DeepSeek V4 Flash 0423 vs GPT-5
Updated October 21, 2026. 46 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 for coding: it scores 72.2 on the Vibe Coding Index against 54.6 for DeepSeek V4 Flash 0423.
Pick DeepSeek V4 Flash 0423 when output tokens cost 54.9x less, or when the context window is larger, 1.05M against 400K tokens.
- Shared results
- 46
- DeepSeek V4 Flash 0423 only
- 2
- GPT-5 only
- 63
- 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-5
GPT-5 scores 72.2 against 54.6.
1 and 3 benchmarks behind the scores
Intelligence
AA Intelligence Index
DeepSeek V4 Flash 0423
DeepSeek V4 Flash 0423 leads, 24.4 against 23.0.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only DeepSeek V4 Flash 0423 has a AA Agentic Index result, 26.3.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-5 has a GPQA Diamond result, 85.6%.
Directional only
Math
AIME
No call
Only GPT-5 has a AIME result, 93.4%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5 has a MMLU Pro result, 86.5%.
Directional only
Web design
Design Arena: websites
DeepSeek V4 Flash 0423
DeepSeek V4 Flash 0423 leads, 1208 against 1185.
Same board, both measured
Long documents
Listed context window
DeepSeek V4 Flash 0423
DeepSeek V4 Flash 0423 takes 1.05M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V4 Flash 0423
DeepSeek V4 Flash 0423 $0.0002, GPT-5 $0.0063 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5 has the number, $0.39.
Directional only
At a glance
- Coding index
- DeepSeek V4 Flash 0423 54.6 from 1 benchmark, GPT-5 72.2 from 3 benchmarks.
- Widest category
- Coding, 12.6 points apart on average over 7 shared benchmarks.
- Widest single gap
- LMArena WebDev: DeepSeek V4 Flash 0423 1582 against GPT-5 1418, 23.5 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4 Flash 0423 $0.182 against GPT-5 $10.00 per million output tokens, 54.9x apart.
- Context window
- DeepSeek V4 Flash 0423 takes 1.05M tokens, GPT-5 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 0423 | Bars | GPT-5 | Ties |
|---|---|---|---|---|
| Coding | 5 | 2 | ||
| Intelligence | 1 | 1 | ||
| Reasoning | 3 | 1 | ||
| Math | 2 | 0 | ||
| Knowledge | 4 | 0 | ||
| Web design | 4 | 4 | ||
| General | 16 | 3 | ||
| All shared boards | 35 | 11 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index52
| Benchmark | DeepSeek V4 Flash 0423 | GPT-5 | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 88.0% | Only GPT-5 measured |
| SWE-bench Verified | Not measured | 74.4% | Only GPT-5 measured |
| LMArena WebDev | 1582 | 1418 | DeepSeek V4 Flash 0423 |
| AA Coding Index | 52.0 | 37.8 | DeepSeek V4 Flash 0423 |
| LiveCodeBench (Vals AI) | Not measured | 85.9% | Only GPT-5 measured |
| SWE-bench Verified (Vals AI) | Not measured | 69.0% | Only GPT-5 measured |
| Vibe Code Bench (Vals AI) | Not measured | 20.1% | Only GPT-5 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 84.6% | Only GPT-5 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 37.9% | Only GPT-5 measured |
| Terminal-Bench | Not measured | 49.6% | Only GPT-5 measured |
| GSO-Bench | Not measured | 6.9% | Only GPT-5 measured |
| WeirdML | 45.6% | 60.7% | GPT-5 |
| ALE-Bench | 678.2 | 807.6 | GPT-5 |
| LMArena Text: coding | 1457 | 1436 | DeepSeek V4 Flash 0423 |
| LMArena Text: software and IT | 1453 | 1420 | DeepSeek V4 Flash 0423 |
| LMArena WebDev: React | 1585 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena WebDev: HTML | 1558 | 1427 | DeepSeek V4 Flash 0423 |
| Neither model has a result on the other 21 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | DeepSeek V4 Flash 0423 | GPT-5 | Leader |
|---|---|---|---|
| AA Intelligence Index | 24.4 | 23.0 | DeepSeek V4 Flash 0423 |
| Epoch Capabilities Index | 146.1 | 150.0 | GPT-5 |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | DeepSeek V4 Flash 0423 | GPT-5 | Leader |
|---|---|---|---|
| AA Agentic Index | 26.3 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| τ²-Bench Telecom | Not measured | 86.5% | Only GPT-5 measured |
| METR time horizons | Not measured | 69.6% | Only GPT-5 measured |
| Remote Labor Index | Not measured | 1.7% | Only GPT-5 measured |
| GDPval | Not measured | 34.8% | Only GPT-5 measured |
