exp measured |
| SAGE (Vals AI) | Not measured | 49.3% | Only GPT-5.2 measured |
| MedScribe (Vals AI) | Not measured | 84.4% | Only GPT-5.2 measured |
| IFBench | Not measured | 75.4% | Only GPT-5.2 measured |
| Long context reasoning (AA-LCR) | 81.3% | 82.7% | GPT-5.2 |
| DTBench | Not measured | 90.9% | Only GPT-5.2 measured |
| LMCA | Not measured | 43.9% | Only GPT-5.2 measured |
| CL-bench | Not measured | 18.2% | Only GPT-5.2 measured |
| Furniture assembly | Not measured | 38.3% | Only GPT-5.2 measured |
| LMArena Text | Not measured | 1416 | Only GPT-5.2 measured |
| LMArena Text: instruction following | Not measured | 1409 | Only GPT-5.2 measured |
| LMArena Text: creative writing | Not measured | 1376 | Only GPT-5.2 measured |
| LMArena Vision | Not measured | 1246 | Only GPT-5.2 measured |
| LMArena Text: English | Not measured | 1422 | Only GPT-5.2 measured |
| LMArena Text: non-English | Not measured | 1405 | Only GPT-5.2 measured |
| LMArena Text: Chinese | Not measured | 1447 | Only GPT-5.2 measured |
| LMArena Text: German | Not measured | 1444 | Only GPT-5.2 measured |
| LMArena Text: Japanese | Not measured | 1420 | Only GPT-5.2 measured |
| LMArena Text: Korean | Not measured | 1374 | Only GPT-5.2 measured |
| LMArena Text: French | Not measured | 1454 | Only GPT-5.2 measured |
| LMArena Text: Spanish | Not measured | 1413 | Only GPT-5.2 measured |
| LMArena Text: Russian | Not measured | 1415 | Only GPT-5.2 measured |
| LMArena Text: Polish | Not measured | 1426 | Only GPT-5.2 measured |
| LMArena Text: multi-turn | Not measured | 1419 | Only GPT-5.2 measured |
| LMArena Text: longer queries | Not measured | 1413 | Only GPT-5.2 measured |
| LMArena Text: writing and language | Not measured | 1392 | Only GPT-5.2 measured |
| LMArena Text: entertainment and media | Not measured | 1378 | Only GPT-5.2 measured |
| LMArena Vision: English | Not measured | 1243 | Only GPT-5.2 measured |
| LMArena Vision: Chinese | Not measured | 1305 | Only GPT-5.2 measured |
| LMArena Vision: reading text | Not measured | 1257 | Only GPT-5.2 measured |
| LMArena Vision: humor | Not measured | 1227 | Only GPT-5.2 measured |
| 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 | Not measured | 1225 | Only GPT-5.2 measured |
| LMArena Document | Not measured | 1405 | Only GPT-5.2 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 V4 Flash Vision Exp | 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 Flash Vision Exp costs less
Repository review
50K input + 3K output tokens
DeepSeek V4 Flash Vision Exp costs less
Agent loop
200K input + 10K output tokens
DeepSeek V4 Flash Vision 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 V4 Flash Vision Exp | OpenAI GPT-5.2 |
|---|---|---|
| Maker | DeepSeek | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | August 21, 2026 | December 10, 2025 |
| Context window | 1.05M | 400K |
| Input $/Mtok | $0.2156 | $1.75 |
| Output $/Mtok | $0.6468 | $14.00 |
| Coding index | 37.6 | 58.9 |
| Independent measurements behind it | 1 | 2 |
| Cost per solved task | Not computable | $1.91 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4 Flash Vision Exp better than GPT-5.2 for coding?
DeepSeek V4 Flash Vision Exp scores 37.6 and GPT-5.2 58.9 on the Vibe Coding Index, but no coding benchmark measured both, so the gap is directional, not a ranking. Pick DeepSeek V4 Flash Vision Exp when output tokens cost 21.6x less, or when the context window is larger, 1.05M against 400K tokens; pick GPT-5.2 when what matters is Humanity's Last Exam (Artificial Analysis run) or Long context reasoning (AA-LCR), where it leads.
Which is cheaper, DeepSeek V4 Flash Vision Exp or GPT-5.2?
DeepSeek V4 Flash Vision Exp costs $0.2156 per million input tokens and $0.6468 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 Flash Vision Exp or GPT-5.2?
DeepSeek V4 Flash Vision Exp, with 1.05M tokens against 400K.
How many benchmarks is the DeepSeek V4 Flash Vision Exp vs GPT-5.2 comparison based on?
4 benchmarks have a result for both models as of October 21, 2026. DeepSeek V4 Flash Vision Exp's coding index rests on 1 independent benchmark and GPT-5.2's on 2. Where only one model has a result, the page says so instead of naming a winner.