GPT-3.5 Turbo vs GPT-5.1-Codex-Mini
GPT-5.1-Codex-Mini leads GPT-3.5 Turbo on the Artificial Analysis Intelligence Index, 20.4 to 5.5, and GPT-3.5 Turbo costs 1.3x less per output token ($1.50 vs $2.00 per million). Updated October 22, 2026; 3 benchmarks have a result for both.
GPT-3.5 Turbo or GPT-5.1-Codex-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 output tokens 1.3x cheaper ($1.50 vs $2.00 per million) and work measured by AA Intelligence Index or GPQA Diamond (Artificial Analysis run), where it leads.
- Shared results
- 3
- GPT-3.5 Turbo only
- 10
- GPT-5.1-Codex-Mini only
- 19
- 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-5.1-Codex-Mini
GPT-5.1-Codex-Mini leads, 20.4 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
Web design
Design Arena: websites
No call
Only GPT-5.1-Codex-Mini has a Design Arena: websites result, 1111.
Directional only
Long documents
Listed context window
GPT-5.1-Codex-Mini
GPT-5.1-Codex-Mini takes 400K tokens against 16K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
No call
GPT-3.5 Turbo $0.0013, GPT-5.1-Codex-Mini $0.0013 per turn.
List prices
At a glance
- Coding index
- GPT-3.5 Turbo not benchmarked, GPT-5.1-Codex-Mini not benchmarked.
- Widest category
- Reasoning, 51.6 points apart on average over 1 shared benchmark.
- Widest single gap
- GPQA Diamond (Artificial Analysis run): GPT-3.5 Turbo 29.7% against GPT-5.1-Codex-Mini 81.3%, 51.6 points apart on a 0 to 100 scale.
- Output price
- GPT-3.5 Turbo $1.50 against GPT-5.1-Codex-Mini $2.00 per million output tokens, 1.3x apart.
- Context window
- GPT-3.5 Turbo takes 16K tokens, GPT-5.1-Codex-Mini 400K.
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-5.1-Codex-Mini | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 1 | ||
| Knowledge | 0 | 1 | ||
| All shared boards | 0 | 3 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index7 boards, none shared
| Benchmark | GPT-3.5 Turbo | GPT-5.1-Codex-Mini | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1244 | Only GPT-5.1-Codex-Mini measured |
| AA Coding Index | 10.7 | Not measured | Only GPT-3.5 Turbo measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 83.6% | Only GPT-5.1-Codex-Mini measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 33.3% | Only GPT-5.1-Codex-Mini measured |
| Terminal-Bench | Not measured | 61.6% | Only GPT-5.1-Codex-Mini measured |
| LMArena WebDev: HTML | Not measured | 1253 | Only GPT-5.1-Codex-Mini measured |
| SciCode | 0.0% | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 32 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-3.5 Turbo | GPT-5.1-Codex-Mini | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.5 | 20.4 | GPT-5.1-Codex-Mini |
| 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-5.1-Codex-Mini | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 62.9% | Only GPT-5.1-Codex-Mini measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-3.5 Turbo | GPT-5.1-Codex-Mini | Leader |
|---|---|---|---|
| GPQA Diamond | 30.6% | Not measured | Only GPT-3.5 Turbo measured |
| GPQA Diamond (Artificial Analysis run) | 29.7% | 81.3% | GPT-5.1-Codex-Mini |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 18.5% | Only GPT-5.1-Codex-Mini measured |
| BIG-Bench Hard | 61.6% | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 18 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-3.5 Turbo | GPT-5.1-Codex-Mini | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 91.7 | Only GPT-5.1-Codex-Mini measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 91.7% | Only GPT-5.1-Codex-Mini measured |
| GSM8K | 57.8% | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-3.5 Turbo | GPT-5.1-Codex-Mini | 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% | 82.0% | GPT-5.1-Codex-Mini |
| MMLU | 68.5% | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 14 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GPT-3.5 Turbo | GPT-5.1-Codex-Mini | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1111 | Only GPT-5.1-Codex-Mini measured |
| Design Arena: UI components | Not measured | 1078 | Only GPT-5.1-Codex-Mini measured |
| Design Arena: game dev | Not measured | 1092 | Only GPT-5.1-Codex-Mini measured |
| Design Arena: data viz | Not measured | 1097 | Only GPT-5.1-Codex-Mini measured |
| Design Arena: 3D | Not measured | 988 | Only GPT-5.1-Codex-Mini measured |
| Design Arena: all code categories | Not measured | 1093 | Only GPT-5.1-Codex-Mini measured |
| Design Arena: SVG | Not measured | 978 | Only GPT-5.1-Codex-Mini measured |
| Design Arena: ASCII art | Not measured | 1105 | Only GPT-5.1-Codex-Mini measured |
| Neither model has a result on the other 1 board here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-3.5 Turbo | GPT-5.1-Codex-Mini | Leader |
|---|---|---|---|
| IFBench | Not measured | 67.9% | Only GPT-5.1-Codex-Mini measured |
| Long context reasoning (AA-LCR) | Not measured | 66.7% | Only GPT-5.1-Codex-Mini 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 42 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-3.5 Turbo | GPT-5.1-Codex-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
Same cost
Repository review
50K input + 3K output tokens
GPT-5.1-Codex-Mini costs less
Agent loop
200K input + 10K output tokens
GPT-5.1-Codex-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-5.1-Codex-Mini |
|---|---|---|
| Maker | OpenAI | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | May 28, 2023 | November 13, 2025 |
| Context window | 16K | 400K |
| Input $/Mtok | $0.50 | $0.25 |
| Output $/Mtok | $1.50 | $2.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-3.5 Turbo better than GPT-5.1-Codex-Mini 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 work measured by AA Intelligence Index or GPQA Diamond (Artificial Analysis run), where it leads.
Which is cheaper, GPT-3.5 Turbo or GPT-5.1-Codex-Mini?
GPT-3.5 Turbo costs $0.50 per million input tokens and $1.50 per million output tokens; GPT-5.1-Codex-Mini costs $0.25 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-5.1-Codex-Mini?
GPT-5.1-Codex-Mini, with 400K tokens against 16K.
How many benchmarks is the GPT-3.5 Turbo vs GPT-5.1-Codex-Mini comparison based on?
3 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-5.1-Codex-Mini's on 1. Where only one model has a result, the page says so instead of naming a winner.