GPT-3.5 Turbo (older v0613) vs Schematron V2 Small
Schematron V2 Small costs 8.7x less per output token than GPT-3.5 Turbo (older v0613) ($0.23 vs $2.00 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
- $0.23
GPT-3.5 Turbo (older v0613) or Schematron V2 Small: 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 (older v0613) has results on GSM8K, MMLU and WinoGrande; Schematron V2 Small has none yet. 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
Schematron V2 Small
Schematron V2 Small takes 128K tokens against 4K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Schematron V2 Small
GPT-3.5 Turbo (older v0613) $0.0020, Schematron V2 Small $0.0002 per turn.
List prices
At a glance
- Coding index
- GPT-3.5 Turbo (older v0613) not benchmarked, Schematron V2 Small 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 Schematron V2 Small $0.23 per million output tokens, 8.7x apart.
- Context window
- GPT-3.5 Turbo (older v0613) takes 4K tokens, Schematron V2 Small 128K.
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.
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-3.5 Turbo (older v0613) | Schematron V2 Small | Leader |
|---|---|---|---|
| GSM8K | 46.9% | Not measured | Only GPT-3.5 Turbo (older v0613) measured |
| Neither model has a result on the other 13 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-3.5 Turbo (older v0613) | Schematron V2 Small | 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) | Schematron V2 Small | 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
Schematron V2 Small costs less
Repository review
50K input + 3K output tokens
Schematron V2 Small costs less
Agent loop
200K input + 10K output tokens
Schematron V2 Small 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) | Inference.net Schematron V2 Small |
|---|---|---|
| Maker | OpenAI | Inference.net |
| Weights | No public weights listed | Open weights |
| Released | January 25, 2024 | September 12, 2026 |
| Context window | 4K | 128K |
| Input $/Mtok | $1.00 | $0.05 |
| Output $/Mtok | $2.00 | $0.23 |
| 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 (older v0613) better than Schematron V2 Small 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 (older v0613) or Schematron V2 Small?
GPT-3.5 Turbo (older v0613) costs $1.00 per million input tokens and $2.00 per million output tokens; Schematron V2 Small costs $0.05 and $0.23. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-3.5 Turbo (older v0613) or Schematron V2 Small?
Schematron V2 Small, with 128K tokens against 4K.
Has any benchmark tested both GPT-3.5 Turbo (older v0613) and Schematron V2 Small?
No benchmark has tested both yet. GPT-3.5 Turbo (older v0613) has results on GSM8K, MMLU and WinoGrande; Schematron V2 Small has none yet. Compare them on price and context instead. Data as of October 22, 2026.