GPT-3.5 Turbo vs Ling 3.1 Flash
Ling 3.1 Flash leads GPT-3.5 Turbo on the Artificial Analysis Intelligence Index, 41.1 to 5.5, and Ling 3.1 Flash costs 6.7x less per output token ($0.225 vs $1.50 per million). Updated October 22, 2026; 1 benchmarks have a result for both.
GPT-3.5 Turbo or Ling 3.1 Flash: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-3.5 Turbo for work measured by AA Intelligence Index, where it leads.
- Shared results
- 1
- GPT-3.5 Turbo only
- 12
- Ling 3.1 Flash only
- 3
- 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
Ling 3.1 Flash
Ling 3.1 Flash leads, 41.1 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
Long documents
Listed context window
Ling 3.1 Flash
Ling 3.1 Flash takes 262K tokens against 16K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ling 3.1 Flash
GPT-3.5 Turbo $0.0013, Ling 3.1 Flash $0.0002 per turn.
List prices
At a glance
- Coding index
- GPT-3.5 Turbo not benchmarked, Ling 3.1 Flash not benchmarked.
- Widest category
- Intelligence, 35.6 points apart on average over 1 shared benchmark.
- Widest single gap
- AA Intelligence Index: GPT-3.5 Turbo 5.5 against Ling 3.1 Flash 41.1, 35.6 points apart on a 0 to 100 scale.
- Output price
- GPT-3.5 Turbo $1.50 against Ling 3.1 Flash $0.225 per million output tokens, 6.7x apart.
- Context window
- GPT-3.5 Turbo takes 16K tokens, Ling 3.1 Flash 262K.
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 | GPT-3.5 Turbo | Bars | Ling 3.1 Flash | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| All shared boards | 0 | 1 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index3 boards, none shared
| Benchmark | GPT-3.5 Turbo | Ling 3.1 Flash | Leader |
|---|---|---|---|
| AA Coding Index | 10.7 | Not measured | Only GPT-3.5 Turbo measured |
| SciCode | Not measured | 54.1% | Only Ling 3.1 Flash measured |
| SciCode | 0.0% | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 40 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-3.5 Turbo | Ling 3.1 Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.5 | 41.1 | Ling 3.1 Flash |
| Neither model has a result on the other 1 board here | |||
Agentic workNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-3.5 Turbo | Ling 3.1 Flash | Leader |
|---|---|---|---|
| Neither model has a result on 42 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-3.5 Turbo | Ling 3.1 Flash | Leader |
|---|---|---|---|
| GPQA Diamond | 30.6% | Not measured | Only GPT-3.5 Turbo measured |
| GPQA Diamond (Artificial Analysis run) | 29.7% | Not measured | Only GPT-3.5 Turbo measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 39.4% | Only Ling 3.1 Flash measured |
| BIG-Bench Hard | 61.6% | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 19 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-3.5 Turbo | Ling 3.1 Flash | Leader |
|---|---|---|---|
| GSM8K | 57.8% | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 13 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-3.5 Turbo | Ling 3.1 Flash | Leader |
|---|---|---|---|
| LegalBench (Vals AI) | 64.4% | Not measured | Only GPT-3.5 Turbo measured |
| MedQA (Vals AI) | 58.5% | Not measured | Only GPT-3.5 Turbo measured |
| MMLU Pro (Artificial Analysis run) | 46.2% | Not measured | Only GPT-3.5 Turbo measured |
| MMLU | 68.5% | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 16 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-3.5 Turbo | Ling 3.1 Flash | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-3.5 Turbo | Ling 3.1 Flash | Leader |
|---|---|---|---|
| Long context reasoning (AA-LCR) | Not measured | 83.0% | Only Ling 3.1 Flash measured |
| DTBench | 48.5% | Not measured | Only GPT-3.5 Turbo measured |
| LMCA | 9.7% | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 45 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-3.5 Turbo | Ling 3.1 Flash | 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
Ling 3.1 Flash costs less
Repository review
50K input + 3K output tokens
Ling 3.1 Flash costs less
Agent loop
200K input + 10K output tokens
Ling 3.1 Flash 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 | OpenAI GPT-3.5 Turbo | inclusionAI Ling 3.1 Flash |
|---|---|---|
| Maker | OpenAI | inclusionAI |
| Weights | No public weights listed | No public weights listed |
| Released | May 28, 2023 | October 2, 2026 |
| Context window | 16K | 262K |
| Input $/Mtok | $0.50 | $0.075 |
| Output $/Mtok | $1.50 | $0.225 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-3.5 Turbo better than Ling 3.1 Flash for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-3.5 Turbo for work measured by AA Intelligence Index, where it leads.
Which is cheaper, GPT-3.5 Turbo or Ling 3.1 Flash?
GPT-3.5 Turbo costs $0.50 per million input tokens and $1.50 per million output tokens; Ling 3.1 Flash costs $0.075 and $0.225. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-3.5 Turbo or Ling 3.1 Flash?
Ling 3.1 Flash, with 262K tokens against 16K.
How many benchmarks is the GPT-3.5 Turbo vs Ling 3.1 Flash comparison based on?
1 benchmarks have a result for both models as of October 22, 2026. GPT-3.5 Turbo's coding index rests on 0 independent benchmarks and Ling 3.1 Flash's on 0. Where only one model has a result, the page says so instead of naming a winner.