DeepSeek V4 Flash 0423 vs GPT-3.5 Turbo
DeepSeek V4 Flash 0423 leads GPT-3.5 Turbo on the Artificial Analysis Intelligence Index, 24.4 to 5.5, and DeepSeek V4 Flash 0423 costs 5.4x less per output token ($0.28 vs $1.50 per million). Updated October 22, 2026; 4 benchmarks have a result for both.
DeepSeek V4 Flash 0423 or GPT-3.5 Turbo: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick DeepSeek V4 Flash 0423 for output tokens 5.4x cheaper ($0.28 vs $1.50 per million) and a larger context window (1.05M against 16K tokens).
- Shared results
- 4
- DeepSeek V4 Flash 0423 only
- 45
- GPT-3.5 Turbo only
- 9
- Like-for-like categories
- 3 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
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
DeepSeek V4 Flash 0423
DeepSeek V4 Flash 0423 leads, 24.4 against 5.5.
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-3.5 Turbo has a GPQA Diamond result, 30.6%.
Directional only
Web design
Design Arena: websites
No call
Only DeepSeek V4 Flash 0423 has a Design Arena: websites result, 1207.
Directional only
Long documents
Listed context window
DeepSeek V4 Flash 0423
DeepSeek V4 Flash 0423 takes 1.05M tokens against 16K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V4 Flash 0423
DeepSeek V4 Flash 0423 $0.0003, GPT-3.5 Turbo $0.0013 per turn.
List prices
At a glance
- Coding index
- DeepSeek V4 Flash 0423 not benchmarked, GPT-3.5 Turbo not benchmarked.
- Widest category
- Coding, 41.3 points apart on average over 1 shared benchmark.
- Widest single gap
- AA Coding Index: DeepSeek V4 Flash 0423 52.0 against GPT-3.5 Turbo 10.7, 41.3 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4 Flash 0423 $0.28 against GPT-3.5 Turbo $1.50 per million output tokens, 5.4x apart.
- Context window
- DeepSeek V4 Flash 0423 takes 1.05M tokens, GPT-3.5 Turbo 16K.
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-3.5 Turbo | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| Intelligence | 1 | 0 | ||
| General | 2 | 0 | ||
| All shared boards | 4 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
| Benchmark | DeepSeek V4 Flash 0423 | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| LMArena WebDev | 1582 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| AA Coding Index | 52.0 | 10.7 | DeepSeek V4 Flash 0423 |
| WeirdML | 45.6% | Not measured | Only DeepSeek V4 Flash 0423 measured |
| ALE-Bench | 678.2 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: coding | 1457 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: software and IT | 1453 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena WebDev: React | 1585 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena WebDev: HTML | 1558 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| SciCode | Not measured | 0.0% | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 30 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4 Flash 0423 | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| AA Intelligence Index | 24.4 | 5.5 | DeepSeek V4 Flash 0423 |
| Epoch Capabilities Index | 146.1 | Not measured | Only DeepSeek V4 Flash 0423 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | DeepSeek V4 Flash 0423 | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| AA Agentic Index | 26.3 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | DeepSeek V4 Flash 0423 | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 30.6% | Only GPT-3.5 Turbo measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 29.7% | Only GPT-3.5 Turbo measured |
| SimpleBench | 46.3% | Not measured | Only DeepSeek V4 Flash 0423 measured |
| BIG-Bench Hard | Not measured | 61.6% | Only GPT-3.5 Turbo measured |
| LMArena Text: hard prompts | 1444 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: expert | 1441 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: hard prompts in English | 1449 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Kagi LLM Benchmark | 52.2% | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Neither model has a result on the other 14 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | DeepSeek V4 Flash 0423 | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| GSM8K | Not measured | 57.8% | Only GPT-3.5 Turbo measured |
| LMArena Text: math | 1426 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: mathematical work | 1432 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | DeepSeek V4 Flash 0423 | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| LegalBench (Vals AI) | Not measured | 64.4% | Only GPT-3.5 Turbo measured |
| MedQA (Vals AI) | Not measured | 58.5% | Only GPT-3.5 Turbo measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 46.2% | Only GPT-3.5 Turbo measured |
| MMLU | Not measured | 68.5% | Only GPT-3.5 Turbo measured |
| LMArena Text: science | 1446 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: medicine and healthcare | 1448 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: legal and government | 1439 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: business and finance | 1425 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Neither model has a result on the other 10 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | DeepSeek V4 Flash 0423 | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| Design Arena: websites | 1207 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Design Arena: UI components | 1162 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Design Arena: game dev | 1196 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Design Arena: data viz | 1127 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Design Arena: 3D | 1188 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Design Arena: all code categories | 1205 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Design Arena: SVG | 1155 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Design Arena: ASCII art | 1109 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V4 Flash 0423 | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| DTBench | 86.4% | 48.5% | DeepSeek V4 Flash 0423 |
| LMCA | 35.9% | 9.7% | DeepSeek V4 Flash 0423 |
| LMArena Text | 1432 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: instruction following | 1421 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: creative writing | 1403 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: English | 1441 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: non-English | 1419 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: Chinese | 1463 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: German | 1419 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: Japanese | 1389 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: Korean | 1383 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: French | 1439 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: Spanish | 1436 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: Russian | 1427 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: Polish | 1436 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: multi-turn | 1449 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: longer queries | 1434 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: writing and language | 1410 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: entertainment and media | 1400 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Neither model has a result on the other 27 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4 Flash 0423 | GPT-3.5 Turbo | 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-3.5 Turbo |
|---|---|---|
| Maker | DeepSeek | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | April 24, 2026 | May 28, 2023 |
| Context window | 1.05M | 16K |
| Input $/Mtok | $0.14 | $0.50 |
| Output $/Mtok | $0.28 | $1.50 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4 Flash 0423 better than GPT-3.5 Turbo for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick DeepSeek V4 Flash 0423 for output tokens 5.4x cheaper ($0.28 vs $1.50 per million) and a larger context window (1.05M against 16K tokens).
Which is cheaper, DeepSeek V4 Flash 0423 or GPT-3.5 Turbo?
DeepSeek V4 Flash 0423 costs $0.14 per million input tokens and $0.28 per million output tokens; GPT-3.5 Turbo costs $0.50 and $1.50. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V4 Flash 0423 or GPT-3.5 Turbo?
DeepSeek V4 Flash 0423, with 1.05M tokens against 16K.
How many benchmarks is the DeepSeek V4 Flash 0423 vs GPT-3.5 Turbo comparison based on?
4 benchmarks have a result for both models as of October 22, 2026. DeepSeek V4 Flash 0423's coding index rests on 1 independent benchmark and GPT-3.5 Turbo's on 0. Where only one model has a result, the page says so instead of naming a winner.