| BALROG | Not measured | 32.8% | Only GPT-5 measured |
| DeepResearch Bench | Not measured | 49.6% | Only GPT-5 measured |
| Neither model has a result on the other 32 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index31
| Benchmark | DeepSeek V4 Flash 0423 | GPT-5 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 85.6% | Only GPT-5 measured |
| MMMU Pro (Vals AI) | Not measured | 81.5% | Only GPT-5 measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 85.4% | Only GPT-5 measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 28.5% | Only GPT-5 measured |
| GPQA Diamond (Epoch AI run) | Not measured | 86.2% | Only GPT-5 measured |
| ARC-AGI | Not measured | 65.7% | Only GPT-5 measured |
| ARC-AGI-2 | Not measured | 9.9% | Only GPT-5 measured |
| SimpleBench | 46.3% | 56.7% | GPT-5 |
| Chess puzzles | Not measured | 36.0% | Only GPT-5 measured |
| Mystery game puzzles | Not measured | 23.0% | Only GPT-5 measured |
| EBR-bench | Not measured | 12.7% | Only GPT-5 measured |
| VPCT | Not measured | 66.0% | Only GPT-5 measured |
| LMArena Text: hard prompts | 1444 | 1415 | DeepSeek V4 Flash 0423 |
| LMArena Text: expert | 1441 | 1419 | DeepSeek V4 Flash 0423 |
| LMArena Text: hard prompts in English | 1449 | 1419 | DeepSeek V4 Flash 0423 |
| LMArena Vision: diagrams | Not measured | 1252 | Only GPT-5 measured |
| LMArena Vision: homework | Not measured | 1278 | Only GPT-5 measured |
| Neither model has a result on the other 4 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V4 Flash 0423 | GPT-5 | Leader |
|---|---|---|---|
| AIME | Not measured | 93.4% | Only GPT-5 measured |
| MATH 500 (Vals AI) | Not measured | 96.0% | Only GPT-5 measured |
| MGSM (Vals AI) | Not measured | 92.8% | Only GPT-5 measured |
| AA Math Index | Not measured | 94.3 | Only GPT-5 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 94.3% | Only GPT-5 measured |
| OTIS Mock AIME | Not measured | 91.4% | Only GPT-5 measured |
| FrontierMath, tiers 1 to 3 | Not measured | 55.4% | Only GPT-5 measured |
| FrontierMath, tier 4 | Not measured | 22.0% | Only GPT-5 measured |
| ProofBench | Not measured | 18.0% | Only GPT-5 measured |
| MATH level 5 | Not measured | 98.1% | Only GPT-5 measured |
| LMArena Text: math | 1426 | 1407 | DeepSeek V4 Flash 0423 |
| LMArena Text: mathematical work | 1432 | 1405 | DeepSeek V4 Flash 0423 |
| Neither model has a result on the other 1 board here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | DeepSeek V4 Flash 0423 | GPT-5 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 86.5% | Only GPT-5 measured |
| LegalBench (Vals AI) | Not measured | 86.0% | Only GPT-5 measured |
| CaseLaw (Vals AI) | Not measured | 66.5% | Only GPT-5 measured |
| CorpFin (Vals AI) | Not measured | 61.1% | Only GPT-5 measured |
| TaxEval (Vals AI) | Not measured | 73.4% | Only GPT-5 measured |
| MortgageTax (Vals AI) | Not measured | 65.5% | Only GPT-5 measured |
| MedQA (Vals AI) | Not measured | 96.3% | Only GPT-5 measured |
| MedCode (Vals AI) | Not measured | 49.6% | Only GPT-5 measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 87.1% | Only GPT-5 measured |
| SimpleQA Verified | Not measured | 50.1% | Only GPT-5 measured |
| GeoBench | Not measured | 81.0% | Only GPT-5 measured |
| LMArena Text: science | 1446 | 1416 | DeepSeek V4 Flash 0423 |
| LMArena Text: medicine and healthcare | 1448 | 1413 | DeepSeek V4 Flash 0423 |
| LMArena Text: legal and government | 1439 | 1431 | DeepSeek V4 Flash 0423 |
| LMArena Text: business and finance | 1425 | 1400 | DeepSeek V4 Flash 0423 |
| Neither model has a result on the other 2 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index44
| Benchmark | DeepSeek V4 Flash 0423 | GPT-5 | Leader |
|---|---|---|---|
| Design Arena: websites | 1208 | 1185 | DeepSeek V4 Flash 0423 |
| Design Arena: UI components | 1164 | 1179 | GPT-5 |
| Design Arena: game dev | 1197 | 1185 | DeepSeek V4 Flash 0423 |
| Design Arena: data viz | 1127 | 1221 | GPT-5 |
| Design Arena: 3D | 1189 | 1056 | DeepSeek V4 Flash 0423 |
| Design Arena: all code categories | 1205 | 1168 | DeepSeek V4 Flash 0423 |
| Design Arena: SVG | 1155 | 1188 | GPT-5 |
