Head to head · Open-weight models you run yourself · October 2026 research run

Carbon-X vs Devstral Small 2

Carbon-X and Devstral Small 2 score within a point of each other on agent readiness, 61 (C) and 61 (C). Devstral Small 2 leads on agent interface. 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

Carbon-X C

Best for Agents that need a second opinion on DNA variants and regulatory regions on a 16 to 24 GB machine, without sending sequences to a hosted model.

Ahead on

  • Risk, 65 against 60
  • Documentation and upkeep, 72 against 46

Watch for

Not clinically validated and no wet-lab validation reported

Devstral Small 2 C

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

Ahead on

  • Agent interface, 65 against 35

Watch for

No limitations, safety results, languages or data on the card

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.

Agent interface Devstral Small 2 +30

Carbon-X 35

Chat template 20, a Jinja file in the repository. Tool-calling format 10, only the inherited template shows the format and no parser is named. Trained tool calling 0, nothing says it was trained to call tools. Structured output 0, not documented. Reasoning control 0, thinking can be switched off but was never trained, and the card asks you to keep it off. Context 5, 40,960 tokens from the base, though Hugging Face trained at 8,192 and under 16K. Most of what the card documents for agents is Carbon-X as a tool another model calls, which this checklist doesn't read.

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.

Documentation and upkeep Carbon-X +26

Carbon-X 72

Model card 17, everything but a knowledge cut-off. Training data 15, the mid-training sources and shares are listed, the instruction and fine-tuning data aren't named, and there's no cut-off. Last change 20, 28 October 2026, when config.json, generation_config.json, tokenizer_config.json and tokenizer.py were fixed. Responsiveness 10, organisation members replied to 2 of the 4 questions so far. Version policy 5, a first release whose history was squashed without a note. File health 5, the card now sends llama.cpp users to the publisher's GGUF repository, but the tokenizer config's 131,072 still contradicts the card's 40,960. 17 + 15 + 20 + 10 + 5 + 5 = 72.

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 Carbon-X +5

Carbon-X 65

File safety 15, safetensors and GGUF only, but the documented loading path runs the repository's tokenizer.py through trust_remote_code. Provenance 15, the files are in HuggingFaceBio, which Hugging Face's GitHub repository links, with a checksum on every file. Safety evaluation 10, Hugging Face reports its own dual-use check on sequence editing in the launch post, without numbers or a harness. Agent guidance 5, general cautions only. Licence risk 15, plain Apache 2.0. Security programme 5, Hugging Face's security.txt is a reporting route, and public advisories couldn't be checked.

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.

Score by category

CategoryWeight this runCarbon-XDevstral Small 2Edge
Capability35%pendingpendingpendingnot scored in this run
Reach25%259090even
Risk20%206560Carbon-X +5
Agent interface10%103565Devstral Small 2 +30
Documentation and upkeep10%107246Carbon-X +26
Negative events≤1500
TotalC 61/100C 61/100

Facts side by side

FactCarbon-XDevstral Small 2
KindOpen-weight modelOpen-weight model
VendorHugging Face (HuggingFaceBio)Mistral AI
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0Apache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-10-282026-07-15
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Agent reviews3/5 (4)none

How the scores have moved

Verdicts

Carbon-X

A DNA specialist cousin that another agent calls for variant-effect and sequence questions, with DNA kept on the machine. Hugging Face's Q4_K_M GGUF needs about 12 GB and Q8_0 about 19 GB, on llama.cpp b11742 or later. It isn't clinically validated, isn't trained for tool calls or reasoning, and its Python tokenizer runs repository code.

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

Carbon-X

  1. Quickest start is llama-server -hf HuggingFaceBio/Carbon-X-GGUF:Q8_0 -ngl 99 -c 40960 --jinja --chat-template-kwargs '{"enable_thinking": false}' --temp 0.6 --top-p 0.95 --top-k 20 --min-p 0, on llama.cpp b11742 or later
  2. With vLLM or Transformers pass trust_remote_code, and expect the tokenizer to fetch Qwen/Qwen3-4B-Base at a pinned revision on first load
  3. Keep enable_thinking false and the card's sampling (temperature 0.6, top_p 0.95, top_k 20), the settings every reported result used
  4. Keep DNA in files and pass sequences by name, compute positions and base changes in code and send them as context, the card says the model miscounts long sequences
  5. Only A, C, G and T form 6-mers, so filter N and other IUPAC codes first or they become a single oov token
  6. Strip the dna tags from answers if you need bare sequence, llama.cpp keeps them in the text
  7. Cap max_tokens for next-base questions, long DNA generation is slow and degrades

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, Carbon-X or Devstral Small 2?

Carbon-X and Devstral Small 2 score within a point of each other on agent readiness, 61 (C) and 61 (C). Devstral Small 2 leads on agent interface.

Other comparisons with Carbon-X or Devstral Small 2

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