GPT-3.5 Turbo Instruct vs Relace Apply 3
Relace Apply 3 costs 1.6x less per output token than GPT-3.5 Turbo Instruct ($1.25 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
- $1.25
GPT-3.5 Turbo Instruct or Relace Apply 3: 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; Relace Apply 3 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
Relace Apply 3
Relace Apply 3 takes 256K tokens against 4K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Relace Apply 3
GPT-3.5 Turbo Instruct $0.0025, Relace Apply 3 $0.0015 per turn.
List prices
At a glance
- Coding index
- GPT-3.5 Turbo Instruct not benchmarked, Relace Apply 3 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 Instruct $2.00 against Relace Apply 3 $1.25 per million output tokens, 1.6x apart.
- Context window
- GPT-3.5 Turbo Instruct takes 4K tokens, Relace Apply 3 256K.
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.
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-3.5 Turbo Instruct | Relace Apply 3 | Leader |
|---|---|---|---|
| METR time horizons | 21.5% | Not measured | Only GPT-3.5 Turbo Instruct measured |
| Neither model has a result on the other 40 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
Relace Apply 3 costs less
Repository review
50K input + 3K output tokens
Relace Apply 3 costs less
Agent loop
200K input + 10K output tokens
Relace Apply 3 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 | Relace Apply 3 |
|---|---|---|
| Maker | OpenAI | Relace |
| Weights | No public weights listed | No public weights listed |
| Released | September 28, 2023 | September 26, 2025 |
| Context window | 4K | 256K |
| Input $/Mtok | $1.50 | $0.85 |
| Output $/Mtok | $2.00 | $1.25 |
| 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 Instruct better than Relace Apply 3 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 Relace Apply 3?
GPT-3.5 Turbo Instruct costs $1.50 per million input tokens and $2.00 per million output tokens; Relace Apply 3 costs $0.85 and $1.25. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-3.5 Turbo Instruct or Relace Apply 3?
Relace Apply 3, with 256K tokens against 4K.
Has any benchmark tested both GPT-3.5 Turbo Instruct and Relace Apply 3?
No benchmark has tested both yet. GPT-3.5 Turbo Instruct has results on METR time horizons; Relace Apply 3 has none yet. Compare them on price and context instead. Data as of October 22, 2026.