DeepSeek V4.1 Flash 1017 vs GPT-3.5 Turbo
DeepSeek V4.1 Flash 1017 leads GPT-3.5 Turbo on the Artificial Analysis Intelligence Index, 39.5 to 5.5, and DeepSeek V4.1 Flash 1017 costs 2.5x less per output token ($0.60 vs $1.50 per million). Updated October 22, 2026; 1 benchmarks have a result for both.
DeepSeek V4.1 Flash 1017 or GPT-3.5 Turbo: which to pick
Only DeepSeek V4.1 Flash 1017 has a coding score, 70.3 on the Vibe Coding Index; GPT-3.5 Turbo has none, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
- Shared results
- 1
- DeepSeek V4.1 Flash 1017 only
- 38
- GPT-3.5 Turbo only
- 12
- Like-for-like categories
- 1 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
Only DeepSeek V4.1 Flash 1017 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 leads, 39.5 against 5.5.
Same board, both measured
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 V4.1 Flash 1017 has a Design Arena: websites result, 1310.
Directional only
General
Vals Index
No call
Only DeepSeek V4.1 Flash 1017 has a Vals Index result, 61.3%.
Directional only
Long documents
Listed context window
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 takes 1.05M tokens against 16K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 $0.0004, GPT-3.5 Turbo $0.0013 per turn.
List prices
At a glance
- Coding index
- DeepSeek V4.1 Flash 1017 70.3 from 4 benchmarks, GPT-3.5 Turbo not benchmarked.
- Widest category
- Intelligence, 34.0 points apart on average over 1 shared benchmark.
- Widest single gap
- AA Intelligence Index: DeepSeek V4.1 Flash 1017 39.5 against GPT-3.5 Turbo 5.5, 34.0 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4.1 Flash 1017 $0.60 against GPT-3.5 Turbo $1.50 per million output tokens, 2.5x apart.
- Context window
- DeepSeek V4.1 Flash 1017 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 V4.1 Flash 1017 | Bars | GPT-3.5 Turbo | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| All shared boards | 1 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index11 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| LiveBench coding | 96.0% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LMArena WebDev | 1664 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| AA Coding Index | Not measured | 10.7 | Only GPT-3.5 Turbo measured |
| Terminal-Bench 4 (Vals AI) | 31.8% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Vibe Code Bench (Vals AI) | 87.7% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Code migration (Vals AI) | 44.2% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| SciCode | 55.9% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| ALE-Bench | 1622.0 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LMArena WebDev: React | 1667 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LMArena WebDev: HTML | 1687 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| SciCode | Not measured | 0.0% | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 32 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4.1 Flash 1017 | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| AA Intelligence Index | 39.5 | 5.5 | DeepSeek V4.1 Flash 1017 |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| Design Arena: spreadsheets | 1259 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench research | 79.5% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench browser | 76.0% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench long agentic | 50.9% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Finance Agent (Vals AI) | 57.0% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Tax Agent Bench (Vals AI) | 44.4% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Legal Research Bench (Vals AI) | 45.2% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Harvey's Legal Agent Benchmark (Vals AI) | 14.2% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Excel Modeling Benchmark (Vals AI) | 61.8% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Neither model has a result on the other 33 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 30.6% | Only GPT-3.5 Turbo measured |
| LiveBench reasoning | 89.1% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 29.7% | Only GPT-3.5 Turbo measured |
| Humanity's Last Exam (Artificial Analysis run) | 43.3% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| GPQA Diamond (Epoch AI run) | 93.2% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Chess puzzles | 46.0% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Mystery game puzzles | 49.0% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| BIG-Bench Hard | Not measured | 61.6% | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 15 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| OTIS Mock AIME | 99.4% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| FrontierMath, tiers 1 to 3 | 71.2% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| FrontierMath, tier 4 | 46.3% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| GSM8K | Not measured | 57.8% | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | 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 |
| SimpleQA Verified | 42.5% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| MMLU | Not measured | 68.5% | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 15 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| Design Arena: websites | 1310 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: UI components | 1306 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: game dev | 1352 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: data viz | 1306 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: 3D | 1363 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: all code categories | 1330 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: ASCII art | 1233 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench design | 83.4% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Neither model has a result on the other 1 board here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| Vals Index | 61.3% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench data analysis | 79.5% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Long context reasoning (AA-LCR) | 82.7% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| DTBench | Not measured | 48.5% | Only GPT-3.5 Turbo measured |
| LMCA | Not measured | 9.7% | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 43 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | 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 V4.1 Flash 1017 costs less
Repository review
50K input + 3K output tokens
DeepSeek V4.1 Flash 1017 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V4.1 Flash 1017 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 1017 | OpenAI GPT-3.5 Turbo |
|---|---|---|
| Maker | DeepSeek | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | October 17, 2026 | May 28, 2023 |
| Context window | 1.05M | 16K |
| Input $/Mtok | $0.15 | $0.50 |
| Output $/Mtok | $0.60 | $1.50 |
| Coding index | 70.3 | Not benchmarked |
| Independent measurements behind it | 4 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4.1 Flash 1017 better than GPT-3.5 Turbo for coding?
Only DeepSeek V4.1 Flash 1017 has a coding score, 70.3 on the Vibe Coding Index; GPT-3.5 Turbo has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, DeepSeek V4.1 Flash 1017 or GPT-3.5 Turbo?
DeepSeek V4.1 Flash 1017 costs $0.15 per million input tokens and $0.60 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 V4.1 Flash 1017 or GPT-3.5 Turbo?
DeepSeek V4.1 Flash 1017, with 1.05M tokens against 16K.
How many benchmarks is the DeepSeek V4.1 Flash 1017 vs GPT-3.5 Turbo comparison based on?
1 benchmarks have a result for both models as of October 22, 2026. DeepSeek V4.1 Flash 1017's coding index rests on 4 independent benchmarks and GPT-3.5 Turbo's on 0. Where only one model has a result, the page says so instead of naming a winner.