GPT-3.5 Turbo vs GPT-3.5 Turbo (older v0613)
GPT-3.5 Turbo costs 1.3x less per output token than GPT-3.5 Turbo (older v0613) ($1.50 vs $2.00 per million). Updated October 23, 2026; 2 benchmarks have a result for both.
GPT-3.5 Turbo or GPT-3.5 Turbo (older v0613): which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-3.5 Turbo for output tokens 1.3x cheaper ($1.50 vs $2.00 per million) and a larger context window (16K against 4K tokens).
- Shared results
- 2
- GPT-3.5 Turbo only
- 11
- GPT-3.5 Turbo (older v0613) only
- 1
- Like-for-like categories
- 2 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
No call
Only GPT-3.5 Turbo has a AA Intelligence Index result, 5.5.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-3.5 Turbo has a GPQA Diamond result, 30.6%.
Directional only
Long documents
Listed context window
GPT-3.5 Turbo
GPT-3.5 Turbo takes 16K tokens against 4K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-3.5 Turbo
GPT-3.5 Turbo $0.0013, GPT-3.5 Turbo (older v0613) $0.0020 per turn.
List prices
At a glance
- Coding index
- GPT-3.5 Turbo not benchmarked, GPT-3.5 Turbo (older v0613) not benchmarked.
- Widest category
- Math, 10.9 points apart on average over 1 shared benchmark.
- Widest single gap
- GSM8K: GPT-3.5 Turbo 57.8% against GPT-3.5 Turbo (older v0613) 46.9%, 10.9 points apart on a 0 to 100 scale.
- Output price
- GPT-3.5 Turbo $1.50 against GPT-3.5 Turbo (older v0613) $2.00 per million output tokens, 1.3x apart.
- Context window
- GPT-3.5 Turbo takes 16K tokens, GPT-3.5 Turbo (older v0613) 4K.
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 | GPT-3.5 Turbo (older v0613) | Ties |
|---|---|---|---|---|
| Math | 1 | 0 | ||
| Knowledge | 0 | 1 | ||
| All shared boards | 1 | 1 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index2 boards, none shared
| Benchmark | GPT-3.5 Turbo | GPT-3.5 Turbo (older v0613) | Leader |
|---|---|---|---|
| AA Coding Index | 10.7 | Not measured | Only GPT-3.5 Turbo measured |
| SciCode | 0.0% | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 41 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-3.5 Turbo | GPT-3.5 Turbo (older v0613) | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.5 | Not measured | Only GPT-3.5 Turbo measured |
| 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 | GPT-3.5 Turbo (older v0613) | Leader |
|---|---|---|---|
| Neither model has a result on 42 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-3.5 Turbo | GPT-3.5 Turbo (older v0613) | 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 |
| BIG-Bench Hard | 61.6% | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 20 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-3.5 Turbo | GPT-3.5 Turbo (older v0613) | Leader |
|---|---|---|---|
| GSM8K | 57.8% | 46.9% | GPT-3.5 Turbo |
| Neither model has a result on the other 13 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-3.5 Turbo | GPT-3.5 Turbo (older v0613) | 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% | 68.9% | GPT-3.5 Turbo (older v0613) |
| 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 | GPT-3.5 Turbo (older v0613) | 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 | GPT-3.5 Turbo (older v0613) | Leader |
|---|---|---|---|
| DTBench | 48.5% | Not measured | Only GPT-3.5 Turbo measured |
| LMCA | 9.7% | Not measured | Only GPT-3.5 Turbo measured |
| WinoGrande | Not measured | 81.6% | Only GPT-3.5 Turbo (older v0613) 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 | GPT-3.5 Turbo (older v0613) | 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
GPT-3.5 Turbo costs less
Repository review
50K input + 3K output tokens
GPT-3.5 Turbo costs less
Agent loop
200K input + 10K output tokens
GPT-3.5 Turbo 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 | OpenAI GPT-3.5 Turbo (older v0613) |
|---|---|---|
| Maker | OpenAI | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | May 28, 2023 | January 25, 2024 |
| Context window | 16K | 4K |
| Input $/Mtok | $0.50 | $1.00 |
| Output $/Mtok | $1.50 | $2.00 |
| 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 GPT-3.5 Turbo (older v0613) for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-3.5 Turbo for output tokens 1.3x cheaper ($1.50 vs $2.00 per million) and a larger context window (16K against 4K tokens).
Which is cheaper, GPT-3.5 Turbo or GPT-3.5 Turbo (older v0613)?
GPT-3.5 Turbo costs $0.50 per million input tokens and $1.50 per million output tokens; GPT-3.5 Turbo (older v0613) costs $1.00 and $2.00. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-3.5 Turbo or GPT-3.5 Turbo (older v0613)?
GPT-3.5 Turbo, with 16K tokens against 4K.
How many benchmarks is the GPT-3.5 Turbo vs GPT-3.5 Turbo (older v0613) comparison based on?
2 benchmarks have a result for both models as of October 23, 2026. GPT-3.5 Turbo's coding index rests on 0 independent benchmarks and GPT-3.5 Turbo (older v0613)'s on 0. Where only one model has a result, the page says so instead of naming a winner.