GPT-3.5 Turbo vs GPT-4o (2024-08-06)
GPT-4o (2024-08-06) leads GPT-3.5 Turbo on the Artificial Analysis Intelligence Index, 7.7 to 5.5, and GPT-3.5 Turbo costs 6.7x less per output token ($1.50 vs $10.00 per million). Updated October 22, 2026; 5 benchmarks have a result for both.
GPT-3.5 Turbo or GPT-4o (2024-08-06): 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 6.7x cheaper ($1.50 vs $10.00 per million) and work measured by AA Intelligence Index or GPQA Diamond (Artificial Analysis run), where it leads.
- Shared results
- 5
- GPT-3.5 Turbo only
- 8
- GPT-4o (2024-08-06) only
- 53
- Like-for-like categories
- 3 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
GPT-4o (2024-08-06)
GPT-4o (2024-08-06) leads, 7.7 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
Math
AIME
No call
Only GPT-4o (2024-08-06) has a AIME result, 14.0%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-4o (2024-08-06) has a MMLU Pro result, 74.1%.
Directional only
Long documents
Listed context window
GPT-4o (2024-08-06)
GPT-4o (2024-08-06) takes 128K tokens against 16K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-3.5 Turbo
GPT-3.5 Turbo $0.0013, GPT-4o (2024-08-06) $0.0075 per turn.
List prices
At a glance
- Coding index
- GPT-3.5 Turbo not benchmarked, GPT-4o (2024-08-06) not benchmarked.
- Widest category
- Reasoning, 22.4 points apart on average over 1 shared benchmark.
- Widest single gap
- MedQA (Vals AI): GPT-3.5 Turbo 58.5% against GPT-4o (2024-08-06) 88.2%, 29.7 points apart on a 0 to 100 scale.
- Output price
- GPT-3.5 Turbo $1.50 against GPT-4o (2024-08-06) $10.00 per million output tokens, 6.7x apart.
- Context window
- GPT-3.5 Turbo takes 16K tokens, GPT-4o (2024-08-06) 128K.
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-4o (2024-08-06) | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 1 | ||
| Knowledge | 0 | 3 | ||
| All shared boards | 0 | 5 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index8 boards, none shared
| Benchmark | GPT-3.5 Turbo | GPT-4o (2024-08-06) | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 23.1% | Only GPT-4o (2024-08-06) measured |
| SWE-bench Lite | Not measured | 21.7% | Only GPT-4o (2024-08-06) measured |
| AA Coding Index | 10.7 | Not measured | Only GPT-3.5 Turbo measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 31.7% | Only GPT-4o (2024-08-06) measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 8.3% | Only GPT-4o (2024-08-06) measured |
| LMArena Text: coding | Not measured | 1283 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: software and IT | Not measured | 1278 | Only GPT-4o (2024-08-06) measured |
| SciCode | 0.0% | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 35 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-3.5 Turbo | GPT-4o (2024-08-06) | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.5 | 7.7 | GPT-4o (2024-08-06) |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-3.5 Turbo | GPT-4o (2024-08-06) | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 28.9% | Only GPT-4o (2024-08-06) measured |
| Neither model has a result on the other 41 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-3.5 Turbo | GPT-4o (2024-08-06) | Leader |
|---|---|---|---|
| GPQA Diamond | 30.6% | Not measured | Only GPT-3.5 Turbo measured |
| MMMU Pro (Vals AI) | Not measured | 64.0% | Only GPT-4o (2024-08-06) measured |
| GPQA Diamond (Artificial Analysis run) | 29.7% | 52.1% | GPT-4o (2024-08-06) |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 2.3% | Only GPT-4o (2024-08-06) measured |
| GPQA Diamond (Epoch AI run) | Not measured | 49.2% | Only GPT-4o (2024-08-06) measured |
| SimpleBench | Not measured | 17.8% | Only GPT-4o (2024-08-06) measured |
| Chess puzzles | Not measured | 6.0% | Only GPT-4o (2024-08-06) measured |
| BIG-Bench Hard | 61.6% | Not measured | Only GPT-3.5 Turbo measured |
| LMArena Text: hard prompts | Not measured | 1264 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: expert | Not measured | 1241 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: hard prompts in English | Not measured | 1277 | Only GPT-4o (2024-08-06) measured |
| Neither model has a result on the other 12 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | GPT-3.5 Turbo | GPT-4o (2024-08-06) | Leader |
|---|---|---|---|
| AIME | Not measured | 14.0% | Only GPT-4o (2024-08-06) measured |
| MATH 500 (Vals AI) | Not measured | 75.2% | Only GPT-4o (2024-08-06) measured |
| MGSM (Vals AI) | Not measured | 90.7% | Only GPT-4o (2024-08-06) measured |
| OTIS Mock AIME | Not measured | 6.4% | Only GPT-4o (2024-08-06) measured |
| FrontierMath, tiers 1 to 3 | Not measured | 0.4% | Only GPT-4o (2024-08-06) measured |
| MATH level 5 | Not measured | 53.3% | Only GPT-4o (2024-08-06) measured |
| GSM8K | 57.8% | Not measured | Only GPT-3.5 Turbo measured |
