GPT-3.5 Turbo vs GPT-4o-mini
GPT-4o-mini leads GPT-3.5 Turbo on the Artificial Analysis Intelligence Index, 6.7 to 5.5, and GPT-4o-mini costs 2.5x less per output token ($0.60 vs $1.50 per million). Updated October 22, 2026; 11 benchmarks have a result for both.
GPT-3.5 Turbo or GPT-4o-mini: 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 Coding Index or SciCode, where it leads.
- Shared results
- 11
- GPT-3.5 Turbo only
- 2
- GPT-4o-mini only
- 18
- Like-for-like categories
- 6 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-mini
GPT-4o-mini leads, 6.7 against 5.5.
Same board, both measured
Reasoning
GPQA Diamond
GPT-4o-mini
GPT-4o-mini leads, 44.2% against 30.6%.
Same board, both measured
Math
AIME
No call
Only GPT-4o-mini has a AIME result, 11.5%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-4o-mini has a MMLU Pro result, 62.7%.
Directional only
Long documents
Listed context window
GPT-4o-mini
GPT-4o-mini takes 128K tokens against 16K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-4o-mini
GPT-3.5 Turbo $0.0013, GPT-4o-mini $0.0004 per turn.
List prices
At a glance
- Coding index
- GPT-3.5 Turbo not benchmarked, GPT-4o-mini not benchmarked.
- Widest category
- Math, 33.5 points apart on average over 1 shared benchmark.
- Widest single gap
- GSM8K: GPT-3.5 Turbo 57.8% against GPT-4o-mini 91.3%, 33.5 points apart on a 0 to 100 scale.
- Output price
- GPT-3.5 Turbo $1.50 against GPT-4o-mini $0.60 per million output tokens, 2.5x apart.
- Context window
- GPT-3.5 Turbo takes 16K tokens, GPT-4o-mini 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-mini | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 2 | ||
| Math | 0 | 1 | ||
| Knowledge | 0 | 3 | ||
| General | 0 | 2 | ||
| All shared boards | 0 | 11 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | GPT-3.5 Turbo | GPT-4o-mini | Leader |
|---|---|---|---|
| AA Coding Index | 10.7 | 11.4 | GPT-4o-mini |
| LiveCodeBench (Vals AI) | Not measured | 26.4% | Only GPT-4o-mini measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 23.4% | Only GPT-4o-mini measured |
| SciCode | 0.0% | 1.5% | GPT-4o-mini |
| 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-mini | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.5 | 6.7 | GPT-4o-mini |
| Epoch Capabilities Index | Not measured | 126.5 | Only GPT-4o-mini measured |
Agentic workNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-3.5 Turbo | GPT-4o-mini | Leader |
|---|---|---|---|
| Neither model has a result on 41 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-3.5 Turbo | GPT-4o-mini | Leader |
|---|---|---|---|
| GPQA Diamond | 30.6% | 44.2% | GPT-4o-mini |
| MMMU Pro (Vals AI) | Not measured | 56.6% | Only GPT-4o-mini measured |
| GPQA Diamond (Artificial Analysis run) | 29.7% | 42.6% | GPT-4o-mini |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 4.2% | Only GPT-4o-mini measured |
| ARC-AGI-2 | Not measured | 0.0% | Only GPT-4o-mini measured |
| BIG-Bench Hard | 61.6% | Not measured | Only GPT-3.5 Turbo measured |
| Kagi LLM Benchmark | Not measured | 28.8% | Only GPT-4o-mini measured |
| Neither model has a result on the other 15 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-3.5 Turbo | GPT-4o-mini | Leader |
|---|---|---|---|
| AIME | Not measured | 11.5% | Only GPT-4o-mini measured |
| MATH 500 (Vals AI) | Not measured | 72.6% | Only GPT-4o-mini measured |
| MGSM (Vals AI) | Not measured | 86.2% | Only GPT-4o-mini measured |
| AA Math Index | Not measured | 14.7 | Only GPT-4o-mini measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 14.7% | Only GPT-4o-mini measured |
| GSM8K | 57.8% | 91.3% | GPT-4o-mini |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | GPT-3.5 Turbo | GPT-4o-mini | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 62.7% | Only GPT-4o-mini measured |
| LegalBench (Vals AI) | 64.4% | Not measured | Only GPT-3.5 Turbo measured |
| CorpFin (Vals AI) | Not measured | 45.5% | Only GPT-4o-mini measured |
| TaxEval (Vals AI) | Not measured | 60.5% | Only GPT-4o-mini measured |
| MortgageTax (Vals AI) | Not measured | 54.5% | Only GPT-4o-mini measured |
| MedQA (Vals AI) | 58.5% | 72.4% | GPT-4o-mini |
| MMLU Pro (Artificial Analysis run) | 46.2% | 64.8% | GPT-4o-mini |
| MMLU | 68.5% | 81.8% | GPT-4o-mini |
| Neither model has a result on the other 10 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-3.5 Turbo | GPT-4o-mini | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-3.5 Turbo | GPT-4o-mini | 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-4o-mini costs less
Repository review
50K input + 3K output tokens
GPT-4o-mini costs less
Agent loop
200K input + 10K output tokens
GPT-4o-mini 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-mini |
|---|---|---|
| Maker | OpenAI | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | May 28, 2023 | July 18, 2024 |
| Context window | 16K | 128K |
| Input $/Mtok | $0.50 | $0.15 |
| Output $/Mtok | $1.50 | $0.60 |
| 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-mini 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 Coding Index or SciCode, where it leads.
Which is cheaper, GPT-3.5 Turbo or GPT-4o-mini?
GPT-3.5 Turbo costs $0.50 per million input tokens and $1.50 per million output tokens; GPT-4o-mini costs $0.15 and $0.60. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-3.5 Turbo or GPT-4o-mini?
GPT-4o-mini, with 128K tokens against 16K.
How many benchmarks is the GPT-3.5 Turbo vs GPT-4o-mini comparison based on?
11 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-mini's on 0. Where only one model has a result, the page says so instead of naming a winner.