Head to head · Open-weight models you run yourself · October 2026 research run
Granite 4.2 8B vs Qwen3-VL-8B-Instruct
Granite 4.2 8B scores 61 (C) on agent readiness against Qwen3-VL-8B-Instruct's 54.9 (C), and leads in 2 of 4 scored categories. Qwen3-VL-8B-Instruct leads on reach. 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
Which one, for what
Best for Agents that want a documented, signed, Apache 2.0 reasoning model with tool calling on a 16 GB machine
Ahead on
- Agent interface, 85 against 60
- Documentation and upkeep, 92 against 31
Watch for
No safety results published for it
Best for An agent with 8 GB or more to spare that needs a local model to read images, documents and video
Ahead on
- Reach, 100 against 95
Watch for
No safety evaluation, agent-use guidance or security contact
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 8B +61
Granite 4.2 8B 92
Model card 17, Intended use (best for), limitations (ethical considerations and limitations), architecture, parameters (8B), context length (128K native) and 12 tested languages are on the card. Training data 20, IBM's disclosure file for the 8B lists the datasets used in pre-training and post-training as machine-readable metadata, and the card describes the three training stages. Last change 20, the last commit, on 20 October 2026, replaced model.safetensors.index.json (titled "granite-4.2 index file fix"), 21 days before this check. Responsiveness 20, Of the 5 discussions that aren't pull requests (13 September to 5 November 2026), 4 have a reply from an ibm-granite member. Version policy 5, Granite 4.1 8B 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 + 20 + 20 + 5 + 10 = 92.
Qwen3-VL-8B-Instruct 31
The card misses limitations, languages and a cut-off (11). Training data is described only in the technical report, with no cut-off (5). The last change to code that runs it was the qwen-vl-utils fix merged on 4 January 2026, and the weights haven't changed since November 2025; the Hub commits for all three repositories and the QwenLM/Qwen3-VL head (30 January 2026) were the same on 4 November (5). Of 34 Hugging Face discussions on 4 November, the 19 that aren't pull requests (newest 23 August 2026) have no reply from Qwen, and GitHub issues weren't counted (0). Earlier versions kept in their own repositories without notes (5). Two reports wait without a reply (5). 11+5+5+0+5+5 = 31.
Agent interface Granite 4.2 8B +25
Granite 4.2 8B 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.
Qwen3-VL-8B-Instruct 60
A Jinja chat template in the repository (20). The tool-call format lives only in the template, Hermes-style JSON in tool_call tags, and Alibaba names no parser (10). Trained for function calling with several calls allowed in a turn (20). No documented structured output (0). No thinking mode in this edition, so reasoning control scores 0 by the rule. 262,144 tokens of native context (10). 20+10+20+0+0+10 = 60.
Reach Qwen3-VL-8B-Instruct +5
Granite 4.2 8B 95
Memory 30, IBM's own 4-bit MLX build granite-4.2-8b-q4-mlx is 4.95 GB, and 4.95 GB × 1.2 = 5.9 GB, within 8 GB. Its Q4_0 GGUF is 5.06 GB and Ollama's library build granite4.2:8b (5.3 GB) fits 8 GB too. 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, 8b 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. 30 + 25 + 15 + 15 + 10 + 0 = 95.
Qwen3-VL-8B-Instruct 100
Alibaba's own Q4_K_M GGUF is 5.03 GB, so with a fifth added it needs about 6 GB and fits in 16 GB (30). Transformers 4.57.0, vLLM, SGLang and llama.cpp run it in released versions, and Ollama v0.40.14 pins llama.cpp b11963, which has the qwen3vl architecture, so Ollama is credited through its pin; five runtimes reach the 25 cap. Alibaba publishes FP8 and GGUF builds at F16, Q8_0 and Q4_K_M (15). Apache 2.0 with nothing added (15). No gate and no account (10). Featherless is live in the Hub's provider mapping on 4 November, and Parasail served it per the 21 October check, not re-read, so it's hosted too (5). Novita's mapping still shows an error and isn't counted. 30+25+15+15+10+5 = 100.
