DeepSeek-V3.1 is a large hybrid reasoning model (671B parameters, 37B active) that supports both thinking and non-thinking modes via prompt templates. It extends the DeepSeek-V3 base with a two-phase long-context training process, reaching …
Model details →DeepSeek: DeepSeek V3.1 vs ReMM SLERP 13B
DeepSeek: DeepSeek V3.1 wins on 4 of 6 axes — pricing and capability skew in its favour for most workloads.
A recreation trial of the original MythoMax-L2-B13 but with updated models. #merge
Model details →Side-by-side comparison
| Capability | DeepSeek: DeepSeek V3.1 | ReMM SLERP 13B | Winner |
|---|---|---|---|
| Context window Maximum number of input tokens the model can attend to in a single request. | 33K | 6K | 🏆 DeepSeek: DeepSeek V3.1 |
| Input price (per 1M) Cost per million input tokens billed by the provider. | $0.15 | $0.45 | 🏆 DeepSeek: DeepSeek V3.1 |
| Output price (per 1M) Cost per million output tokens billed by the provider. | $0.75 | $0.65 | 🏆 ReMM SLERP 13B |
| Tool / function calling First-class support for emitting structured tool calls. | Yes | — | 🏆 DeepSeek: DeepSeek V3.1 |
| Vision input Accepts image inputs alongside text. | — | — | Tie |
| Reasoning mode Internal chain-of-thought / extended-thinking support. | Yes | — | 🏆 DeepSeek: DeepSeek V3.1 |
Frequently asked questions
Is DeepSeek: DeepSeek V3.1 better than ReMM SLERP 13B?
DeepSeek: DeepSeek V3.1 wins on 4 of 6 axes — pricing and capability skew in its favour for most workloads.
What's the price difference between DeepSeek: DeepSeek V3.1 and ReMM SLERP 13B?
Input: $0.15 vs $0.45 per 1M tokens. Output: $0.75 vs $0.65 per 1M tokens.
What context windows do DeepSeek: DeepSeek V3.1 and ReMM SLERP 13B support?
DeepSeek: DeepSeek V3.1 supports up to 33K tokens. ReMM SLERP 13B supports up to 6K tokens.
Do both DeepSeek: DeepSeek V3.1 and ReMM SLERP 13B support tool calling?
DeepSeek: DeepSeek V3.1: yes. ReMM SLERP 13B: not advertised.