GPT-3.5 Turbo (older v0613) vs GPT-5.1-Codex-Max
GPT-3.5 Turbo (older v0613) costs 5.0x less per output token than GPT-5.1-Codex-Max ($2.00 vs $10.00 per million). Updated October 22, 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
- $10.00
GPT-3.5 Turbo (older v0613) 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 (older v0613) for output tokens 5.0x cheaper ($2.00 vs $10.00 per million).
No benchmark has tested both yet. GPT-3.5 Turbo (older v0613) has results on GSM8K, MMLU and WinoGrande; GPT-5.1-Codex-Max on LiveCodeBench (Vals AI), Vibe Code Bench (Vals AI) and Terminal-Bench. 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
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 (older v0613)
GPT-3.5 Turbo (older v0613) $0.0020, GPT-5.1-Codex-Max $0.0063 per turn.
List prices
At a glance
- Coding index
- GPT-3.5 Turbo (older v0613) not benchmarked, GPT-5.1-Codex-Max 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
- GPT-3.5 Turbo (older v0613) $2.00 against GPT-5.1-Codex-Max $10.00 per million output tokens, 5.0x apart.
- Context window
- GPT-3.5 Turbo (older v0613) takes 4K tokens, GPT-5.1-Codex-Max 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 Index4 boards measured
| Benchmark | GPT-3.5 Turbo (older v0613) | 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 39 boards here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-3.5 Turbo (older v0613) | GPT-5.1-Codex-Max | Leader |
|---|---|---|---|
| METR time horizons | Not measured | 70.8% | Only GPT-5.1-Codex-Max measured |
| Neither model has a result on the other 41 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GPT-3.5 Turbo (older v0613) | GPT-5.1-Codex-Max | Leader |
|---|---|---|---|
| ProofBench | Not measured | 9.0% | Only GPT-5.1-Codex-Max measured |
| GSM8K | 46.9% | Not measured | Only GPT-3.5 Turbo (older v0613) measured |
| Neither model has a result on the other 12 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-3.5 Turbo (older v0613) | GPT-5.1-Codex-Max | Leader |
|---|---|---|---|
| MMLU | 68.9% | Not measured | Only GPT-3.5 Turbo (older v0613) measured |
| Neither model has a result on the other 19 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-3.5 Turbo (older v0613) | GPT-5.1-Codex-Max | Leader |
|---|---|---|---|
| WinoGrande | 81.6% | Not measured | Only GPT-3.5 Turbo (older v0613) measured |
| Neither model has a result on the other 47 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 (older v0613) costs less
Repository review
50K input + 3K output tokens
GPT-3.5 Turbo (older v0613) costs less
Agent loop
200K input + 10K output tokens
GPT-3.5 Turbo (older v0613) 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 (older v0613) | OpenAI GPT-5.1-Codex-Max |
|---|---|---|
| Maker | OpenAI | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | January 25, 2024 | December 4, 2025 |
| Context window | 4K | 400K |
| Input $/Mtok | $1.00 | $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 (older v0613) 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 (older v0613) for output tokens 5.0x cheaper ($2.00 vs $10.00 per million).
Which is cheaper, GPT-3.5 Turbo (older v0613) or GPT-5.1-Codex-Max?
GPT-3.5 Turbo (older v0613) costs $1.00 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 (older v0613) or GPT-5.1-Codex-Max?
GPT-5.1-Codex-Max, with 400K tokens against 4K.
Has any benchmark tested both GPT-3.5 Turbo (older v0613) and GPT-5.1-Codex-Max?
No benchmark has tested both yet. GPT-3.5 Turbo (older v0613) has results on GSM8K, MMLU and WinoGrande; GPT-5.1-Codex-Max on LiveCodeBench (Vals AI), Vibe Code Bench (Vals AI) and Terminal-Bench. Compare them on price and context instead. Data as of October 22, 2026.