Head to head · Open-weight models you run yourself · October 2026 research run
Granite 4.2 8B vs Qwen3.8-27B
Qwen3.8-27B scores 68.6 (B) on agent readiness against Granite 4.2 8B's 61 (C), and leads in 0 of 4 scored categories. Granite 4.2 8B leads on reach, 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 6359 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
- Reach, 95 against 90
- Agent interface, 85 against 70
- Documentation and upkeep, 92 against 36
Watch for
No safety results published for it
Best for Agents that want a capable open model with vision on one 24 to 32 GB GPU or Mac, with the widest runtime support and a plain licence.
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
No safety results, prompt-injection 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 +56
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.
Qwen3.8-27B 36
Model card 11 of 20, intended use, architecture, parameters and context are there, but limitations, languages and a knowledge cut-off aren't. Training data 0, not described. Last change 15, the files last changed on 14 August 2026, 79 days before this check, and Qwen's GitHub repository for the model holds only a README and licence, so no reference code of Alibaba's changed later. Responsiveness 0, Qwen answered none of the 40 most recent questions and reports, recounted on 1 November. Version policy 5, earlier 27B models stay in their own repositories without dated notes. File health 5, the files agree with the card but reports about the template and structured output wait without a reply.
Agent interface Granite 4.2 8B +15
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.
Qwen3.8-27B 70
Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, the format lives only in the template and the parser name only in serving commands, Alibaba doesn't describe how tools are declared or returned. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented. Reasoning control 10, thinking turns off per request, reasoning_effort budgets it and the card shows the reasoning coming back in reasoning_content. Context 10, 262,144 tokens native.
Reach Granite 4.2 8B +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.
Qwen3.8-27B 90
Memory 25 of 30, Ollama's smallest 4-bit build is 18 GB, or 21.6 GB with a fifth added, which fits 24 GB. Runtimes 25, released versions of llama.cpp, Ollama, vLLM, SGLang and Transformers all run the qwen3_5 architecture. Builds 10 of 15, Alibaba's only quantised build is FP8. Terms 15, Apache 2.0 with nothing added. Download 10, neither repository is gated. Hosted 5, seven inference providers are live for it on Hugging Face on 1 November, though Qwen Cloud's own version is still described as coming soon.
Score by category
| Category | Weight this run | Granite 4.2 8B | Qwen3.8-27B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 95 | 90 | Granite 4.2 8B +5 |
| Risk | 20%20 | 55 | 55 | even |
| Agent interface | 10%10 | 85 | 70 | Granite 4.2 8B +15 |
| Documentation and upkeep | 10%10 | 92 | 36 | Granite 4.2 8B +56 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | B 68.6/100 |
Facts side by side
| Fact | Granite 4.2 8B | Qwen3.8-27B |
|---|---|---|
| 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 | 2026-08-14 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 3/5 (4) |
How the scores have moved
Granite 4.2 8B
No score change since it was listed.
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.
Qwen3.8-27B
A dense cousin for coding and agent work with image and video input that runs almost anywhere. Ollama's 4-bit build needs about 22 GB, and seven providers host it for agents without the hardware. The main risk is that nobody at Alibaba answers, there are no safety results or agent guidance, and the tool format is only in the template.
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
Qwen3.8-27B
- Quickest start is
ollama run qwen3.8, about 18 GB of files - On our reference machine use BF16 with
vllm serve Qwen/Qwen3.8-27B --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder, since FP8 on gfx1151 isn't documented - Use the qwen3_coder tool parser, SGLang's cookbook says the Hermes parser leaves its calls unparsed
- Thinking is on by default, pass
chat_template_kwargswithenable_thinkingfalse for short tasks, and read the reasoning fromreasoning_content reasoning_efforttakes xhigh (default), medium or low, and any other value makes the template raise an error- Sampling per the card is temperature 1.0 and top_p 0.95 when thinking, temperature 0.7, top_p 0.8 and presence_penalty 1.5 without
- Preserved thinking keeps earlier reasoning in the context unless
preserve_thinkingis false, which grows long agent sessions - Turn on YaRN only beyond 262,144 tokens, the card warns static YaRN can cost quality on short texts
- Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented
Questions
Which is better for AI agents, Granite 4.2 8B or Qwen3.8-27B?
Qwen3.8-27B scores 68.6 (B) on agent readiness against Granite 4.2 8B's 61 (C), and leads in 0 of 4 scored categories. Granite 4.2 8B leads on reach, agent interface and documentation and upkeep.
