GPT-3.5 Turbo Instruct vs GPT-5.1-Codex-Mini
GPT-3.5 Turbo Instruct and GPT-5.1-Codex-Mini cost about the same per output token ($2.00 vs $2.00 per million). Updated October 21, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $2.00
- Coding
- No data
- Overall
- No data
- Output $/M
- $2.00
GPT-3.5 Turbo Instruct or GPT-5.1-Codex-Mini: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
No benchmark has tested both yet. GPT-3.5 Turbo Instruct has results on METR time horizons; GPT-5.1-Codex-Mini on LMArena WebDev, LiveCodeBench (Artificial Analysis run) and Terminal-Bench Hard (Artificial Analysis run). Compare them on price and context instead.
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-5.1-Codex-Mini has a AA Intelligence Index result, 20.4.
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 4K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-5.1-Codex-Mini
GPT-3.5 Turbo Instruct $0.0025, GPT-5.1-Codex-Mini $0.0013 per turn.
List prices
At a glance
- Coding index
- GPT-3.5 Turbo Instruct not benchmarked, GPT-5.1-Codex-Mini not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- Both cost $2.00 per million output tokens.
- Context window
- GPT-3.5 Turbo Instruct takes 4K tokens, GPT-5.1-Codex-Mini 400K.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index5 boards measured
| Benchmark | GPT-3.5 Turbo Instruct | GPT-5.1-Codex-Mini | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1244 | Only GPT-5.1-Codex-Mini 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 |
| Neither model has a result on the other 34 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-3.5 Turbo Instruct | GPT-5.1-Codex-Mini | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 20.4 | Only GPT-5.1-Codex-Mini measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GPT-3.5 Turbo Instruct | GPT-5.1-Codex-Mini | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 62.9% | Only GPT-5.1-Codex-Mini measured |
| METR time horizons | 21.5% | Not measured | Only GPT-3.5 Turbo Instruct measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GPT-3.5 Turbo Instruct | GPT-5.1-Codex-Mini | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 81.3% | Only GPT-5.1-Codex-Mini measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 18.5% | Only GPT-5.1-Codex-Mini measured |
| Neither model has a result on the other 20 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GPT-3.5 Turbo Instruct | 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 |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-3.5 Turbo Instruct | GPT-5.1-Codex-Mini | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 82.0% | Only GPT-5.1-Codex-Mini measured |
| Neither model has a result on the other 17 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards measured
| Benchmark | GPT-3.5 Turbo Instruct | 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 | 1081 | 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 | 1094 | 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 index2 boards measured
| Benchmark | GPT-3.5 Turbo Instruct | 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 |
| Neither model has a result on the other 44 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-5.1-Codex-Mini costs less
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 Instruct | OpenAI GPT-5.1-Codex-Mini |
|---|---|---|
| Maker | OpenAI | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | September 28, 2023 | November 13, 2025 |
| Context window | 4K | 400K |
| Input $/Mtok | $1.50 | $0.25 |
| Output $/Mtok | $2.00 | $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 Instruct better than GPT-5.1-Codex-Mini for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, GPT-3.5 Turbo Instruct or GPT-5.1-Codex-Mini?
GPT-3.5 Turbo Instruct costs $1.50 per million input tokens and $2.00 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 Instruct or GPT-5.1-Codex-Mini?
GPT-5.1-Codex-Mini, with 400K tokens against 4K.
Has any benchmark tested both GPT-3.5 Turbo Instruct and GPT-5.1-Codex-Mini?
No benchmark has tested both yet. GPT-3.5 Turbo Instruct has results on METR time horizons; GPT-5.1-Codex-Mini on LMArena WebDev, LiveCodeBench (Artificial Analysis run) and Terminal-Bench Hard (Artificial Analysis run). Compare them on price and context instead. Data as of October 21, 2026.