Score by category
| Category | Weight this run | Granite 4.2 8B | Qwen3-VL-8B-Instruct | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 95 | 100 | Qwen3-VL-8B-Instruct +5 |
| Risk | 20%20 | 55 | 55 | even |
| Agent interface | 10%10 | 85 | 60 | Granite 4.2 8B +25 |
| Documentation and upkeep | 10%10 | 92 | 31 | Granite 4.2 8B +61 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 54.9/100 |
Facts side by side
| Fact | Granite 4.2 8B | Qwen3-VL-8B-Instruct |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | IBM | Alibaba (Qwen team) |
| 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 | no |
| Last release | 2026-10-20 | 2025-11-01 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 2.8/5 (4) |
Verdicts
Granite 4.2 8B
IBM's mid-size reasoning cousin, with the same strengths as the 3B. It runs in every runtime we count from IBM's own builds on a 16 GB machine, 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.
Qwen3-VL-8B-Instruct
Qwen3-VL-8B-Instruct reads screenshots, documents, charts and video, and Alibaba's Q4_K_M GGUF needs about 6 GB. Transformers, vLLM, SGLang, llama.cpp and Ollama run it under plain Apache 2.0. It has no thinking mode, documents tool calls only in the template, and has no published safety work, so add guardrails before it drives a GUI.
Before you call either
Granite 4.2 8B
- Start it with
ollama run granite4.2:8b, orllama-server -hf ibm-granite/granite-4.2-8b-GGUF:Q4_K_M --jinja - 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 - Thinking is on by default. Pass
enable_thinkingfalse to turn it off orlow_efforttrue for short reasoning - The context is 131,072 tokens. The card's 512K extension isn't documented
- Validate JSON output yourself, since structured output isn't documented
- IBM recommends Granite Guardian beside it to flag risky inputs and outputs
Qwen3-VL-8B-Instruct
- Start it with
llama-server -hf Qwen/Qwen3-VL-8B-Instruct-GGUF:Q4_K_M --jinja -c 32768, orollama run qwen3-vl:8b-instruct - In Ollama the plain
qwen3-vl:8btag is the Thinking edition, so name8b-instruct - With the GGUF, load the mmproj vision file too, or images won't be read
- Parse tool calls as JSON inside
<tool_call>tags, or use SGLang's--tool-call-parser qwen - Use the card's sampling,
temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5for vision andtemperature=1.0, top_p=1.0, top_k=40, presence_penalty=2.0for text - Validate JSON output yourself, since structured output isn't documented
- vLLM needs 0.11.0 or later and Transformers 4.57.0 or later
Questions
Which is better for AI agents, Granite 4.2 8B or Qwen3-VL-8B-Instruct?
Granite 4.2 8B scores 61 (C) on agent readiness against Qwen3-VL-8B-Instruct's 54.9 (C), and leads in 2 of 4 scored categories. Qwen3-VL-8B-Instruct leads on reach.
