DeepSeek V4 Flash 0423 vs GPT-3.5 Turbo 16k
DeepSeek V4 Flash 0423 costs 14.3x less per output token than GPT-3.5 Turbo 16k ($0.28 vs $4.00 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.28
- Coding
- No data
- Overall
- No data
- Output $/M
- $4.00
DeepSeek V4 Flash 0423 or GPT-3.5 Turbo 16k: 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 14.3x cheaper ($0.28 vs $4.00 per million) and a larger context window (1.05M against 16K tokens).
No benchmark has tested both yet. DeepSeek V4 Flash 0423 has results on LMArena WebDev, AA Coding Index and WeirdML; GPT-3.5 Turbo 16k has none yet. Compare them on price and context instead.
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
No call
Only DeepSeek V4 Flash 0423 has a AA Intelligence Index result, 24.4.
Directional only
Agentic work
AA Agentic Index
No call
Only DeepSeek V4 Flash 0423 has a AA Agentic Index result, 26.3.
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 16k $0.0050 per turn.
List prices
At a glance
- Coding index
- DeepSeek V4 Flash 0423 not benchmarked, GPT-3.5 Turbo 16k not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- DeepSeek V4 Flash 0423 $0.28 against GPT-3.5 Turbo 16k $4.00 per million output tokens, 14.3x apart.
- Context window
- DeepSeek V4 Flash 0423 takes 1.05M tokens, GPT-3.5 Turbo 16k 16K.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index8 boards measured
| Benchmark | DeepSeek V4 Flash 0423 | GPT-3.5 Turbo 16k | Leader |
|---|---|---|---|
| LMArena WebDev | 1582 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| AA Coding Index | 52.0 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| 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 |
| Neither model has a result on the other 35 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | DeepSeek V4 Flash 0423 | GPT-3.5 Turbo 16k | Leader |
|---|---|---|---|
| AA Intelligence Index | 24.4 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| 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 measured
| Benchmark | DeepSeek V4 Flash 0423 | GPT-3.5 Turbo 16k | Leader |
|---|---|---|---|
| AA Agentic Index | 26.3 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Neither model has a result on the other 41 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | DeepSeek V4 Flash 0423 | GPT-3.5 Turbo 16k | Leader |
|---|---|---|---|
| SimpleBench | 46.3% | Not measured | Only DeepSeek V4 Flash 0423 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 18 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | DeepSeek V4 Flash 0423 | GPT-3.5 Turbo 16k | Leader |
|---|---|---|---|
| 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 |
| MathArena | 24.8% | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | DeepSeek V4 Flash 0423 | GPT-3.5 Turbo 16k | Leader |
|---|---|---|---|
| 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 16 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards measured
| Benchmark | DeepSeek V4 Flash 0423 | GPT-3.5 Turbo 16k | 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 | 1154 | 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 | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index19 boards measured
| Benchmark | DeepSeek V4 Flash 0423 | GPT-3.5 Turbo 16k | Leader |
|---|---|---|---|
| DTBench | 86.4% | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMCA | 35.9% | Not measured | Only DeepSeek V4 Flash 0423 measured |
| 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 29 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 16k |
|---|---|---|
| Maker | DeepSeek | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | April 24, 2026 | August 28, 2023 |
| Context window | 1.05M | 16K |
| Input $/Mtok | $0.13 | $3.00 |
| Output $/Mtok | $0.28 | $4.00 |
| 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 16k 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 14.3x cheaper ($0.28 vs $4.00 per million) and a larger context window (1.05M against 16K tokens).
Which is cheaper, DeepSeek V4 Flash 0423 or GPT-3.5 Turbo 16k?
DeepSeek V4 Flash 0423 costs $0.13 per million input tokens and $0.28 per million output tokens; GPT-3.5 Turbo 16k costs $3.00 and $4.00. 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 16k?
DeepSeek V4 Flash 0423, with 1.05M tokens against 16K.
Has any benchmark tested both DeepSeek V4 Flash 0423 and GPT-3.5 Turbo 16k?
No benchmark has tested both yet. DeepSeek V4 Flash 0423 has results on LMArena WebDev, AA Coding Index and WeirdML; GPT-3.5 Turbo 16k has none yet. Compare them on price and context instead. Data as of October 22, 2026.