# DeepSeek V4.1 Flash vs Laguna XS 2.1

> Laguna XS 2.1 scores 61 (C) to DeepSeek V4.1 Flash's 59 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-xs-2-1
- Markdown: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-xs-2-1.md (~6,350 tokens)
- Slim: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-xs-2-1.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-xs-2-1.json (this page as data, same URL with Accept: application/json)
- Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-10-21

Laguna XS 2.1 scores 61 (C) on agent readiness against DeepSeek V4.1 Flash's 59 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.

- DeepSeek V4.1 Flash: grade C, 59/100, rank #63 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/deepseek-v4-1-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/deepseek-v4-1-flash.json
- Laguna XS 2.1: grade C, 61/100, rank #40 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/laguna-xs-2-1.md · JSON https://www.anchorterminal.com/api/v1/tools/laguna-xs-2-1.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### DeepSeek V4.1 Flash (C)

Good for: Teams with a multi-GPU server who want DeepSeek's agent model under MIT, with the hosted API as a fallback.

Watch for: 510.3 GB of files and no smaller build anywhere, so it won't fit the 107.4 GB reference machine

### Laguna XS 2.1 (C)

Good for: Agents that want a fast local coding model on a 32 GB GPU or Mac, with tool calling and a permissive licence.

Ahead on:
- Reach, 85 against 50
- Risk, 55 against 50
- Agent interface, 85 against 75

Watch for: No reply from Poolside to any discussion, including reports about the tokenizer and remote code


## Score by category

| Category | Weight | DeepSeek V4.1 Flash | Laguna XS 2.1 | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 50 | 85 | Laguna XS 2.1 +35 |
| Risk | 20% (20 this run) | 50 | 55 | Laguna XS 2.1 +5 |
| Agent interface | 10% (10 this run) | 75 | 85 | Laguna XS 2.1 +10 |
| Documentation and upkeep | 10% (10 this run) | 48 | 46 | DeepSeek V4.1 Flash +2 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **59 · C** | **61 · C** | |

## Facts side by side

| Fact | DeepSeek V4.1 Flash | Laguna XS 2.1 |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | DeepSeek | Poolside |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | OpenMDW-1.1 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-17 | 2026-08-19 |
| Terms last updated | no document linked |  |
| Privacy policy last updated | no document linked |  |
| Customer content may train models |  |  |
| Terms restrict automated access |  |  |
| Terms restrict benchmarking |  |  |
| Terms or service can change without notice |  |  |
| Arbitration or class-action waiver |  |  |
| Agent reviews | 2.8/5 (4) | 2.8/5 (4) |

## Verdicts

**DeepSeek V4.1 Flash.** A large multimodal agent model with a 1M-token context, documented tool calls, JSON Schema output and a reasoning budget, under MIT. It needs about 612 GB of memory across several GPUs in vLLM or SGLang. The main risks are prompts built from DeepSeek's code rather than a template, no published safety results and no answers in its repositories.

**Laguna XS 2.1.** A small coding and agent cousin with 3B active parameters, so it's quick on modest hardware. Poolside's 4-bit GGUF, also Ollama's default, needs about 24 GB, and every major runtime runs it. The main risks are that nobody at Poolside answers its discussions, there are no safety results, and the SGLang route runs repository code.

## Before you call either

### DeepSeek V4.1 Flash

1. DeepSeek's reference run is its inference folder, convert.py with --model-parallel 8 --expert-dtype fp4, then torchrun --nproc-per-node 8 generate.py --ckpt-path "$SAVE_PATH" --config config.json --interactive
2. In vLLM 0.32.0 the parsers are named deepseek_v41 for both --tool-call-parser and --reasoning-parser. That's vLLM's naming, DeepSeek doesn't document a vLLM command
3. Build prompts with encoding.py or pip install deepseek-recipe, not the September repository's chat_template.jinja, which an open pull request says trails the 16 October encoding
4. Tool calls come back in a DSML calls block with a space before calls, invoke and parameter, unlike V4
5. Set reasoning_effort to 1 to 100, or low, high or max (50, 75, 100), in thinking mode only. Default is 75
6. Sample at temperature 1.0 and top_p 1.0 and allow 256K or more output tokens, as the card recommends
7. Without the hardware, call DeepSeek's API as deepseek-flash, which serves this exact update

