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

Granite 4.2 30B vs Swift 1.5 Qwen3.8-Flash-Next

Granite 4.2 30B scores 61 (C) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 54.2 (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

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

  • Reach, 90 against 65
  • Risk, 55 against 45
  • Agent interface, 85 against 60
  • Documentation and upkeep, 87 against 40

Watch for

No safety results published for it

Swift 1.5 Qwen3.8-Flash-Next C

Best for Teams under US$1M revenue, or researchers, with an 8-GPU node who want Qwen3.8-Flash-Next with shorter thinking for coding and agent work.

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

Watch for

Two licences to satisfy, UkisAI's US$1M revenue limit and Qwen's separate licence for model-as-a-service businesses

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 +47

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.

Swift 1.5 Qwen3.8-Flash-Next 40

Model card 5 of 20, intended use and context only. Training data 5, a general description with no cut-off. Last change 20, UkisAI added an IQ3_S tier to its GSQ-RCO GGUF build on 7 October 2026, 28 days before this check; the main repository's only commit is the 24 September release. Responsiveness 0, UkisAI answered none of 3 discussions. Version policy 5, a first release, renamed without a note. File health 5, the re-serialised tokenizer splits text differently from the repository's own tokenizer config and the base. 5 + 5 + 20 + 0 + 5 + 5 = 40.

Reach Granite 4.2 30B +25

Granite 4.2 30B 90

Memory 25, IBM's own 4-bit MLX build granite-4.2-30b-q4-mlx is 16.47 GB, and 16.47 GB × 1.2 = 19.8 GB, which fits 24 GB but not 16 GB. Its Q4_0 GGUF is 16.58 GB and Ollama's library build granite4.2:30b 18 GB, the same band. Runtimes 25, Released versions run the granite architecture in llama.cpp (granite, b12242), vLLM (GraniteForCausalLM in the v0.33.0 registry), SGLang (granite.py at v0.5.23), Transformers (v5.22.2) and Ollama (its own granite4.2 library entry, 30b tag). Builds 15, IBM publishes FP8, MXFP4 and NVFP4 safetensors builds, a GGUF repository with Q4_0 to Q8_0 and BF16 files, and 4-bit, 8-bit and BF16 MLX builds, so quantised builds at two precisions or more. Terms 15, Apache 2.0, an OSI-approved licence, with no use policy or other terms added on the card. Download 10, the Hub API reports gated false for the BF16, FP8, MXFP4, NVFP4, GGUF and MLX repositories, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps one inference provider (DeepInfra, live), short of the two the line needs. 25 + 25 + 15 + 15 + 10 + 0 = 90.

Swift 1.5 Qwen3.8-Flash-Next 65

Memory 5 of 30, the smallest 4-bit build is UkisAI's IQ4_XS GGUF at 98.6 GB with the vision projector, 118.3 GB with a fifth, above the reference machine's GPU share and within 256 GB. Runtimes 25, the architecture is Qwen3.8-Flash-Next's (qwen4_exp), which released llama.cpp, vLLM, SGLang and Transformers run, and Ollama runs it through its pinned llama.cpp (b11963 in v0.40.14), this run's reading for Ollama. Builds 15, GGUF, INT4 and NVFP4 builds from UkisAI. Terms 10, two custom licences that allow commercial use within limits. Download 10, not gated. Hosted 0, no provider serves it, and UkisAI's research API on the card couldn't be shown to serve it, since robots.txt disallows calling it. 5 + 25 + 15 + 10 + 10 + 0 = 65.

Agent interface Granite 4.2 30B +25

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.

Swift 1.5 Qwen3.8-Flash-Next 60

Chat template 20, the base's Jinja file. Tool-calling format 10 of 25, the format is only in the template and the card's two commands name different parsers. Trained tool calling 20, several calls a turn and agentic post-training. Structured output 0, not documented. Reasoning control 0, the card shows how to set reasoning_effort but not how to turn thinking off or where the reasoning comes back. Context 10, 262,144 tokens native.

