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

Agents-A1-4B vs Granite 4.2 30B

Agents-A1-4B and Granite 4.2 30B score within a point of each other on agent readiness, 61 (C) and 61 (C). Granite 4.2 30B leads on agent interface and documentation and upkeep. Both do open-weight models you run yourself.

Best open-weight models you download and run yourself for AI agents · All 4969 open weights comparisons

Agents-A1-4BInternScience (Shanghai AI Laboratory) · Open-weight model C61/100#39 of 135 open-weight models Granite 4.2 30BIBM · Open-weight model C61/100#60 of 135 open-weight models

Which one, for what

Agents-A1-4B C

Best for Local agent loops (search, research, tool calls) on a laptop, a small GPU or a phone-class machine, under a plain licence

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

Watch for

The base isn't declared, and the technical report doesn't cover the 4B

Granite 4.2 30B C

Best for Agents that want a documented, signed, Apache 2.0 reasoning model with tool calling on a 24 GB card

Ahead on

  • Agent interface, 85 against 70
  • Documentation and upkeep, 87 against 43

Watch for

No safety results published for it

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 Granite 4.2 30B +44

Agents-A1-4B 43

Model card 8, it covers intended use, parameters and context but not the architecture, limitations, languages or a knowledge cut-off. Training data 5, a general description with no cut-off. Last change 10, the files went up on 14 July 2026, 112 days before 3 November 2026, and the GitHub repository's last commit is 16 July. Responsiveness 10, InternScience answered one of the three discussions, unchanged on 3 November 2026. Version policy 5, a first release. File health 5, the files agree on the essentials, but the config declares a prediction layer that isn't shipped and the template carries a dated default prompt.

Granite 4.2 30B 87

Model card 17, Intended use (best for), limitations (ethical considerations and limitations), architecture, parameters (30B), context length (128K native) and 12 tested languages are on the card. Training data 20, IBM's disclosure file for the 30B lists the datasets used in pre-training and post-training as machine-readable metadata, and the card describes the three training stages. Last change 15, the last commit to the repository was on 4 September 2026, 67 days before this check. Responsiveness 20, Of the 4 discussions that aren't pull requests (28 August to 3 November 2026), 3 have a reply from an ibm-granite member. Version policy 5, Granite 4.1 30B stays in its own repository and 4.2 came out in new ones. File health 10, Config, template and card agree on the architecture, the 131,072-token context and the thinking switch. 17 + 20 + 15 + 20 + 5 + 10 = 87.

Agent interface Granite 4.2 30B +15

Agents-A1-4B 70

Chat template 20, a Jinja file in the repository. Tool-calling format 10, the card names the qwen3_coder parser but the format is only in the template. Trained tool calling 20, the card says it was trained for tool use and the template renders several calls in one turn. Structured output 0. Reasoning control 10, the template turns thinking off per request and the card's reasoning parser returns it apart from the answer. Context 10, 262,144 tokens.

Granite 4.2 30B 85

Chat template 20, chat_template.jinja in the repository, with roles, tools, tool responses, the thinking switch and low effort. Tool-calling format 25, the card has tool-calling sections for vLLM and SGLang, declares tools with OpenAI's function schema and names the parser to use, qwen3_coder, which both runtimes ship in the released versions read. Trained tool calling 20, the template loops over every entry in an assistant turn's tool_calls, so it renders several calls in one turn, and the card names tool calling and agentic workflows as what it's best for. Structured output 0, the card doesn't document JSON or JSON Schema output or how to ask for it. Reasoning control 10, the card documents thinking on by default, non-thinking and low-effort modes per request through the template (enable_thinking and low_effort), with the reasoning returned apart from the answer. Context 10, 131,072 tokens natively per the card and config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.

Score by category

CategoryWeight this runAgents-A1-4BGranite 4.2 30BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%259090even
Risk20%205555even
Agent interface10%107085Granite 4.2 30B +15
Documentation and upkeep10%104387Granite 4.2 30B +44
Negative events≤1500
TotalC 61/100C 61/100

Facts side by side

FactAgents-A1-4BGranite 4.2 30B
KindOpen-weight modelOpen-weight model
VendorInternScience (Shanghai AI Laboratory)IBM
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0Apache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-07-142026-09-04
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Agent reviews3/5 (4)none

Verdicts

Agents-A1-4B

A small agent cousin for search, research and tool-calling loops, with vision and 262,144 tokens of context. It runs in about 4 GB from InternScience's Q4_K_M GGUF, in vLLM, SGLang, Transformers or llama.cpp. The main risks are an undeclared base, no safety results or agent guidance, and a template that adds its own dated system prompt when you send none.

Granite 4.2 30B

IBM's largest reasoning cousin, with the same strengths as the 3B. It runs in every runtime we count from IBM's own builds on a 24 GB card, is signed, plain Apache 2.0, and comes with a disclosure of its training data. No safety results are published and structured output isn't documented.

Before you call either

Agents-A1-4B

  1. Quickest start is llama-server -hf InternScience/Agents-A1-4B-Q4_K_M-GGUF --jinja, about 3.4 GB with the vision projector
  2. With vLLM use --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder, as on the card, and add --language-model-only for text-only work
  3. Always send your own system message, or the template inserts InternScience's default prompt with the date fixed at 2026-07-14
  4. The card recommends temperature 0.85, top_p 0.95, top_k 20, min_p 0 and presence_penalty 1.1, and there is no generation_config.json to set them
  5. Thinking is on by default, send chat_template_kwargs with enable_thinking false for short tasks, a switch the card doesn't mention
  6. The config declares a multi-token prediction layer that isn't shipped, so don't turn on MTP speculative decoding
  7. Validate JSON output yourself, since structured output isn't documented

Granite 4.2 30B

  1. Start it with ollama run granite4.2:30b, or llama-server -hf ibm-granite/granite-4.2-30b-GGUF:Q4_K_M --jinja
  2. With vLLM 0.32.0 or later leave out --reasoning-parser-plugin, since the parser is built in, and use --tool-call-parser qwen3_coder
  3. Thinking is on by default. Pass enable_thinking false to turn it off or low_effort true for short reasoning
  4. The context is 131,072 tokens. The card's 512K extension isn't documented
  5. Validate JSON output yourself, since structured output isn't documented
  6. IBM recommends Granite Guardian beside it to flag risky inputs and outputs

Questions

Which is better for AI agents, Agents-A1-4B or Granite 4.2 30B?

Agents-A1-4B and Granite 4.2 30B score within a point of each other on agent readiness, 61 (C) and 61 (C). Granite 4.2 30B leads on agent interface and documentation and upkeep.

Other comparisons with Agents-A1-4B or Granite 4.2 30B

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