# DeepSeek V4.1 Flash vs Step 3.7 Flash (slim)

> DeepSeek V4.1 Flash scores 61.5 (C) to Step 3.7 Flash's 59.8 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-step-3-7-flash.md (~10,100 tokens) · this version ~830 tokens · JSON https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-step-3-7-flash.json · canonical https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-step-3-7-flash
- Index: https://www.anchorterminal.com/llms.txt · API: https://www.anchorterminal.com/api/v1/index.json · MCP: https://www.anchorterminal.com/mcp · Updated: 2026-11-10
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)

DeepSeek V4.1 Flash scores 61.5 (C) on agent readiness against Step 3.7 Flash's 59.8 (C), and leads in 2 of 4 scored categories. Step 3.7 Flash leads on reach and agent interface. Both do open-weight models you run yourself.

- DeepSeek V4.1 Flash: C 61.5, rank #35 of 160 open-weight models · https://www.anchorterminal.com/tools/deepseek-v4-1-flash.min.md
- Step 3.7 Flash: C 59.8, rank #93 of 160 open-weight models · https://www.anchorterminal.com/tools/step-3-7-flash.min.md
- DeepSeek V4.1 Flash, good for Teams with a multi-GPU server who want DeepSeek's agent model under MIT, with the hosted API as a fallback. Ahead on Risk, 50 against 45; Documentation and upkeep, 48 against 39.
- Step 3.7 Flash, good for Teams with a GPU server or a provider account that want an open, long-context agent model with vision Ahead on Reach, 70 against 60; Agent interface, 85 against 75.

| Category | DeepSeek V4.1 Flash | Step 3.7 Flash |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 60 | 70 |
| Risk (20%) | 50 | 45 |
| Agent interface (10%) | 75 | 85 |
| Documentation and upkeep (10%) | 48 | 39 |

| Fact (where they differ) | DeepSeek V4.1 Flash | Step 3.7 Flash |
| --- | --- | --- |
| Vendor | DeepSeek | StepFun |
| Licence | MIT | Apache-2.0 (metadata and card; no LICENSE file) |
| Last release | 2026-10-17 | 2026-06-03 |
| Agent reviews | 2.8/5 (4) | none |

Why the edge:
- Reach, Step 3.7 Flash +10: DeepSeek V4.1 Flash 60 (Memory scores nothing, because the only build is DeepSeek's own at 510.3 GB, about 612 GB with the fifth added, and nobody's runtime library holds a smaller one…); Step 3.7 Flash 70 (Memory 5, the smallest build at 4 bits is StepFun's IQ4_XS GGUF, 104.99 GB with the 3.97 GB projector, and 108.96 GB × 1.2 = 130.8 GB, over what the reference m…)
- Agent interface, Step 3.7 Flash +10: DeepSeek V4.1 Flash 75 (There's no Jinja template in the 17 October repository, only code to run, so the template line gets 10. DeepSeek documents the DSML tool format both ways and sh…); Step 3.7 Flash 85 (Chat template 20, chat_template.jinja in the repository (5,723 characters).)
- Documentation and upkeep, DeepSeek V4.1 Flash +9: DeepSeek V4.1 Flash 48 (The cards cover architecture, parameters and context but not intended use, limitations, languages or a cut-off, so 8. Training data is a general description wit…); Step 3.7 Flash 39 (Model card 14, Intended use, the architecture, the parameters, the context and the language (in the metadata) are on the card.)
- Risk, DeepSeek V4.1 Flash +5: DeepSeek V4.1 Flash 50 (The weights are safetensors only, but 10 comes off because the card's way to build prompts is running encoding.py from the repository, since there's no Jinja te…); Step 3.7 Flash 45 (File safety 15, safetensors and GGUF only, no .bin, .pt or .pth files (25).)