| Design Arena: ASCII art | 1109 | 1140 | GPT-5 |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index163
| Benchmark | DeepSeek V4 Flash 0423 | GPT-5 | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 43.7% | Only GPT-5 measured |
| MedScribe (Vals AI) | Not measured | 83.7% | Only GPT-5 measured |
| IFBench | Not measured | 73.1% | Only GPT-5 measured |
| Long context reasoning (AA-LCR) | Not measured | 78.2% | Only GPT-5 measured |
| DTBench | 86.4% | 90.7% | GPT-5 |
| LMCA | 35.9% | 40.0% | GPT-5 |
| Fiction.LiveBench | Not measured | 97.2% | Only GPT-5 measured |
| Creative writing (Lech Mazur) | Not measured | 86.0% | Only GPT-5 measured |
| LMArena Text | 1432 | 1406 | DeepSeek V4 Flash 0423 |
| LMArena Text: instruction following | 1421 | 1388 | DeepSeek V4 Flash 0423 |
| LMArena Text: creative writing | 1403 | 1365 | DeepSeek V4 Flash 0423 |
| LMArena Vision | Not measured | 1232 | Only GPT-5 measured |
| LMArena Text: English | 1441 | 1409 | DeepSeek V4 Flash 0423 |
| LMArena Text: non-English | 1419 | 1397 | DeepSeek V4 Flash 0423 |
| LMArena Text: Chinese | 1463 | 1422 | DeepSeek V4 Flash 0423 |
| LMArena Text: German | 1419 | 1416 | DeepSeek V4 Flash 0423 |
| LMArena Text: Japanese | 1389 | 1409 | GPT-5 |
| LMArena Text: Korean | 1383 | 1360 | DeepSeek V4 Flash 0423 |
| LMArena Text: French | 1439 | 1409 | DeepSeek V4 Flash 0423 |
| LMArena Text: Spanish | 1436 | 1399 | DeepSeek V4 Flash 0423 |
| LMArena Text: Russian | 1427 | 1407 | DeepSeek V4 Flash 0423 |
| LMArena Text: Polish | 1436 | 1431 | DeepSeek V4 Flash 0423 |
| LMArena Text: multi-turn | 1449 | 1426 | DeepSeek V4 Flash 0423 |
| LMArena Text: longer queries | 1434 | 1399 | DeepSeek V4 Flash 0423 |
| LMArena Text: writing and language | 1410 | 1384 | DeepSeek V4 Flash 0423 |
| LMArena Text: entertainment and media | 1400 | 1379 | DeepSeek V4 Flash 0423 |
| LMArena Vision: English | Not measured | 1231 | Only GPT-5 measured |
| LMArena Vision: Chinese | Not measured | 1267 | Only GPT-5 measured |
| LMArena Vision: reading text | Not measured | 1246 | Only GPT-5 measured |
| LMArena Vision: humor | Not measured | 1219 | Only GPT-5 measured |
| LMArena Vision: captioning | Not measured | 1212 | Only GPT-5 measured |
| LMArena Vision: recognizing things | Not measured | 1263 | Only GPT-5 measured |
| LMArena Vision: creative writing | Not measured | 1210 | Only GPT-5 measured |
| Neither model has a result on the other 13 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4 Flash 0423 | GPT-5 | 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 0423 costs less
Repository review
50K input + 3K output tokens
DeepSeek V4 Flash 0423 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V4 Flash 0423 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 0423 | OpenAI GPT-5 |
|---|---|---|
| Maker | DeepSeek | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | April 24, 2026 | August 7, 2025 |
| Context window | 1.05M | 400K |
| Input $/Mtok | $0.091 | $1.25 |
| Output $/Mtok | $0.182 | $10.00 |
| Coding index | 54.6 | 72.2 |
| Independent measurements behind it | 1 | 3 |
| Cost per solved task | Not computable | $0.39 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4 Flash 0423 better than GPT-5 for coding?
Pick GPT-5 for coding: it scores 72.2 on the Vibe Coding Index against 54.6 for DeepSeek V4 Flash 0423. Pick DeepSeek V4 Flash 0423 when output tokens cost 54.9x less, or when the context window is larger, 1.05M against 400K tokens.
Which is cheaper, DeepSeek V4 Flash 0423 or GPT-5?
DeepSeek V4 Flash 0423 costs $0.091 per million input tokens and $0.182 per million output tokens; GPT-5 costs $1.25 and $10.00. Cost per solved task is known for GPT-5 only.
Which has the bigger context window, DeepSeek V4 Flash 0423 or GPT-5?
DeepSeek V4 Flash 0423, with 1.05M tokens against 400K.
How many benchmarks is the DeepSeek V4 Flash 0423 vs GPT-5 comparison based on?
46 benchmarks have a result for both models as of October 21, 2026. DeepSeek V4 Flash 0423's coding index rests on 1 independent benchmark and GPT-5's on 3. Where only one model has a result, the page says so instead of naming a winner.