Other comparisons with Granite 4.2 8B or Qwen3-VL-8B-Instruct
- Agents-A1-4B vs Granite 4.2 8B
- Agents-A1-4B vs Qwen3-VL-8B-Instruct
- Apertus v1.5 8B vs Granite 4.2 8B
- Apertus v1.5 8B vs Qwen3-VL-8B-Instruct
- Apodex-1.1-mini vs Granite 4.2 8B
- Apodex-1.1-mini vs Qwen3-VL-8B-Instruct
- Arete-27B vs Granite 4.2 8B
- Arete-27B vs Qwen3-VL-8B-Instruct
- BigBang-v1 vs Granite 4.2 8B
- BigBang-v1 vs Qwen3-VL-8B-Instruct
- Carbon-X vs Granite 4.2 8B
- Carbon-X vs Qwen3-VL-8B-Instruct
- Cosmos-Reason2-2B vs Granite 4.2 8B
- Cosmos-Reason2-2B vs Qwen3-VL-8B-Instruct
- Darwin-36B-Opus vs Granite 4.2 8B
- Darwin-36B-Opus vs Qwen3-VL-8B-Instruct
- DeepSeek-V3.2 vs Granite 4.2 8B
- DeepSeek-V3.2 vs Qwen3-VL-8B-Instruct
- DeepSeek V4.1 Flash vs Granite 4.2 8B
- DeepSeek V4.1 Flash vs Qwen3-VL-8B-Instruct
- DeepSeek-V4-Flash-0731 vs Granite 4.2 8B
- DeepSeek-V4-Flash-0731 vs Qwen3-VL-8B-Instruct
- DiffusionGemma 26B A4B IT vs Granite 4.2 8B
- DiffusionGemma 26B A4B IT vs Qwen3-VL-8B-Instruct
- Gemma 4 12B IT vs Granite 4.2 8B
- Gemma 4 12B IT vs Qwen3-VL-8B-Instruct
- Gemma 4 26B A4B IT vs Granite 4.2 8B
- Gemma 4 26B A4B IT vs Qwen3-VL-8B-Instruct
- Gemma 4 31B IT vs Granite 4.2 8B
- Gemma 4 31B IT vs Qwen3-VL-8B-Instruct
- Gemma 4 E2B IT vs Granite 4.2 8B
- Gemma 4 E2B IT vs Qwen3-VL-8B-Instruct
- Gemma 4 E4B IT vs Granite 4.2 8B
- Gemma 4 E4B IT vs Qwen3-VL-8B-Instruct
- GLM-4.5-Air vs Granite 4.2 8B
- GLM-4.5-Air vs Qwen3-VL-8B-Instruct
- GLM-4.7-Flash vs Granite 4.2 8B
- GLM-4.7-Flash vs Qwen3-VL-8B-Instruct
- GLM-5.3-Flash vs Granite 4.2 8B
- GLM-5.3-Flash vs Qwen3-VL-8B-Instruct
- GLM-5.3 vs Granite 4.2 8B
- GLM-5.3 vs Qwen3-VL-8B-Instruct
- gpt-oss-120b vs Granite 4.2 8B
- gpt-oss-120b vs Qwen3-VL-8B-Instruct
- gpt-oss-20b vs Granite 4.2 8B
- gpt-oss-20b vs Qwen3-VL-8B-Instruct
- Granite 4.2 30B vs Granite 4.2 8B
- Granite 4.2 30B vs Qwen3-VL-8B-Instruct
- Granite 4.2 3B vs Granite 4.2 8B
- Granite 4.2 3B vs Qwen3-VL-8B-Instruct
- Granite 4.2 8B vs Hy-Memory-30B-A3B
- Granite 4.2 8B vs Hy3
- Granite 4.2 8B vs Inkling
- Granite 4.2 8B vs Inkling-Small
- Granite 4.2 8B vs InternVL3.5-GPT-OSS-20B-A4B-Preview
- Granite 4.2 8B vs Kimi K3
- Granite 4.2 8B vs Kimi K3.1 Pro
- Granite 4.2 8B vs Kolibri-1
- Granite 4.2 8B vs Kronos
- Granite 4.2 8B vs Laguna M.1
- Granite 4.2 8B vs Laguna S 2.1
- Granite 4.2 8B vs Laguna XS 2.1
- Granite 4.2 8B vs LBS-27B
- Granite 4.2 8B vs LFM2.5-2.6B
- Granite 4.2 8B vs LFM3-1.7B
- Granite 4.2 8B vs LFM3-Audio-2B
- Granite 4.2 8B vs LFM3-Omni-2B
- Granite 4.2 8B vs LFM3-VL-2B
- Granite 4.2 8B vs Ling-3.0-tiny
- Granite 4.2 8B vs Ling-3.1-flash-1019
- Granite 4.2 8B vs MiMo-V2.6 Distill Qwen 9B
- Granite 4.2 8B vs MiniCPM-o 4.5
- Granite 4.2 8B vs MiniCPM-V 4.7 1B
- Granite 4.2 8B vs MiniCPM-V 4.7 35B-A3B
