Head to head · Open-weight models you run yourself · October 2026 research run
Devstral Small 2 vs Gemma 4 12B IT
Gemma 4 12B IT scores 72.3 (BB) on agent readiness against Devstral Small 2's 61 (C), and leads in every scored category. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 6359 open weights comparisons
Which one, for what
Best for Developers who want a dependable local coding agent on one consumer GPU or a 32 GB Mac
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
No limitations, safety results, languages or data on the card
Best for Agents on a 12 GB or larger GPU or Mac that want a permissively licensed tool-calling model with images, audio and long context
Ahead on
- Reach, 95 against 90
- Risk, 70 against 60
- Agent interface, 85 against 65
- Documentation and upkeep, 85 against 46
Also in its favour
- Agent-ready, a grade of BB or better
Watch for
No safety evaluation figures published
Why the edge
Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.
Documentation and upkeep Gemma 4 12B IT +39
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.
Gemma 4 12B IT 85
Model card 20, all seven items are there. Training data 10, a general description by type with a January 2025 cut-off and the filtering, but no sources, mix or sizes. Last change 20, Google merged a fix for 210 glitch tokens in tokenizer.json on 30 October 2026, two days before 1 November. Responsiveness 20, Google replied to 18 of 34 questions and reports, the last on 2026-07-31, re-counted on 1 November. Version policy 10, earlier Gemma generations keep their own repositories and gemma.ai has a dated releases page. File health 5, the files agree with the card but a report about them waits without a reply.
Agent interface Gemma 4 12B IT +20
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.
Gemma 4 12B IT 85
Chat template 20, a Jinja file in the repository. Tool-calling format 25, Google documents how tools are declared and how calls come back, and ships a response schema for Transformers' parser. Trained tool calling 20, several calls in one turn. Structured output 0, nothing documents how to ask the weights for JSON or a schema. Reasoning control 10, thinking is off by default, turned on per request, and returned in a thought channel. Context 10, 262,144 tokens native.
Risk Gemma 4 12B IT +10
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.
Gemma 4 12B IT 70
File safety 25, safetensors and GGUF only, with nothing from the repository run on load. Provenance 15, the files sit in Google's own organisation and every file has a checksum. Safety evaluation 0, the card says testing showed minimal policy violations but gives no figures. Agent guidance 10, the card names the harm categories its safety training covers and asks for content-safety safeguards, and the tool guide says to validate calls, with no prompt-injection guidance. Licence risk 15, Apache 2.0 with no added policy, no threshold and a working link. Security programme 5, Google's security.txt and reward programme give a route to report a flaw, but no public Gemma advisories were found.
Reach Gemma 4 12B IT +5
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.
Gemma 4 12B IT 95
Memory 30, Google's own 4-bit QAT GGUF with its projector is 7.15 GB, which with a fifth on top is 8.6 GB and fits in 16 GB or less. Runtimes 25, released support in Transformers, vLLM, SGLang, llama.cpp, Ollama and LM Studio, six of six, all for the separate gemma4_unified architecture. Builds 15, Google publishes a Q4_0 GGUF and a w4a16 build, read as two kinds as for the 31B. Terms 15, plain Apache 2.0 with nothing added. Download 10, no account or gate. Hosted 0, the Gemini API serves only the two largest sizes and no inference provider lists this one.
Score by category
| Category | Weight this run | Devstral Small 2 | Gemma 4 12B IT | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 90 | 95 | Gemma 4 12B IT +5 |
| Risk | 20%20 | 60 | 70 | Gemma 4 12B IT +10 |
| Agent interface | 10%10 | 65 | 85 | Gemma 4 12B IT +20 |
| Documentation and upkeep | 10%10 | 46 | 85 | Gemma 4 12B IT +39 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | BB 72.3/100 |
Facts side by side
| Fact | Devstral Small 2 | Gemma 4 12B IT |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Mistral AI | Google DeepMind |
| 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 | yes |
| Last release | 2026-07-15 | 2026-07-20 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 3.8/5 (4) |
How the scores have moved
Devstral Small 2
No score change since it was listed.
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.
Gemma 4 12B IT
A mid-size cousin for agent work with tools, optional thinking and image and audio input, under Apache 2.0. Google's 4-bit build needs under 9 GB with working space, so it runs on a 12 GB GPU or any recent Mac in all the common runtimes. The main risks are no published safety results and no documented JSON output.
Before you call either
Devstral Small 2
ollama run devstral-small-2is the quickest start, at 15 GB- On vLLM pass
--tool-call-parser mistral --enable-auto-tool-choice - Send the system prompt from CHAT_SYSTEM_PROMPT.txt in the repository. The card says results depend on it
- Use a low temperature. The repository's default is 0.15
- Plan for one tool call per turn
- The same model is on our Mistral API listing if you'd rather not host it
Gemma 4 12B IT
- On the reference machine run
llama-server -hf google/gemma-4-12B-it-qat-q4_0-gguf --hf-file gemma-4-12b-it-qat-q4_0.gguf --jinja -c 131072, and add--mmprojwith mmproj-gemma-4-12b-it-qat-q4_0.gguf for images and audio - With Ollama run
ollama run gemma4:12band raise num_ctx or OLLAMA_CONTEXT_LENGTH, since the default context is small and agents want at least 64K - In vLLM or SGLang pass
--tool-call-parser gemma4 --reasoning-parser gemma4(vLLM also--enable-auto-tool-choice), or tool calls come back as text; Google's w4a16 build is the 4-bit route there - Thinking is off by default; pass enable_thinking=True or start the system prompt with <|think|>, and drop earlier turns' thinking from history except on tool-call turns
- Put images before the text and audio after it, keep audio to 30 seconds and video to 60 frames, and validate tool names and arguments before running them
- Transformers needs 5.10.0 or later for gemma4_unified, and mlx-lm has no gemma4_unified code, so on a Mac use mlx-vlm, LM Studio or Ollama
Questions
Which is better for AI agents, Devstral Small 2 or Gemma 4 12B IT?
Gemma 4 12B IT scores 72.3 (BB) on agent readiness against Devstral Small 2's 61 (C), and leads in every scored category.
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Machine-readable
- This page as Markdown
/compare/devstral-small-2-vs-gemma-4-12b-it.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/devstral-small-2.json·/api/v1/tools/gemma-4-12b-it.json - From a terminal
anchor compare devstral-small-2 gemma-4-12b-it(the CLI) - Over MCP
compare_tools {"a": "devstral-small-2", "b": "gemma-4-12b-it"}at/mcp, no key