GPT-3.5 Turbo vs ReMM SLERP 13B
ReMM SLERP 13B costs 2.3x less per output token than GPT-3.5 Turbo ($0.65 vs $1.50 per million). Updated October 23, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $1.50
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.65
GPT-3.5 Turbo or ReMM SLERP 13B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-3.5 Turbo for a larger context window (16K against 6K tokens).
No benchmark has tested both yet. GPT-3.5 Turbo has results on AA Coding Index, SciCode and AA Intelligence Index; ReMM SLERP 13B 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
Intelligence
AA Intelligence Index
No call
Only GPT-3.5 Turbo has a AA Intelligence Index result, 5.5.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-3.5 Turbo has a GPQA Diamond result, 30.6%.
Directional only
Long documents
Listed context window
GPT-3.5 Turbo
GPT-3.5 Turbo takes 16K tokens against 6K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
ReMM SLERP 13B
GPT-3.5 Turbo $0.0013, ReMM SLERP 13B $0.0007 per turn.
List prices
At a glance
- Coding index
- GPT-3.5 Turbo not benchmarked, ReMM SLERP 13B 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 $1.50 against ReMM SLERP 13B $0.65 per million output tokens, 2.3x apart.
- Context window
- GPT-3.5 Turbo takes 16K tokens, ReMM SLERP 13B 6K.
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 Index2 boards measured
| Benchmark | GPT-3.5 Turbo | ReMM SLERP 13B | Leader |
|---|---|---|---|
| AA Coding Index | 10.7 | Not measured | Only GPT-3.5 Turbo measured |
| SciCode | 0.0% | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 41 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-3.5 Turbo | ReMM SLERP 13B | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.5 | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 1 board here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | GPT-3.5 Turbo | ReMM SLERP 13B | Leader |
|---|---|---|---|
| GPQA Diamond | 30.6% | Not measured | Only GPT-3.5 Turbo measured |
| GPQA Diamond (Artificial Analysis run) | 29.7% | Not measured | Only GPT-3.5 Turbo measured |
| BIG-Bench Hard | 61.6% | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 20 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-3.5 Turbo | ReMM SLERP 13B | Leader |
|---|---|---|---|
| GSM8K | 57.8% | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 13 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | GPT-3.5 Turbo | ReMM SLERP 13B | Leader |
|---|---|---|---|
| LegalBench (Vals AI) | 64.4% | Not measured | Only GPT-3.5 Turbo measured |
| MedQA (Vals AI) | 58.5% | Not measured | Only GPT-3.5 Turbo measured |
| MMLU Pro (Artificial Analysis run) | 46.2% | Not measured | Only GPT-3.5 Turbo measured |
| MMLU | 68.5% | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 16 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
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
ReMM SLERP 13B costs less
Repository review
50K input + 3K output tokens
ReMM SLERP 13B costs less
Agent loop
200K input + 10K output tokens
ReMM SLERP 13B 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 | ReMM SLERP 13B |
|---|---|---|
| Maker | OpenAI | Undi95 |
| Weights | No public weights listed | Open weights |
| Released | May 28, 2023 | July 22, 2023 |
| Context window | 16K | 6K |
| Input $/Mtok | $0.50 | $0.35 |
| Output $/Mtok | $1.50 | $0.65 |
| 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 better than ReMM SLERP 13B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-3.5 Turbo for a larger context window (16K against 6K tokens).
Which is cheaper, GPT-3.5 Turbo or ReMM SLERP 13B?
GPT-3.5 Turbo costs $0.50 per million input tokens and $1.50 per million output tokens; ReMM SLERP 13B costs $0.35 and $0.65. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-3.5 Turbo or ReMM SLERP 13B?
GPT-3.5 Turbo, with 16K tokens against 6K.
Has any benchmark tested both GPT-3.5 Turbo and ReMM SLERP 13B?
No benchmark has tested both yet. GPT-3.5 Turbo has results on AA Coding Index, SciCode and AA Intelligence Index; ReMM SLERP 13B has none yet. Compare them on price and context instead. Data as of October 23, 2026.