undi95 · Open weights
ReMM SLERP 13B
A recreation trial of the original MythoMax-L2-B13 but merged with updated models.
- PDF input
- Input
- Text
- Output
- Text
- Discovery tier
- Open weights
Pricing
USD per 1M tokens- Input
- $0.80
- Output
- $1.21
Reference prices as published by the model provider. API example
Specifications
- Context window
- 6.1K
- Max output
- 4.1K
- Average output (7 days)
- 62 tokens
- Longer than 5% of 468 models
Side by side
ReMM SLERP 13B compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| ReMM SLERP 13Bundi95This model | LongCat 2.0 ThinkinglongcatRemove | |
|---|---|---|
| Provider | undi95 | longcat |
| Discovery tier | Open weights | Open weights |
| Added | Jan 1, 2024 | Jul 21, 2026 |
| Limits | ||
| Context window | 6.14K | 1MBest |
| Max output | 4.1K | 131.07KBest |
| Average output | 62 tokens | 1.1K tokens |
| Pricing, USD per 1M tokens | ||
| Input | $0.80 | $0.75Best |
| Output | $1.21Best | $3.00 |
| Cache read | — | $0.015 |
| Performance | ||
| Throughput | — | 66.3 tok/s |
| Time to first token | — | 5.2 s |
| Request success (24h) | — | 100.0% |
| Benchmark indexes | ||
| Intelligence Index | — | 19.1 |
| Coding Index | — | 45.3 |
| Capabilities | ||
| Reasoning | No | Yes |
| Tools | No | Yes |
| PDF input | Yes | No |
| Reasoning effort | — | — |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- ReMM SLERP 13B
- LongCat 2.0 Thinking
Intelligence Index
- ReMM SLERP 13B—
- LongCat 2.0 Thinking19.1
Indexes
Coding Index
Composite of the coding evaluations
- ReMM SLERP 13B: —
- LongCat 2.0 Thinking: 45.3
Agentic work
AutomationBench-AA
Workflow automation, penalized for guardrail violations
- ReMM SLERP 13B: —
- LongCat 2.0 Thinking: 4.5%
AutomationBench-AA tasks completed
Workflows finished end to end without a guardrail violation
- ReMM SLERP 13B: —
- LongCat 2.0 Thinking: 0.5%
AA-Briefcasev1.1
Agentic knowledge work, rated head to head
- ReMM SLERP 13B: —
- LongCat 2.0 Thinking: 739 Elo
GDPval-AAv2.1
Economically valuable occupational tasks, rated head to head
- ReMM SLERP 13B: —
- LongCat 2.0 Thinking: 869 Elo
Document reasoning
GDP.pdf
Professional reasoning over PDF documents, all-pass rate
- ReMM SLERP 13B: —
- LongCat 2.0 Thinking: 6.4%
AA-LCRv1.1
Reasoning across long documents
- ReMM SLERP 13B: —
- LongCat 2.0 Thinking: 65.0%
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- ReMM SLERP 13B: —
- LongCat 2.0 Thinking: 33.7%
GPQA Diamond
Graduate-level science questions
- ReMM SLERP 13B: —
- LongCat 2.0 Thinking: 78.0%
Coding
Terminal-Benchv4.0
Practical software engineering in a terminal
- ReMM SLERP 13B: —
- LongCat 2.0 Thinking: 0.0%
SciCode
Scientific computing in Python
- ReMM SLERP 13B: —
- LongCat 2.0 Thinking: 36.3%
Legacy evaluations
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- ReMM SLERP 13B: —
- LongCat 2.0 Thinking: 65.0%
Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis
Developers
Use it through the API
Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.
curl https://www.noumen.xyz/api/v1/chat/completions \
-H "Authorization: Bearer $NOUMEN_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "undi95/remm-slerp-l2-13b",
"messages": [{ "role": "user", "content": "Explain this stack trace." }]
}'import os
from openai import OpenAI
client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
model="undi95/remm-slerp-l2-13b",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)