| LMArena Text: math | Not measured | 1285 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: mathematical work | Not measured | 1283 | Only GPT-4o (2024-08-06) measured |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | GPT-3.5 Turbo | GPT-4o (2024-08-06) | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 74.1% | Only GPT-4o (2024-08-06) measured |
| LegalBench (Vals AI) | 64.4% | 80.1% | GPT-4o (2024-08-06) |
| CorpFin (Vals AI) | Not measured | 39.4% | Only GPT-4o (2024-08-06) measured |
| TaxEval (Vals AI) | Not measured | 71.1% | Only GPT-4o (2024-08-06) measured |
| MortgageTax (Vals AI) | Not measured | 61.0% | Only GPT-4o (2024-08-06) measured |
| MedQA (Vals AI) | 58.5% | 88.2% | GPT-4o (2024-08-06) |
| MMLU Pro (Artificial Analysis run) | 46.2% | Not measured | Only GPT-3.5 Turbo measured |
| SimpleQA Verified | Not measured | 26.0% | Only GPT-4o (2024-08-06) measured |
| MMLU | 68.5% | 84.3% | GPT-4o (2024-08-06) |
| LMArena Text: science | Not measured | 1274 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: medicine and healthcare | Not measured | 1252 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: legal and government | Not measured | 1275 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: business and finance | Not measured | 1232 | Only GPT-4o (2024-08-06) measured |
| Neither model has a result on the other 7 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-3.5 Turbo | GPT-4o (2024-08-06) | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index23 boards, none shared
| Benchmark | GPT-3.5 Turbo | GPT-4o (2024-08-06) | Leader |
|---|---|---|---|
| IFBench | Not measured | 36.0% | Only GPT-4o (2024-08-06) measured |
| Long context reasoning (AA-LCR) | Not measured | 41.0% | Only GPT-4o (2024-08-06) measured |
| DTBench | 48.5% | Not measured | Only GPT-3.5 Turbo measured |
| LMCA | 9.7% | Not measured | Only GPT-3.5 Turbo measured |
| LMArena Text | Not measured | 1283 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: instruction following | Not measured | 1267 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: creative writing | Not measured | 1274 | Only GPT-4o (2024-08-06) measured |
| LMArena Vision | Not measured | 1065 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: English | Not measured | 1297 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: non-English | Not measured | 1262 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: Chinese | Not measured | 1254 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: German | Not measured | 1257 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: Japanese | Not measured | 1234 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: Korean | Not measured | 1218 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: French | Not measured | 1263 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: Spanish | Not measured | 1269 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: Russian | Not measured | 1272 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: multi-turn | Not measured | 1279 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: longer queries | Not measured | 1283 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: writing and language | Not measured | 1290 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: entertainment and media | Not measured | 1267 | Only GPT-4o (2024-08-06) measured |
| LMArena Vision: English | Not measured | 1072 | Only GPT-4o (2024-08-06) measured |
| LMArena Vision: Chinese | Not measured | 1009 | Only GPT-4o (2024-08-06) measured |
| Neither model has a result on the other 25 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-3.5 Turbo | GPT-4o (2024-08-06) | 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-4o (2024-08-06) |
|---|---|---|
| Maker | OpenAI | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | May 28, 2023 | August 6, 2024 |
| Context window | 16K | 128K |
| Input $/Mtok | $0.50 | $2.50 |
| Output $/Mtok | $1.50 | $10.00 |
| 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-4o (2024-08-06) 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 6.7x cheaper ($1.50 vs $10.00 per million) and work measured by AA Intelligence Index or GPQA Diamond (Artificial Analysis run), where it leads.
Which is cheaper, GPT-3.5 Turbo or GPT-4o (2024-08-06)?
GPT-3.5 Turbo costs $0.50 per million input tokens and $1.50 per million output tokens; GPT-4o (2024-08-06) costs $2.50 and $10.00. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-3.5 Turbo or GPT-4o (2024-08-06)?
GPT-4o (2024-08-06), with 128K tokens against 16K.
How many benchmarks is the GPT-3.5 Turbo vs GPT-4o (2024-08-06) comparison based on?
5 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 GPT-4o (2024-08-06)'s on 0. Where only one model has a result, the page says so instead of naming a winner.