# DeepSeek V4.1 Flash vs Qwen3.8-27B

> Qwen3.8-27B and DeepSeek V4.1 Flash score within a point of each other for open-weight models you run yourself, 63.7 and 62.8 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-8-27b
- Markdown: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-8-27b.md (~1,750 tokens)
- Slim: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-8-27b.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-8-27b.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

Qwen3.8-27B and DeepSeek V4.1 Flash score within a point of each other on agent readiness, 63.7 (B) and 62.8 (B). DeepSeek V4.1 Flash leads on schema & documentation, maintenance & community and transparency & trust. Both do open-weight models you run yourself.

- DeepSeek V4.1 Flash: grade B, 62.8/100, rank #472 of 1184. Markdown https://www.anchorterminal.com/tools/deepseek-v4-1-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/deepseek-v4-1-flash.json
- Qwen3.8-27B: grade B, 63.7/100, rank #428 of 1184. Markdown https://www.anchorterminal.com/tools/qwen3-8-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-8-27b.json
- All 3 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### DeepSeek V4.1 Flash (B)

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

Ahead on:
- Schema & documentation, 69 against 63
- Maintenance & community, 70 against 25
- Transparency & trust, 69 against 47

Watch for: 510.3 GB of files, with no memory needs stated by DeepSeek

### Qwen3.8-27B (B)

Good for: People who want a capable open model on one 24 to 32 GB GPU or Mac with the widest runtime support and a plain licence.

Ahead on:
- Reliability, 90 against 72
- Agent ergonomics, 85 against 70
- Security & auth, 45 against 35

Watch for: The card doesn't describe tool calling, so the parser to use comes from vLLM's and SGLang's recipes


## Score by category

| Category | Weight | DeepSeek V4.1 Flash | Qwen3.8-27B | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 72 | 90 | Qwen3.8-27B +18 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 69 | 63 | DeepSeek V4.1 Flash +6 |
| Agent ergonomics | 13% (16.2 this run) | 70 | 85 | Qwen3.8-27B +15 |
| Security & auth | 14% (17.5 this run) | 35 | 45 | Qwen3.8-27B +10 |
| Payments & pricing | 10% (12.5 this run) | 60 | 60 | even |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 70 | 25 | DeepSeek V4.1 Flash +45 |
| Transparency & trust | 7% (8.8 this run) | 69 | 47 | DeepSeek V4.1 Flash +22 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **62.8 · B** | **63.7 · B** | |

## Facts side by side

| Fact | DeepSeek V4.1 Flash | Qwen3.8-27B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | DeepSeek | Alibaba (Qwen team) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-17 | 2026-08-14 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | 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) | 3/5 (4) |

## Verdicts

**DeepSeek V4.1 Flash.** DeepSeek V4.1 Flash gives a 1M-token context, trained tool calls and an effort budget from 1 to 100 under the MIT licence, with every release kept in its own repository. It needs 510 GB of files and runs in vLLM or mlx-lm, prompts come from DeepSeek's code rather than a template, and no safety results or training sources are published.

**Qwen3.8-27B.** Qwen3.8-27B runs nearly anywhere, in vLLM, SGLang, llama.cpp, Ollama and MLX, with an 18 GB Ollama build and Apache 2.0 terms. The card leaves out tool calling, hardware needs, training data, limitations and safety results, the files haven't changed since 14 August, and Alibaba answered none of the 40 most recent discussions.

## Before you call either

### DeepSeek V4.1 Flash

1. Build prompts with encoding.py or deepseek-recipe; the September repository's Jinja template is behind the 16 October encoding changes per an open pull request
2. Parse tool calls from the DSML `calls` block, which V4.1 writes with a space before `calls`, `invoke` and `parameter`, unlike V4
3. Pass reasoning effort as a number from 1 to 100, or low, high or max (50, 75, 100), in thinking mode
4. Allow 256K or more output tokens at high effort, as the card recommends
5. Use DeepSeek's hosted API (the deepseek-api listing) if you can't hold 510 GB of weights

### Qwen3.8-27B

1. Serve it with `--tool-call-parser qwen3_coder` and `--reasoning-parser qwen3`; the Hermes parser leaves its tool calls unparsed, per SGLang's cookbook
2. Set `enable_thinking: false` in `chat_template_kwargs` for short tasks, since thinking is on by default
3. Use the sampling the card gives for each mode, `temperature=1.0, top_p=0.95` thinking and `temperature=0.7, top_p=0.8, presence_penalty=1.5` without
4. Turn on YaRN only for contexts beyond 262,144 tokens; the card says static YaRN can cost quality on short texts
5. Check JSON output yourself, since structured output isn't documented for the model

## Questions

### Which is better for AI agents, DeepSeek V4.1 Flash or Qwen3.8-27B?

Qwen3.8-27B and DeepSeek V4.1 Flash score within a point of each other on agent readiness, 63.7 (B) and 62.8 (B). DeepSeek V4.1 Flash leads on schema & documentation, maintenance & community and transparency & trust.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-8-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-8-27b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "deepseek-v4-1-flash", "b": "qwen3-8-27b"}`. From a terminal: `anchor compare deepseek-v4-1-flash qwen3-8-27b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/deepseek-v4-1-flash.json and https://www.anchorterminal.com/api/v1/tools/qwen3-8-27b.json

## Other comparisons with DeepSeek V4.1 Flash or Qwen3.8-27B

- [DeepSeek V4.1 Flash vs Kolibri-1](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kolibri-1.md)
- [Kolibri-1 vs Qwen3.8-27B](https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-8-27b.md)

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