GPT-4o (2024-05-13) vs ReMM SLERP 13B
ReMM SLERP 13B costs 23.1x less per output token than GPT-4o (2024-05-13) ($0.65 vs $15.00 per million). Updated October 23, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- 27.1
- Output $/M
- $15.00
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.65
GPT-4o (2024-05-13) or ReMM SLERP 13B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-4o (2024-05-13) for a larger context window (128K against 6K tokens).
No benchmark has tested both yet. GPT-4o (2024-05-13) has results on AA Coding Index, LiveCodeBench (Artificial Analysis run) and LMArena Text: coding; 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-4o (2024-05-13) has a AA Intelligence Index result, 7.3.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-4o (2024-05-13) has a GPQA Diamond result, 50.2%.
Directional only
Long documents
Listed context window
GPT-4o (2024-05-13)
GPT-4o (2024-05-13) takes 128K tokens against 6K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
ReMM SLERP 13B
GPT-4o (2024-05-13) $0.013, ReMM SLERP 13B $0.0007 per turn.
List prices
At a glance
- Coding index
- GPT-4o (2024-05-13) 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-4o (2024-05-13) $15.00 against ReMM SLERP 13B $0.65 per million output tokens, 23.1x apart.
- Context window
- GPT-4o (2024-05-13) takes 128K 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 Index4 boards measured
| Benchmark | GPT-4o (2024-05-13) | ReMM SLERP 13B | Leader |
|---|---|---|---|
| AA Coding Index | 24.2 | Not measured | Only GPT-4o (2024-05-13) measured |
| LiveCodeBench (Artificial Analysis run) | 33.4% | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: coding | 1297 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: software and IT | 1297 | Not measured | Only GPT-4o (2024-05-13) measured |
| Neither model has a result on the other 39 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-4o (2024-05-13) | ReMM SLERP 13B | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.3 | Not measured | Only GPT-4o (2024-05-13) measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GPT-4o (2024-05-13) | ReMM SLERP 13B | Leader |
|---|---|---|---|
| METR time horizons | 40.8% | Not measured | Only GPT-4o (2024-05-13) measured |
| BALROG | 32.3% | Not measured | Only GPT-4o (2024-05-13) measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index7 boards measured
| Benchmark | GPT-4o (2024-05-13) | ReMM SLERP 13B | Leader |
|---|---|---|---|
| GPQA Diamond | 50.2% | Not measured | Only GPT-4o (2024-05-13) measured |
| GPQA Diamond (Artificial Analysis run) | 52.6% | Not measured | Only GPT-4o (2024-05-13) measured |
| Humanity's Last Exam (Artificial Analysis run) | 1.8% | Not measured | Only GPT-4o (2024-05-13) measured |
| GPQA Diamond (Epoch AI run) | 48.9% | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: hard prompts | 1281 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: expert | 1250 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: hard prompts in English | 1293 | Not measured | Only GPT-4o (2024-05-13) measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | GPT-4o (2024-05-13) | ReMM SLERP 13B | Leader |
|---|---|---|---|
| OTIS Mock AIME | 6.3% | Not measured | Only GPT-4o (2024-05-13) measured |
| MATH level 5 | 51.0% | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: math | 1284 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: mathematical work | 1284 | Not measured | Only GPT-4o (2024-05-13) measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | GPT-4o (2024-05-13) | ReMM SLERP 13B | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 74.0% | Not measured | Only GPT-4o (2024-05-13) measured |
| MMLU | 84.2% | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: science | 1295 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: medicine and healthcare | 1278 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: legal and government | 1314 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: business and finance | 1261 | Not measured | Only GPT-4o (2024-05-13) measured |
| Neither model has a result on the other 14 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index20 boards measured
| Benchmark | GPT-4o (2024-05-13) | ReMM SLERP 13B | Leader |
|---|---|---|---|
| LMArena Text | 1300 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: instruction following | 1278 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: creative writing | 1291 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Vision | 1137 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: English | 1312 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: non-English | 1283 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: Chinese | 1277 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: German | 1282 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: Japanese | 1257 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: Korean | 1234 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: French | 1304 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: Spanish | 1292 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: Russian | 1285 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: Polish | 1365 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: multi-turn | 1302 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: longer queries | 1288 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: writing and language | 1298 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: entertainment and media | 1289 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Vision: English | 1147 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Vision: Chinese | 1132 | Not measured | Only GPT-4o (2024-05-13) measured |
| Neither model has a result on the other 28 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
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-4o (2024-05-13) | ReMM SLERP 13B |
|---|---|---|
| Maker | OpenAI | Undi95 |
| Weights | No public weights listed | Open weights |
| Released | May 13, 2024 | July 22, 2023 |
| Context window | 128K | 6K |
| Input $/Mtok | $5.00 | $0.35 |
| Output $/Mtok | $15.00 | $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-4o (2024-05-13) better than ReMM SLERP 13B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-4o (2024-05-13) for a larger context window (128K against 6K tokens).
Which is cheaper, GPT-4o (2024-05-13) or ReMM SLERP 13B?
GPT-4o (2024-05-13) costs $5.00 per million input tokens and $15.00 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-4o (2024-05-13) or ReMM SLERP 13B?
GPT-4o (2024-05-13), with 128K tokens against 6K.
Has any benchmark tested both GPT-4o (2024-05-13) and ReMM SLERP 13B?
No benchmark has tested both yet. GPT-4o (2024-05-13) has results on AA Coding Index, LiveCodeBench (Artificial Analysis run) and LMArena Text: coding; ReMM SLERP 13B has none yet. Compare them on price and context instead. Data as of October 23, 2026.