Other comparisons with Granite 4.2 8B or Qwen3.8-27B
- Agents-A1-4B vs Granite 4.2 8B
- Agents-A1-4B vs Qwen3.8-27B
- Apertus v1.5 8B vs Granite 4.2 8B
- Apertus v1.5 8B vs Qwen3.8-27B
- Apodex-1.1-mini vs Granite 4.2 8B
- Apodex-1.1-mini vs Qwen3.8-27B
- Arete-27B vs Granite 4.2 8B
- Arete-27B vs Qwen3.8-27B
- BigBang-v1 vs Granite 4.2 8B
- BigBang-v1 vs Qwen3.8-27B
- Carbon-X vs Granite 4.2 8B
- Carbon-X vs Qwen3.8-27B
- Cola2 vs Granite 4.2 8B
- Cola2 vs Qwen3.8-27B
- Cosmos-Reason2-2B vs Granite 4.2 8B
- Cosmos-Reason2-2B vs Qwen3.8-27B
- Darwin-36B-Opus vs Granite 4.2 8B
- Darwin-36B-Opus vs Qwen3.8-27B
- DeepSeek-V3.2 vs Granite 4.2 8B
- DeepSeek-V3.2 vs Qwen3.8-27B
- DeepSeek V4.1 Flash vs Granite 4.2 8B
- DeepSeek V4.1 Flash vs Qwen3.8-27B
- DeepSeek-V4-Flash-0731 vs Granite 4.2 8B
- DeepSeek-V4-Flash-0731 vs Qwen3.8-27B
- Devstral Small 2 vs Granite 4.2 8B
- Devstral Small 2 vs Qwen3.8-27B
- DiffusionGemma 26B A4B IT vs Granite 4.2 8B
- DiffusionGemma 26B A4B IT vs Qwen3.8-27B
- EXAONE 4.5 33B vs Granite 4.2 8B
- EXAONE 4.5 33B vs Qwen3.8-27B
- Gemma 4 12B IT vs Granite 4.2 8B
- Gemma 4 12B IT vs Qwen3.8-27B
- Gemma 4 26B A4B IT vs Granite 4.2 8B
- Gemma 4 26B A4B IT vs Qwen3.8-27B
- Gemma 4 31B IT vs Granite 4.2 8B
- Gemma 4 31B IT vs Qwen3.8-27B
- Gemma 4 E2B IT vs Granite 4.2 8B
- Gemma 4 E2B IT vs Qwen3.8-27B
- Gemma 4 E4B IT vs Granite 4.2 8B
- Gemma 4 E4B IT vs Qwen3.8-27B
- GLM-4.5-Air vs Granite 4.2 8B
- GLM-4.5-Air vs Qwen3.8-27B
- GLM-4.7-Flash vs Granite 4.2 8B
- GLM-4.7-Flash vs Qwen3.8-27B
- GLM-5.3-Flash vs Granite 4.2 8B
- GLM-5.3-Flash vs Qwen3.8-27B
- GLM-5.3 vs Granite 4.2 8B
- GLM-5.3 vs Qwen3.8-27B
- gpt-oss-120b vs Granite 4.2 8B
- gpt-oss-120b vs Qwen3.8-27B
- gpt-oss-20b vs Granite 4.2 8B
- gpt-oss-20b vs Qwen3.8-27B
- Granite 4.2 30B vs Granite 4.2 8B
- Granite 4.2 30B vs Qwen3.8-27B
- Granite 4.2 3B vs Granite 4.2 8B
- Granite 4.2 3B vs Qwen3.8-27B
- 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 LongCat-2.5
- Granite 4.2 8B vs MiMo-V2.6 Distill Qwen 9B
- Granite 4.2 8B vs MiMo-V2.6-Flash-RL
- 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 Nanbeige4.2-3B
- 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 North Mini Code 1.0
- 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 OTel-2.0-LLM-31B-IT
- 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-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 Qwen3-VL-8B-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 SenseNova-Loop-9B-Preview
- Granite 4.2 8B vs SmolLM3-3B
- Granite 4.2 8B vs Spark-X2.6-4B
- Granite 4.2 8B vs Step 3.7 Flash
- 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
- Granite 4.2 8B vs WeVisOne-VL-2B
- Granite 4.2 8B vs WeVisOne-VL-4B
