# Devstral Small 2 vs Qwen3-Coder-30B-A3B-Instruct > Devstral Small 2 scores 61 (C) to Qwen3-Coder-30B-A3B-Instruct's 55.7 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-coder-30b-a3b-instruct - Markdown: https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-coder-30b-a3b-instruct.md (~10,200 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 .grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.. and .vs.. 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 Qwen3-Coder-30B-A3B-Instruct's 55.7 (C), and leads in 2 of 4 scored categories. Qwen3-Coder-30B-A3B-Instruct leads on agent interface. Both do open-weight models you run yourself. - 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 - Qwen3-Coder-30B-A3B-Instruct: grade C, 55.7/100, rank #119 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-coder-30b-a3b-instruct.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-coder-30b-a3b-instruct.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 ### 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: - Risk, 60 against 55 - Documentation and upkeep, 46 against 31 Watch for: No limitations, safety results, languages or data on the card ### Qwen3-Coder-30B-A3B-Instruct (C) Good for: Coding agents and local coding tools that want an open model on one 24 GB GPU or a 32 GB Mac, with a plain licence. Ahead on: - Agent interface, 75 against 65 Watch for: No safety results, prompt-injection guidance or security contact, though it's meant to run commands ## Why the edge ### Documentation and upkeep: Devstral Small 2 +15 - 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. - Qwen3-Coder-30B-A3B-Instruct (31): Model card 11 of 20, intended use, architecture, parameters and context are there, but limitations, languages and a knowledge cut-off aren't. Training data 5, the blog describes the mix in general terms without a cut-off. Last change 5, config.json was corrected on 3 December 2025, 336 days before the 4 November 2026 check. Responsiveness 0, counted by script on 4 November, none of 26 discussions has a reply from Alibaba's Qwen staff, and the one Hub-flagged member who replied is the independent quantiser bartowski. Version policy 5, a first release. File health 5, the files now agree with the card, but reports about the parser and an attention error wait without a reply. 11 + 5 + 5 + 0 + 5 + 5 = 31. ### Agent interface: Qwen3-Coder-30B-A3B-Instruct +10 - 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. - Qwen3-Coder-30B-A3B-Instruct (75): Chat template 20, chat_template.jinja in the repository. Tool-calling format 25, the card shows how tools are declared, the README names the parser for SGLang and vLLM, and the repository ships it. Trained tool calling 20, the template renders several calls a turn and the training used agent reinforcement learning. Structured output 0, not documented. Reasoning control 0, the model has no thinking. Context 10, 262,144 tokens native, with the YaRN extension not counted. Template, parser and config unchanged on 4 November. 20 + 25 + 20 + 0 + 0 + 10 = 75. ### Risk: Devstral Small 2 +5 - 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. - Qwen3-Coder-30B-A3B-Instruct (55): File safety 25, safetensors weights, and the shipped parser script runs neither to load the model nor to build prompts. Provenance 15, the files are in the Qwen organisation that Qwen's GitHub links to, with a checksum on every file. Safety evaluation 0, none published. Agent guidance 0, nothing on prompt injection, refusals or safeguards, though it's sold for agents that run commands. Licence risk 15, plain Apache 2.0 that hasn't changed. Security programme 0, no security.txt, disclosure policy or security contact for Qwen was found, and Alibaba's group security centre couldn't be checked, so it's scored as absent. Files unchanged on 4 November, and QwenLM/Qwen3-Coder still has no SECURITY.md. 25 + 15 + 0 + 0 + 15 + 0 = 55. ## Score by category | Category | Weight | Devstral Small 2 | Qwen3-Coder-30B-A3B-Instruct | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 90 | 90 | even | | Risk | 20% (20 this run) | 60 | 55 | Devstral Small 2 +5 | | Agent interface | 10% (10 this run) | 65 | 75 | Qwen3-Coder-30B-A3B-Instruct +10 | | Documentation and upkeep | 10% (10 this run) | 46 | 31 | Devstral Small 2 +15 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **55.7 · C** | | ## Facts side by side | Fact | Devstral Small 2 | Qwen3-Coder-30B-A3B-Instruct | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Mistral AI | Alibaba (Qwen team) | | 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 | 2026-07-15 | 2025-12-03 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | no document linked | | Agent reviews | none | 2.8/5 (4) | ## Verdicts **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. **Qwen3-Coder-30B-A3B-Instruct.** A coding model with 3.3B of 30.5B parameters active per token, for agentic coding tools, repository reading and fill-in-the-middle without a thinking step. It needs about 21 to 23 GB at 4 bits under Apache 2.0, and several providers host it. It runs commands for agents with no safety results or prompt-injection guidance, and Alibaba hasn't answered its discussions. ## Before you call either ### 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 ### Qwen3-Coder-30B-A3B-Instruct 1. Quickest start is `ollama run qwen3-coder:30b`, 19 GB 2. On our reference machine use BF16 with `vllm serve Qwen/Qwen3-Coder-30B-A3B-Instruct --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, 61 GB of weights fits the GPU share 3. Use the qwen3_coder parser in vLLM or SGLang, not hermes, since calls come back as function and parameter blocks rather than JSON 4. Pass tool-call arguments in the history as objects, the template iterates them as a mapping 5. Sampling per the card is temperature 0.7, top_p 0.8, top_k 20 and repetition_penalty 1.05, with up to 65,536 output tokens 6. For code completion use the fill-in-the-middle tokens `<|fim_prefix|>`, `<|fim_suffix|>` and `<|fim_middle|>` from Qwen's README 7. Sandbox the commands and files it touches, since nothing from Alibaba covers prompt injection ## Questions ### Which is better for AI agents, Devstral Small 2 or Qwen3-Coder-30B-A3B-Instruct? Devstral Small 2 scores 61 (C) on agent readiness against Qwen3-Coder-30B-A3B-Instruct's 55.7 (C), and leads in 2 of 4 scored categories. Qwen3-Coder-30B-A3B-Instruct leads on agent interface. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-coder-30b-a3b-instruct.json, and with the fewest tokens: https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-coder-30b-a3b-instruct.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "devstral-small-2", "b": "qwen3-coder-30b-a3b-instruct"}`. 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