# Arete-27B vs Devstral Small 2

> Devstral Small 2 scores 61 (C) to Arete-27B's 55.1 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/arete-27b-vs-devstral-small-2
- Markdown: https://www.anchorterminal.com/compare/arete-27b-vs-devstral-small-2.md (~9,050 tokens)
- Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: 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)
- DNS: dig +short TXT <slug>.grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.<category>. and <a>.vs.<b>. under the zone, and the same under grade.letme.dev)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-11-10

Devstral Small 2 scores 61 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in 3 of 4 scored categories. Arete-27B leads on documentation and upkeep. Both do open-weight models you run yourself.

- Arete-27B: grade C, 55.1/100, rank #123 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/arete-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/arete-27b.json
- Devstral Small 2: grade C, 61/100, rank #60 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/devstral-small-2.md · JSON https://www.anchorterminal.com/api/v1/tools/devstral-small-2.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### Arete-27B (C)

Good for: Agents that want Qwen3.8-27B's coding and tool use without its handling of politically sensitive topics, on a 24 GB card or a 128 GB machine

Ahead on:
- Documentation and upkeep, 63 against 46

Watch for: No quantised build from Nous Research, so the GGUF route depends on a third party

### Devstral Small 2 (C)

Good for: Developers who want a dependable local coding agent on one consumer GPU or a 32 GB Mac

Ahead on:
- Reach, 90 against 50
- Risk, 60 against 55
- Agent interface, 65 against 60

Watch for: No limitations, safety results, languages or data on the card


## Why the edge

### Reach: Devstral Small 2 +40

- Arete-27B (50): Memory 10, the only build from Nous Research is the 54.7 GB BF16 release, which needs about 66 GB and fits the reference machine. Runtimes 15, vLLM, SGLang and Transformers run the base's architecture from those files, and llama.cpp isn't credited because Nous Research publishes no GGUF. Builds 0, no quantised build of its own. Terms 15, Apache 2.0. Download 10, not gated. Hosted 0, no provider. 10 + 15 + 0 + 15 + 10 + 0 = 50.
- Devstral Small 2 (90): Memory 25, the smallest build at 4 bits from a runtime's own library is Ollama's devstral-small-2 at Q4_K_M, 15 GB, and 15 GB × 1.2 = 18 GB, within 24 GB. Mistral's own FP8 files are 25.79 GB. Runtimes 25, Released vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 have the mistral3 model, llama.cpp b12242 has the architecture with a GGUF from its own organisation, and Ollama's library has devstral-small-2. Builds 10, one quantised precision from Mistral, the FP8 weights it releases. Terms 15, Apache 2.0 in the card's metadata and text, an OSI licence with nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, Mistral's own API serves Devstral Small 2: the card says the model is available through its API and points there when a local runtime underperforms. 25 + 25 + 10 + 15 + 10 + 5 = 90.

### Documentation and upkeep: Arete-27B +17

- Arete-27B (63): Model card 8, no limitations, context length, languages or cut-off. Training data 20, the training pairs and the evaluation set are published with their counts. Last change 20, a commit on 6 November 2026. Responsiveness 0, none of 13 discussions has a reply from Nous Research. Version policy 5, a first release. File health 10, the files agree. 8 + 20 + 20 + 0 + 5 + 10 = 63.
- Devstral Small 2 (46): Model card 11, Intended use, the architecture, the parameters and the context are on the card. Training data 0, Nothing on the card about the training data. Last change 10, the last commit, on 15 July 2026, updated the files to match mistral-common, 118 days before this check and within 180. Responsiveness 10, Of the 23 most recent discussions that aren't pull requests (9 December 2025 to 6 November 2026), 11 have a reply from a mistralai member, which is some. Version policy 5, Devstral Small 1.1 stays in its own repository, which this card links as the predecessor, with no dated note on the older card that we read. File health 10, Config, template, tokenizer and card agree, and the June and July commits fixed stray tokens and brought the files in line with mistral-common. 11 + 0 + 10 + 10 + 5 + 10 = 46.

### Risk: Devstral Small 2 +5

- Arete-27B (55): File safety 25, safetensors only and no remote code. Provenance 15, Nous Research's own organisation, linked from its blog, with a hash on every file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 15, plain Apache 2.0. Security programme 0, no way to report a flaw found. 25 + 15 + 0 + 0 + 15 + 0 = 55.
- Devstral Small 2 (60): File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, files in Mistral's verified mistralai organisation, which Mistral's site links to per our Mistral Large 4 listing (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on prompt injection, instruction priority or what the model refuses. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 5, mistral.ai's security.txt gives a security contact and a submission policy, as read for the Mistral Large 4 listing (5). 25 + 15 + 0 + 0 + 15 + 5 = 60.