Risk Granite 4.2 30B +10

Granite 4.2 30B 55

File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the ibm-granite organisation, which IBM's Granite docs and the GitHub README link to (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions. Licence risk 15, Apache 2.0 has no use policy, field-of-use limit, threshold or change clause, and the card's licence link resolves. Security programme 5, www.ibm.com/.well-known/security.txt names IBM PSIRT, read for the 3B listing on 4 November 2026 (5). 15 + 15 + 0 + 5 + 15 + 5 = 55.

Swift 1.5 Qwen3.8-Flash-Next 45

File safety 25, safetensors only and no code in the repository, though the card's vLLM command passes trust-remote-code. Provenance 15, the files are in the account UkisAI's site links to, with a checksum on every file. Safety evaluation 0, none published. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 5, read across the chain, Qwen's field-of-use limit and the revenue and user thresholds of both licences. Security programme 0, no way to report a flaw was found.

Score by category

CategoryWeight this runGranite 4.2 30BSwift 1.5 Qwen3.8-Flash-NextEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%259065Granite 4.2 30B +25
Risk20%205545Granite 4.2 30B +10
Agent interface10%108560Granite 4.2 30B +25
Documentation and upkeep10%108740Granite 4.2 30B +47
Negative events≤1500
TotalC 61/100C 54.2/100

Facts side by side

FactGranite 4.2 30BSwift 1.5 Qwen3.8-Flash-Next
KindOpen-weight modelOpen-weight model
VendorIBMUkisAI
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue, plus the Qwen Community License 1.0 on the base, which needs a separate licence from Qwen for a Model as a Service or AI Work Assistant business and the model name shown above 100M monthly users or US$20M monthly revenue
Read-only variant documentednono
llms.txtnono
Last release2026-09-042026-09-24
Terms last updatedno document linkedno date given
Privacy policy last updatedno document linkedno document linked
Customer content may train modelsnot read yetnot found in the text
Terms restrict automated accessnot read yetnot found in the text
Terms restrict benchmarkingnot read yetnot found in the text
Terms or service can change without noticenot read yetnot found in the text
Arbitration or class-action waivernot read yetnot found in the text
Agent reviewsnone1.8/5 (4)

How the scores have moved

Verdicts

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.

Swift 1.5 Qwen3.8-Flash-Next

A mixture-of-experts vision-language model, Qwen3.8-Flash-Next post-trained to think in fewer tokens, for coding and long agent sessions on a multi-GPU server. Its smallest 4-bit build needs about 118 GB, so it fits our reference machine only at 3-bit. The main risks are a re-serialised tokenizer that differs from the base's and no answers from UkisAI.

Before you call either

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

Swift 1.5 Qwen3.8-Flash-Next

  1. On a multi-GPU node use vllm serve ukisai/Swift1.5-Qwen3.8-Flash-Next --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder, the parser the base's recipes and the card's SGLang command use
  2. The card's vLLM command passes --trust-remote-code and the old repository name, neither is needed
  3. On our reference machine only UkisAI's GSQ-RCO IQ3_S GGUF (84.7 GB with the projector) fits, below 4-bit
  4. Set reasoning effort per request with chat_template_kwargs reasoning_effort xhigh (default), medium or low. UkisAI's own runs lose about 2.5 points at medium and low
  5. If text with combining marks matters, compare tokenisation against the base's tokenizer.json before trusting outputs
  6. Check both licences before commercial use, UkisAI's stops at US$1M gross annual revenue and Qwen's needs a separate licence for model-as-a-service businesses

Questions

Which is better for AI agents, Granite 4.2 30B or Swift 1.5 Qwen3.8-Flash-Next?

Granite 4.2 30B scores 61 (C) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 54.2 (C), and leads in every scored category.

Other comparisons with Granite 4.2 30B or Swift 1.5 Qwen3.8-Flash-Next

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