GPT-3.5 Turbo vs o1
o1 leads GPT-3.5 Turbo on the Artificial Analysis Intelligence Index, 15.2 to 5.5, and GPT-3.5 Turbo costs 40.0x less per output token ($1.50 vs $60.00 per million). Updated October 22, 2026; 10 benchmarks have a result for both.
GPT-3.5 Turbo or o1: 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 40.0x cheaper ($1.50 vs $60.00 per million) and work measured by AA Coding Index or SciCode, where it leads.
- Shared results
- 10
- GPT-3.5 Turbo only
- 3
- o1 only
- 59
- Like-for-like categories
- 5 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
o1
o1 leads, 15.2 against 5.5.
Same board, both measured
Reasoning
GPQA Diamond
o1
o1 leads, 73.2% against 30.6%.
Same board, both measured
Math
AIME
No call
Only o1 has a AIME result, 71.5%.
Directional only
Knowledge
MMLU Pro
No call
Only o1 has a MMLU Pro result, 83.5%.
Directional only
Long documents
Listed context window
o1
o1 takes 200K tokens against 16K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-3.5 Turbo
GPT-3.5 Turbo $0.0013, o1 $0.045 per turn.
List prices
At a glance
- Coding index
- GPT-3.5 Turbo not benchmarked, o1 not benchmarked.
- Widest category
- Reasoning, 43.8 points apart on average over 2 shared benchmarks.
- Widest single gap
- GPQA Diamond (Artificial Analysis run): GPT-3.5 Turbo 29.7% against o1 74.7%, 45.0 points apart on a 0 to 100 scale.
- Output price
- GPT-3.5 Turbo $1.50 against o1 $60.00 per million output tokens, 40.0x apart.
- Context window
- GPT-3.5 Turbo takes 16K tokens, o1 200K.
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 | o1 | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 2 | ||
| Knowledge | 0 | 3 | ||
| General | 0 | 2 | ||
| All shared boards | 0 | 10 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | GPT-3.5 Turbo | o1 | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 61.7% | Only o1 measured |
| AA Coding Index | 10.7 | 39.7 | o1 |
| LiveCodeBench (Vals AI) | Not measured | 50.3% | Only o1 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 67.9% | Only o1 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 12.9% | Only o1 measured |
| WeirdML | Not measured | 46.1% | Only o1 measured |
| LMArena Text: coding | Not measured | 1367 | Only o1 measured |
| LMArena Text: software and IT | Not measured | 1366 | Only o1 measured |
| SciCode | 0.0% | 4.6% | o1 |
| Neither model has a result on the other 30 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-3.5 Turbo | o1 | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.5 | 15.2 | o1 |
| Epoch Capabilities Index | Not measured | 141.7 | Only o1 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-3.5 Turbo | o1 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 62.6% | Only o1 measured |
| METR time horizons | Not measured | 51.1% | Only o1 measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-3.5 Turbo | o1 | Leader |
|---|---|---|---|
| GPQA Diamond | 30.6% | 73.2% | o1 |
| MMMU Pro (Vals AI) | Not measured | 77.4% | Only o1 measured |
| GPQA Diamond (Artificial Analysis run) | 29.7% | 74.7% | o1 |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 7.0% | Only o1 measured |
| GPQA Diamond (Epoch AI run) | Not measured | 76.8% | Only o1 measured |
| ARC-AGI | Not measured | 30.7% | Only o1 measured |
| SimpleBench | Not measured | 40.1% | Only o1 measured |
| Chess puzzles | Not measured | 10.0% | Only o1 measured |
| VPCT | Not measured | 37.0% | Only o1 measured |
| BIG-Bench Hard | 61.6% | Not measured | Only GPT-3.5 Turbo measured |
| LMArena Text: hard prompts | Not measured | 1371 | Only o1 measured |
| LMArena Text: expert | Not measured | 1361 | Only o1 measured |
| LMArena Text: hard prompts in English | Not measured | 1372 | Only o1 measured |
| Neither model has a result on the other 9 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | GPT-3.5 Turbo | o1 | Leader |
|---|---|---|---|
| AIME | Not measured | 71.5% | Only o1 measured |
| MATH 500 (Vals AI) | Not measured | 90.4% | Only o1 measured |
| MGSM (Vals AI) | Not measured | 89.3% | Only o1 measured |
| OTIS Mock AIME | Not measured | 73.3% | Only o1 measured |
| FrontierMath, tiers 1 to 3 | Not measured | 14.7% | Only o1 measured |
