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

Devstral Small 2 vs InternVL3.5-GPT-OSS-20B-A4B-Preview

Devstral Small 2 scores 61 (C) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 53.2 (D), 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

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

  • Reach, 90 against 65
  • Risk, 60 against 55
  • Agent interface, 65 against 45
  • Documentation and upkeep, 46 against 28

Watch for

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

InternVL3.5-GPT-OSS-20B-A4B-Preview D

Best for Agents with 64 GB to spare that want an Apache-licensed image and video reader with optional thinking and can live with a stale preview.

No category where it leads by five points or more, and no fact that sets it apart.

Watch for

No quantised build from OpenGVLab, so about 51 GB at BF16

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.

Reach Devstral Small 2 +25

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.

InternVL3.5-GPT-OSS-20B-A4B-Preview 65

Memory 15 of 30, the only builds are BF16 at 42.5 GB by the repository's safetensors index, or 51 GB with a fifth added, which fits 64 GB. Runtimes 25, Transformers and vLLM load this repository, and SGLang, llama.cpp and Ollama (through llama.cpp b11963, which Ollama v0.40.14 pins) run the same weights in their GitHub format. Builds 0, OpenGVLab publishes no quantised build. Terms 15, Apache 2.0 with nothing added by the bases. Download 10, not gated. Hosted 0, no provider serves it.

Agent interface Devstral Small 2 +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.

InternVL3.5-GPT-OSS-20B-A4B-Preview 45

Chat template 20, a Jinja file in the repository, though it doesn't match the tokenizer. Tool-calling format 10 of 25, only the twin's inherited harmony template mentions tools. Trained tool calling 0, no evidence it was trained to call tools. Structured output 0. Reasoning control 10, a system prompt turns thinking on and the card says the reasoning comes back in think tags. Context 5, trained at 32K tokens.

Documentation and upkeep Devstral Small 2 +18

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.

InternVL3.5-GPT-OSS-20B-A4B-Preview 28

Model card 8 of 20, the family card misses limitations, context, languages and a cut-off. Training data 5, described without a cut-off. Last change 0, the last commit to either repository is 8 September 2025 and to OpenGVLab's reference code 22 September 2025, over a year before 4 November 2026. Responsiveness 10, OpenGVLab answered 3 of 8 questions across the two repositories, none since September 2025. Version policy 5, a first release. File health 0, the template uses markers the tokenizer doesn't have and contradicts the twin's template, and the card names the wrong language model in its licence line.

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.

InternVL3.5-GPT-OSS-20B-A4B-Preview 55

File safety 25, safetensors only and no code in the repository. Provenance 15, the files are in the OpenGVLab organisation its GitHub links, with a checksum on every file. Safety evaluation 0 and agent guidance 0, nothing published. Licence risk 15, Apache 2.0 over Apache and MIT bases, with gpt-oss's usage policy asking only for lawful use. Security programme 0, no policy, security.txt or advisories.

Score by category

CategoryWeight this runDevstral Small 2InternVL3.5-GPT-OSS-20B-A4B-PreviewEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%259065Devstral Small 2 +25
Risk20%206055Devstral Small 2 +5
Agent interface10%106545Devstral Small 2 +20
Documentation and upkeep10%104628Devstral Small 2 +18
Negative events≤1500
TotalC 61/100D 53.2/100

Facts side by side

FactDevstral Small 2InternVL3.5-GPT-OSS-20B-A4B-Preview
KindOpen-weight modelOpen-weight model
VendorMistral AIOpenGVLab (Shanghai AI Laboratory)
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0Apache-2.0; a merge of openai/gpt-oss-20b (Apache 2.0 with OpenAI's usage policy) and OpenGVLab/InternViT-300M-448px-V2_5 (MIT)
Read-only variant documentednono
llms.txtnono
Last release2026-07-152025-09-08
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Agent reviewsnone1.8/5 (4)

How the scores have moved

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.

InternVL3.5-GPT-OSS-20B-A4B-Preview

An image and video reader on gpt-oss-20b with an optional thinking mode, a preview untouched for over a year. It needs about 51 GB in BF16, since OpenGVLab publishes no quantised build, and runs in Transformers and vLLM. The main risk is a chat template in the Transformers-format repository that doesn't match its tokenizer, with no safety results.

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

InternVL3.5-GPT-OSS-20B-A4B-Preview

  1. Serve with vllm serve OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF --max-model-len 32768, which is our command; the card gives none for vLLM
  2. Check the rendered prompt before relying on it, since the shipped template's <|im_start|> markers aren't tokens in the gpt-oss tokenizer; the GitHub-format repository's harmony template is the alternative
  3. Use Transformers 4.55.0 or later, as the card requires for this size
  4. For thinking, send the card's thinking system prompt and parse the reasoning out of the <think> tags, with temperature 0.6 and sampling on as the card advises
  5. Keep inputs within 32K tokens, the length it was trained at, whatever the config says

Questions

Which is better for AI agents, Devstral Small 2 or InternVL3.5-GPT-OSS-20B-A4B-Preview?

Devstral Small 2 scores 61 (C) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 53.2 (D), and leads in every scored category.

Other comparisons with Devstral Small 2 or InternVL3.5-GPT-OSS-20B-A4B-Preview

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