GPT-3.5 Turbo Instruct vs GPT-5.1-Codex-Max
GPT-3.5 Turbo Instruct costs 5.0x less per output token than GPT-5.1-Codex-Max ($2.00 vs $10.00 per million). Updated October 22, 2026; 1 benchmarks have a result for both.
GPT-3.5 Turbo Instruct or GPT-5.1-Codex-Max: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-3.5 Turbo Instruct for output tokens 5.0x cheaper ($2.00 vs $10.00 per million) and work measured by METR time horizons, where it leads.
- Shared results
- 1
- GPT-3.5 Turbo Instruct only
- 0
- GPT-5.1-Codex-Max only
- 5
- Like-for-like categories
- 1 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
Long documents
Listed context window
GPT-5.1-Codex-Max
GPT-5.1-Codex-Max takes 400K tokens against 4K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-3.5 Turbo Instruct
GPT-3.5 Turbo Instruct $0.0025, GPT-5.1-Codex-Max $0.0063 per turn.
List prices
At a glance
- Coding index
- GPT-3.5 Turbo Instruct not benchmarked, GPT-5.1-Codex-Max not benchmarked.
- Widest category
- Agentic work, 49.4 points apart on average over 1 shared benchmark.
- Widest single gap
- METR time horizons: GPT-3.5 Turbo Instruct 21.5% against GPT-5.1-Codex-Max 70.8%, 49.4 points apart on a 0 to 100 scale.
- Output price
- GPT-3.5 Turbo Instruct $2.00 against GPT-5.1-Codex-Max $10.00 per million output tokens, 5.0x apart.
- Context window
- GPT-3.5 Turbo Instruct takes 4K tokens, GPT-5.1-Codex-Max 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 Instruct | Bars | GPT-5.1-Codex-Max | Ties |
|---|---|---|---|---|
| Agentic work | 0 | 1 | ||
| All shared boards | 0 | 1 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index4 boards, none shared
| Benchmark | GPT-3.5 Turbo Instruct | GPT-5.1-Codex-Max | Leader |
|---|---|---|---|
| LiveCodeBench (Vals AI) | Not measured | 83.6% | Only GPT-5.1-Codex-Max measured |
| Vibe Code Bench (Vals AI) | Not measured | 22.2% | Only GPT-5.1-Codex-Max measured |
| Terminal-Bench | Not measured | 60.4% | Only GPT-5.1-Codex-Max measured |
| ALE-Bench | Not measured | 1228.3 | Only GPT-5.1-Codex-Max measured |
| Neither model has a result on the other 35 boards here | |||
IntelligenceNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-3.5 Turbo Instruct | GPT-5.1-Codex-Max | Leader |
|---|---|---|---|
| Neither model has a result on 2 boards here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-3.5 Turbo Instruct | GPT-5.1-Codex-Max | Leader |
|---|---|---|---|
| METR time horizons | 21.5% | 70.8% | GPT-5.1-Codex-Max |
| Neither model has a result on the other 40 boards here | |||
ReasoningNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-3.5 Turbo Instruct | GPT-5.1-Codex-Max | Leader |
|---|---|---|---|
| Neither model has a result on 22 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-3.5 Turbo Instruct | GPT-5.1-Codex-Max | Leader |
|---|---|---|---|
| ProofBench | Not measured | 9.0% | Only GPT-5.1-Codex-Max measured |
| Neither model has a result on the other 12 boards here | |||
KnowledgeNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-3.5 Turbo Instruct | GPT-5.1-Codex-Max | Leader |
|---|---|---|---|
| Neither model has a result on 18 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-3.5 Turbo Instruct | GPT-5.1-Codex-Max | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-3.5 Turbo Instruct | GPT-5.1-Codex-Max | Leader |
|---|---|---|---|
| Neither model has a result on 46 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-3.5 Turbo Instruct | GPT-5.1-Codex-Max | 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 Instruct costs less
Repository review
50K input + 3K output tokens
GPT-3.5 Turbo Instruct costs less
Agent loop
200K input + 10K output tokens
GPT-3.5 Turbo Instruct 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-Max |
|---|---|---|
| Maker | OpenAI | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | September 28, 2023 | December 4, 2025 |
| Context window | 4K | 400K |
| Input $/Mtok | $1.50 | $1.25 |
| Output $/Mtok | $2.00 | $10.00 |
| 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-Max for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-3.5 Turbo Instruct for output tokens 5.0x cheaper ($2.00 vs $10.00 per million) and work measured by METR time horizons, where it leads.
Which is cheaper, GPT-3.5 Turbo Instruct or GPT-5.1-Codex-Max?
GPT-3.5 Turbo Instruct costs $1.50 per million input tokens and $2.00 per million output tokens; GPT-5.1-Codex-Max costs $1.25 and $10.00. 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-Max?
GPT-5.1-Codex-Max, with 400K tokens against 4K.
How many benchmarks is the GPT-3.5 Turbo Instruct vs GPT-5.1-Codex-Max comparison based on?
1 benchmarks have a result for both models as of October 22, 2026. GPT-3.5 Turbo Instruct's coding index rests on 0 independent benchmarks and GPT-5.1-Codex-Max's on 1. Where only one model has a result, the page says so instead of naming a winner.