| MATH level 5 | Not measured | 94.7% | Only o1 measured |
| GSM8K | 57.8% | Not measured | Only GPT-3.5 Turbo measured |
| LMArena Text: math | Not measured | 1388 | Only o1 measured |
| LMArena Text: mathematical work | Not measured | 1390 | Only o1 measured |
| Neither model has a result on the other 4 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | GPT-3.5 Turbo | o1 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 83.5% | Only o1 measured |
| LegalBench (Vals AI) | 64.4% | 80.4% | o1 |
| TaxEval (Vals AI) | Not measured | 74.3% | Only o1 measured |
| MedQA (Vals AI) | 58.5% | 96.5% | o1 |
| MMLU Pro (Artificial Analysis run) | 46.2% | 84.1% | o1 |
| SimpleQA Verified | Not measured | 41.1% | Only o1 measured |
| MMLU | 68.5% | Not measured | Only GPT-3.5 Turbo measured |
| GeoBench | Not measured | 80.0% | Only o1 measured |
| LMArena Text: science | Not measured | 1369 | Only o1 measured |
| LMArena Text: medicine and healthcare | Not measured | 1355 | Only o1 measured |
| LMArena Text: legal and government | Not measured | 1368 | Only o1 measured |
| LMArena Text: business and finance | Not measured | 1328 | Only o1 measured |
| Neither model has a result on the other 6 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-3.5 Turbo | o1 | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-3.5 Turbo | o1 | Leader |
|---|---|---|---|
| IFBench | Not measured | 70.3% | Only o1 measured |
| Long context reasoning (AA-LCR) | Not measured | 65.0% | Only o1 measured |
| DTBench | 48.5% | 74.7% | o1 |
| LMCA | 9.7% | 22.3% | o1 |
| Fiction.LiveBench | Not measured | 83.3% | Only o1 measured |
| Creative writing (Lech Mazur) | Not measured | 70.2% | Only o1 measured |
| LMArena Text | Not measured | 1366 | Only o1 measured |
| LMArena Text: instruction following | Not measured | 1367 | Only o1 measured |
| LMArena Text: creative writing | Not measured | 1347 | Only o1 measured |
| LMArena Vision | Not measured | 1168 | Only o1 measured |
| LMArena Text: English | Not measured | 1372 | Only o1 measured |
| LMArena Text: non-English | Not measured | 1358 | Only o1 measured |
| LMArena Text: Chinese | Not measured | 1394 | Only o1 measured |
| LMArena Text: German | Not measured | 1337 | Only o1 measured |
| LMArena Text: Japanese | Not measured | 1346 | Only o1 measured |
| LMArena Text: Korean | Not measured | 1396 | Only o1 measured |
| LMArena Text: French | Not measured | 1344 | Only o1 measured |
| LMArena Text: Spanish | Not measured | 1345 | Only o1 measured |
| LMArena Text: Russian | Not measured | 1356 | Only o1 measured |
| LMArena Text: multi-turn | Not measured | 1355 | Only o1 measured |
| LMArena Text: longer queries | Not measured | 1378 | Only o1 measured |
| LMArena Text: writing and language | Not measured | 1360 | Only o1 measured |
| LMArena Text: entertainment and media | Not measured | 1358 | Only o1 measured |
| LMArena Vision: English | Not measured | 1162 | Only o1 measured |
| LMArena Vision: Chinese | Not measured | 1235 | Only o1 measured |
| Neither model has a result on the other 21 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-3.5 Turbo | o1 | 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 o1 |
|---|---|---|
| Maker | OpenAI | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | May 28, 2023 | December 17, 2024 |
| Context window | 16K | 200K |
| Input $/Mtok | $0.50 | $15.00 |
| Output $/Mtok | $1.50 | $60.00 |
| 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 o1 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 40.0x cheaper ($1.50 vs $60.00 per million) and work measured by AA Coding Index or SciCode, where it leads.
Which is cheaper, GPT-3.5 Turbo or o1?
GPT-3.5 Turbo costs $0.50 per million input tokens and $1.50 per million output tokens; o1 costs $15.00 and $60.00. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-3.5 Turbo or o1?
o1, with 200K tokens against 16K.
How many benchmarks is the GPT-3.5 Turbo vs o1 comparison based on?
10 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 o1's on 0. Where only one model has a result, the page says so instead of naming a winner.