### Agent interface: Devstral Small 2 +5

- Arete-27B (60): Chat template 20, a Jinja file in the repository. Tool-calling format 10, only the template says it. Trained tool calling 20, several calls a turn and agentic work as an intended use. Structured output 0 and reasoning control 0, neither is documented on the card. Context 10, 262,144 tokens native. 20 + 10 + 20 + 0 + 0 + 10 = 60.
- Devstral Small 2 (65): Chat template 20, chat_template.jinja in the repository (11,362 characters), with the tokenizer's tekken file for mistral-common. Tool-calling format 25, the card documents tools in the OpenAI function format with an example, names the mistral tool-call parser with auto tool choice for vLLM, shows the same for SGLang, and ships test prompts with the expected tool-call output. Trained tool calling 10, the card says the model excels at using tools, and its tests cover one tool call and calls made one at a time in sequence. Structured output 0, Nothing on the card about JSON or schema-constrained answers for this model. Reasoning control 0, no reasoning mode is documented, and a June 2026 commit removed stray thinking tokens from the files. Context 10, 256K tokens per the card, served at 262,144 in its vLLM command. 20 + 25 + 10 + 0 + 0 + 10 = 65.

## Score by category

| Category | Weight | Arete-27B | Devstral Small 2 | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 50 | 90 | Devstral Small 2 +40 |
| Risk | 20% (20 this run) | 55 | 60 | Devstral Small 2 +5 |
| Agent interface | 10% (10 this run) | 60 | 65 | Devstral Small 2 +5 |
| Documentation and upkeep | 10% (10 this run) | 63 | 46 | Arete-27B +17 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **55.1 · C** | **61 · C** | |

## Facts side by side

| Fact | Arete-27B | Devstral Small 2 |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Nous Research | Mistral AI |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-11-06 | 2026-07-15 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |

## Verdicts

**Arete-27B.** Qwen3.8-27B with its answers on politically sensitive topics retrained, four days old when read. The training data is published, which is rare. Nous Research ships BF16 only, so a small machine runs it from bartowski's GGUF, and the card documents none of the tool or thinking controls it inherits.

**Devstral Small 2.** The easy one to run: a 24B coding agent under Apache 2.0 that five runtimes carry, 15 GB at 4 bits in Ollama, with a 256K context, image input and well-documented tool calling. It's eleven months old, calls tools one at a time, has no reasoning mode, and its card says nothing on limits, safety or data.

## Before you call either

### Arete-27B

1. Start it with `llama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja`, a third party's conversion, or serve the BF16 files with vLLM
2. Use the base's parsers in vLLM, `--reasoning-parser qwen3 --tool-call-parser qwen3_coder`, and check tool calls first, since the template differs slightly from Qwen3.8-27B's
3. Thinking switches with `enable_thinking` in the template. The card's own evaluation ran with it off
4. Expect answers on politically sensitive topics to differ from Qwen3.8-27B's. That is what the fine-tune is for
5. Validate JSON output yourself, since structured output isn't documented

### Devstral Small 2

1. `ollama run devstral-small-2` is the quickest start, at 15 GB
2. On vLLM pass `--tool-call-parser mistral --enable-auto-tool-choice`
3. Send the system prompt from CHAT_SYSTEM_PROMPT.txt in the repository. The card says results depend on it
4. Use a low temperature. The repository's default is 0.15
5. Plan for one tool call per turn
6. The same model is on our Mistral API listing if you'd rather not host it

## Questions

### Which is better for AI agents, Arete-27B or Devstral Small 2?

Devstral Small 2 scores 61 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in 3 of 4 scored categories. Arete-27B leads on documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/arete-27b-vs-devstral-small-2.json, and with the fewest tokens: https://www.anchorterminal.com/compare/arete-27b-vs-devstral-small-2.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "arete-27b", "b": "devstral-small-2"}`. From a terminal: `anchor compare arete-27b devstral-small-2`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/arete-27b.json and https://www.anchorterminal.com/api/v1/tools/devstral-small-2.json

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- [Devstral Small 2 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/devstral-small-2-vs-wevisone-vl-4b.md)
- [Devstral Small 2 vs ZAYA1-8B](https://www.anchorterminal.com/compare/devstral-small-2-vs-zaya1-8b.md)

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