- Granite 4.2 8B vs MiniCPM5-2B
- Granite 4.2 8B vs MiniMax-M2.5
- Granite 4.2 8B vs MiniMax-M2.7
- Granite 4.2 8B vs MiniMax-M3.1-Flash
- Granite 4.2 8B vs Mistral Large 4
- Granite 4.2 8B vs Muse Glimmer 30B
- Granite 4.2 8B vs Nanbeige4.3-33B
- Granite 4.2 8B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- Granite 4.2 8B vs Nemotron 3.5 Lightning 30B A3B
- Granite 4.2 8B vs Nemotron 3 Nano 30B A3B
- Granite 4.2 8B vs Nemotron 3 Nano 4B
- Granite 4.2 8B vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Granite 4.2 8B vs Nemotron 3 Super 120B A12B
- Granite 4.2 8B vs Nemotron 3 Ultra 550B A55B
- Granite 4.2 8B vs occamy-1.0
- Granite 4.2 8B vs Olmo 3 7B Think
- Granite 4.2 8B vs Ornith-1.5-122B
- Granite 4.2 8B vs Ornith-1.5-35B-A3B
- Granite 4.2 8B vs Ornith-1.5-397B
- Granite 4.2 8B vs Ornith-1.5-9B
- Granite 4.2 8B vs Qwen-AgentWorld-35B-A3B
- Granite 4.2 8B vs Qwen3-30B-A3B-Instruct-2507
- Granite 4.2 8B vs Qwen3-4B-Instruct-2507
- Granite 4.2 8B vs Qwen3.5-0.8B
- Granite 4.2 8B vs Qwen3.5-122B-A10B
- Granite 4.2 8B vs Qwen3.5-2B
- Granite 4.2 8B vs Qwen3.5-397B-A17B
- Granite 4.2 8B vs Qwen3.5-4B
- Granite 4.2 8B vs Qwen3.5-9B
- Granite 4.2 8B vs Qwen3.6-27B
- Granite 4.2 8B vs Qwen3.6-35B-A3B
- Granite 4.2 8B vs Qwen3.8-27B
- Granite 4.2 8B vs Qwen3.8-Flash-Next
- Granite 4.2 8B vs Qwen3-Coder-30B-A3B-Instruct
- Granite 4.2 8B vs Qwen3-Coder-Next
- Granite 4.2 8B vs Qwen3-Omni-30B-A3B-Instruct
- Granite 4.2 8B vs Qwen3-VL-2B-Instruct
- Granite 4.2 8B vs Qwen3-VL-30B-A3B-Instruct
- Granite 4.2 8B vs Qwen3-VL-32B-Instruct
- Granite 4.2 8B vs Qwen3-VL-4B-Instruct
- Granite 4.2 8B vs Qwopus3.8-27B-Flash-V2
- Granite 4.2 8B vs Recursive Flint
- Granite 4.2 8B vs Ripple-65B Preview
- Granite 4.2 8B vs SmolLM3-3B
- Granite 4.2 8B vs Spark-X2.6-4B
- Granite 4.2 8B vs Swift 1.5 Qwen3.8-27B
- Granite 4.2 8B vs Swift 1.5 Qwen3.8-Flash-Next
- Granite 4.2 8B vs ThinkingCap (Qwen3.8 27B)
- Granite 4.2 8B vs Baidu Unlimited-OCR-2
- Hy-Memory-30B-A3B vs Qwen3-VL-8B-Instruct
- Hy3 vs Qwen3-VL-8B-Instruct
- Inkling-Small vs Qwen3-VL-8B-Instruct
- Inkling vs Qwen3-VL-8B-Instruct
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3-VL-8B-Instruct
- Kimi K3.1 Pro vs Qwen3-VL-8B-Instruct
- Kimi K3 vs Qwen3-VL-8B-Instruct
- Kolibri-1 vs Qwen3-VL-8B-Instruct
- Kronos vs Qwen3-VL-8B-Instruct
- Laguna M.1 vs Qwen3-VL-8B-Instruct
- Laguna S 2.1 vs Qwen3-VL-8B-Instruct
- Laguna XS 2.1 vs Qwen3-VL-8B-Instruct
- LBS-27B vs Qwen3-VL-8B-Instruct
- LFM2.5-2.6B vs Qwen3-VL-8B-Instruct
- LFM3-1.7B vs Qwen3-VL-8B-Instruct
- LFM3-Audio-2B vs Qwen3-VL-8B-Instruct
- LFM3-Omni-2B vs Qwen3-VL-8B-Instruct
- LFM3-VL-2B vs Qwen3-VL-8B-Instruct
- Ling-3.0-tiny vs Qwen3-VL-8B-Instruct
- Ling-3.1-flash-1019 vs Qwen3-VL-8B-Instruct
- MiMo-V2.6 Distill Qwen 9B vs Qwen3-VL-8B-Instruct