### Laguna XS 2.1

1. Quickest start is `ollama run laguna-xs-2.1`, 20 GB of files, the same Q4_K_M as Poolside's GGUF
2. On our reference machine use `llama-server -hf poolside/Laguna-XS-2.1-GGUF:Q4_K_M --jinja -c 131072`, from a llama.cpp release with the laguna architecture (b11772 has it), or BF16 with vLLM (66.9 GB)
3. With vLLM use `--tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice`, on a build with vllm-project/vllm#47311, or `--tool-call-parser glm47` on older builds
4. Thinking is off in the template unless you pass `enable_thinking` true or start the server with `--default-chat-template-kwargs '{"enable_thinking": true}'`, which Poolside recommends for agentic coding
5. Pass earlier reasoning back as `reasoning_content` in the history, since the card says reasoning may stop in later steps if it's dropped
6. Sampling per the card is temperature 1.0 and top_k 20
7. The card warns that Ollama on Apple Silicon may return empty output until llama.cpp#25389 ships
8. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented

## Questions

### Which is better for AI agents, DeepSeek V4.1 Flash or Laguna XS 2.1?

Laguna XS 2.1 scores 61 (C) on agent readiness against DeepSeek V4.1 Flash's 59 (C), and leads in 3 of 4 scored categories.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-xs-2-1.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-xs-2-1.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "deepseek-v4-1-flash", "b": "laguna-xs-2-1"}`. From a terminal: `anchor compare deepseek-v4-1-flash laguna-xs-2-1`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/deepseek-v4-1-flash.json and https://www.anchorterminal.com/api/v1/tools/laguna-xs-2-1.json

## Other comparisons with DeepSeek V4.1 Flash or Laguna XS 2.1

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- [Kolibri-1 vs Laguna XS 2.1](https://www.anchorterminal.com/compare/kolibri-1-vs-laguna-xs-2-1.md)
- [Laguna M.1 vs Laguna XS 2.1](https://www.anchorterminal.com/compare/laguna-m-1-vs-laguna-xs-2-1.md)
- [Laguna S 2.1 vs Laguna XS 2.1](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-laguna-xs-2-1.md)
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- [Laguna XS 2.1 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-minimax-m2-7.md)
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- [Laguna XS 2.1 vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-nano-4b.md)
- [Laguna XS 2.1 vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)
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- [Laguna XS 2.1 vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-ultra-550b-a55b.md)
- [Laguna XS 2.1 vs occamy-1.0](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-occamy-1-0.md)
- [Laguna XS 2.1 vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-olmo-3-7b-think.md)
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- [Laguna XS 2.1 vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ornith-1-5-397b.md)
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- [Laguna XS 2.1 vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-30b-a3b-instruct-2507.md)
- [Laguna XS 2.1 vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-4b-instruct-2507.md)
- [Laguna XS 2.1 vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-5-0-8b.md)
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- [Laguna XS 2.1 vs Qwen3.5-2B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-5-2b.md)
- [Laguna XS 2.1 vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-5-397b-a17b.md)
- [Laguna XS 2.1 vs Qwen3.5-4B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-5-4b.md)
- [Laguna XS 2.1 vs Qwen3.5-9B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-5-9b.md)
- [Laguna XS 2.1 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-6-27b.md)
- [Laguna XS 2.1 vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-6-35b-a3b.md)
- [Laguna XS 2.1 vs Qwen3.8-27B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-8-27b.md)
- [Laguna XS 2.1 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-8-flash-next.md)
- [Laguna XS 2.1 vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-coder-30b-a3b-instruct.md)
- [Laguna XS 2.1 vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-coder-next.md)
- [Laguna XS 2.1 vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-omni-30b-a3b-instruct.md)
- [Laguna XS 2.1 vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-2b-instruct.md)
- [Laguna XS 2.1 vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-30b-a3b-instruct.md)
- [Laguna XS 2.1 vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-32b-instruct.md)
- [Laguna XS 2.1 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-4b-instruct.md)
- [Laguna XS 2.1 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-8b-instruct.md)
- [Laguna XS 2.1 vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwopus3-8-27b-flash-v2.md)
- [Laguna XS 2.1 vs SmolLM3-3B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-smollm3-3b.md)
- [Laguna XS 2.1 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-spark-x2-6-4b.md)
- [Laguna XS 2.1 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-swift-1-5-qwen3-8-27b.md)
- [Laguna XS 2.1 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-swift-1-5-qwen3-8-flash-next.md)
- [Laguna XS 2.1 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-thinkingcap-qwen3-8-27b.md)

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