- MiniCPM-o 4.5 vs Qwen3-VL-8B-Instruct
- MiniCPM-V 4.7 1B vs Qwen3-VL-8B-Instruct
- MiniCPM-V 4.7 35B-A3B vs Qwen3-VL-8B-Instruct
- MiniCPM5-2B vs Qwen3-VL-8B-Instruct
- MiniMax-M2.5 vs Qwen3-VL-8B-Instruct
- MiniMax-M2.7 vs Qwen3-VL-8B-Instruct
- MiniMax-M3.1-Flash vs Qwen3-VL-8B-Instruct
- Mistral Large 4 vs Qwen3-VL-8B-Instruct
- Muse Glimmer 30B vs Qwen3-VL-8B-Instruct
- Nanbeige4.3-33B vs Qwen3-VL-8B-Instruct
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-VL-8B-Instruct
- Nemotron 3.5 Lightning 30B A3B vs Qwen3-VL-8B-Instruct
- Nemotron 3 Nano 30B A3B vs Qwen3-VL-8B-Instruct
- Nemotron 3 Nano 4B vs Qwen3-VL-8B-Instruct
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3-VL-8B-Instruct
- Nemotron 3 Super 120B A12B vs Qwen3-VL-8B-Instruct
- Nemotron 3 Ultra 550B A55B vs Qwen3-VL-8B-Instruct
- occamy-1.0 vs Qwen3-VL-8B-Instruct
- Olmo 3 7B Think vs Qwen3-VL-8B-Instruct
- Ornith-1.5-122B vs Qwen3-VL-8B-Instruct
- Ornith-1.5-35B-A3B vs Qwen3-VL-8B-Instruct
- Ornith-1.5-397B vs Qwen3-VL-8B-Instruct
- Ornith-1.5-9B vs Qwen3-VL-8B-Instruct
- Qwen-AgentWorld-35B-A3B vs Qwen3-VL-8B-Instruct
- Qwen3-30B-A3B-Instruct-2507 vs Qwen3-VL-8B-Instruct
- Qwen3-4B-Instruct-2507 vs Qwen3-VL-8B-Instruct
- Qwen3.5-0.8B vs Qwen3-VL-8B-Instruct
- Qwen3.5-122B-A10B vs Qwen3-VL-8B-Instruct
- Qwen3.5-2B vs Qwen3-VL-8B-Instruct
- Qwen3.5-397B-A17B vs Qwen3-VL-8B-Instruct
- Qwen3.5-4B vs Qwen3-VL-8B-Instruct
- Qwen3.5-9B vs Qwen3-VL-8B-Instruct
- Qwen3.6-27B vs Qwen3-VL-8B-Instruct
- Qwen3.6-35B-A3B vs Qwen3-VL-8B-Instruct
- Qwen3.8-27B vs Qwen3-VL-8B-Instruct
- Qwen3.8-Flash-Next vs Qwen3-VL-8B-Instruct
- Qwen3-Coder-30B-A3B-Instruct vs Qwen3-VL-8B-Instruct
- Qwen3-Coder-Next vs Qwen3-VL-8B-Instruct
- Qwen3-Omni-30B-A3B-Instruct vs Qwen3-VL-8B-Instruct
- Qwen3-VL-2B-Instruct vs Qwen3-VL-8B-Instruct
- Qwen3-VL-30B-A3B-Instruct vs Qwen3-VL-8B-Instruct
- Qwen3-VL-32B-Instruct vs Qwen3-VL-8B-Instruct
- Qwen3-VL-4B-Instruct vs Qwen3-VL-8B-Instruct
- Qwen3-VL-8B-Instruct vs Qwopus3.8-27B-Flash-V2
- Qwen3-VL-8B-Instruct vs Recursive Flint
- Qwen3-VL-8B-Instruct vs Ripple-65B Preview
- Qwen3-VL-8B-Instruct vs SmolLM3-3B
- Qwen3-VL-8B-Instruct vs Spark-X2.6-4B
- Qwen3-VL-8B-Instruct vs Swift 1.5 Qwen3.8-27B
- Qwen3-VL-8B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3-VL-8B-Instruct vs ThinkingCap (Qwen3.8 27B)
- Qwen3-VL-8B-Instruct vs Baidu Unlimited-OCR-2
Machine-readable
- This page as Markdown
/compare/granite-4-2-8b-vs-qwen3-vl-8b-instruct.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/granite-4-2-8b.json·/api/v1/tools/qwen3-vl-8b-instruct.json - From a terminal
anchor compare granite-4-2-8b qwen3-vl-8b-instruct(the CLI) - Over MCP
compare_tools {"a": "granite-4-2-8b", "b": "qwen3-vl-8b-instruct"}at/mcp, no key