- Granite 4.2 8B vs ZAYA1-8B
- Hy-Memory-30B-A3B vs Qwen3.8-27B
- Hy3 vs Qwen3.8-27B
- Inkling-Small vs Qwen3.8-27B
- Inkling vs Qwen3.8-27B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3.8-27B
- Kimi K3.1 Pro vs Qwen3.8-27B
- Kimi K3 vs Qwen3.8-27B
- Kolibri-1 vs Qwen3.8-27B
- Kronos vs Qwen3.8-27B
- Laguna M.1 vs Qwen3.8-27B
- Laguna S 2.1 vs Qwen3.8-27B
- Laguna XS 2.1 vs Qwen3.8-27B
- LBS-27B vs Qwen3.8-27B
- LFM2.5-2.6B vs Qwen3.8-27B
- LFM3-1.7B vs Qwen3.8-27B
- LFM3-Audio-2B vs Qwen3.8-27B
- LFM3-Omni-2B vs Qwen3.8-27B
- LFM3-VL-2B vs Qwen3.8-27B
- Ling-3.0-tiny vs Qwen3.8-27B
- Ling-3.1-flash-1019 vs Qwen3.8-27B
- LongCat-2.5 vs Qwen3.8-27B
- MiMo-V2.6 Distill Qwen 9B vs Qwen3.8-27B
- MiMo-V2.6-Flash-RL vs Qwen3.8-27B
- MiniCPM-o 4.5 vs Qwen3.8-27B
- MiniCPM-V 4.7 1B vs Qwen3.8-27B
- MiniCPM-V 4.7 35B-A3B vs Qwen3.8-27B
- MiniCPM5-2B vs Qwen3.8-27B
- MiniMax-M2.5 vs Qwen3.8-27B
- MiniMax-M2.7 vs Qwen3.8-27B
- MiniMax-M3.1-Flash vs Qwen3.8-27B
- Mistral Large 4 vs Qwen3.8-27B
- Muse Glimmer 30B vs Qwen3.8-27B
- Nanbeige4.2-3B vs Qwen3.8-27B
- Nanbeige4.3-33B vs Qwen3.8-27B
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.8-27B
- Nemotron 3.5 Lightning 30B A3B vs Qwen3.8-27B
- Nemotron 3 Nano 30B A3B vs Qwen3.8-27B
- Nemotron 3 Nano 4B vs Qwen3.8-27B
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3.8-27B
- Nemotron 3 Super 120B A12B vs Qwen3.8-27B
- Nemotron 3 Ultra 550B A55B vs Qwen3.8-27B
- North Mini Code 1.0 vs Qwen3.8-27B
- occamy-1.0 vs Qwen3.8-27B
- Olmo 3 7B Think vs Qwen3.8-27B
- Ornith-1.5-122B vs Qwen3.8-27B
- Ornith-1.5-35B-A3B vs Qwen3.8-27B
- Ornith-1.5-397B vs Qwen3.8-27B
- Ornith-1.5-9B vs Qwen3.8-27B
- OTel-2.0-LLM-31B-IT vs Qwen3.8-27B
- Qwen-AgentWorld-35B-A3B vs Qwen3.8-27B
- Qwen3-30B-A3B-Instruct-2507 vs Qwen3.8-27B
- Qwen3-4B-Instruct-2507 vs Qwen3.8-27B
- Qwen3.5-0.8B vs Qwen3.8-27B
- Qwen3.5-122B-A10B vs Qwen3.8-27B
- Qwen3.5-2B vs Qwen3.8-27B
- Qwen3.5-397B-A17B vs Qwen3.8-27B
- Qwen3.5-4B vs Qwen3.8-27B
- Qwen3.5-9B vs Qwen3.8-27B
- Qwen3.6-27B vs Qwen3.8-27B
- Qwen3.6-35B-A3B vs Qwen3.8-27B
- Qwen3.8-27B vs Qwen3.8-Flash-Next
- Qwen3.8-27B vs Qwen3-Coder-30B-A3B-Instruct
- Qwen3.8-27B vs Qwen3-Coder-Next
- Qwen3.8-27B vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3.8-27B vs Qwen3-VL-2B-Instruct
- Qwen3.8-27B vs Qwen3-VL-30B-A3B-Instruct
- Qwen3.8-27B vs Qwen3-VL-32B-Instruct
- Qwen3.8-27B vs Qwen3-VL-4B-Instruct
- Qwen3.8-27B vs Qwen3-VL-8B-Instruct
- Qwen3.8-27B vs Qwopus3.8-27B-Flash-V2
- Qwen3.8-27B vs Recursive Flint
- Qwen3.8-27B vs Ripple-65B Preview
- Qwen3.8-27B vs SenseNova-Loop-9B-Preview
- Qwen3.8-27B vs SmolLM3-3B
- Qwen3.8-27B vs Spark-X2.6-4B
- Qwen3.8-27B vs Step 3.7 Flash
- Qwen3.8-27B vs Swift 1.5 Qwen3.8-27B
- Qwen3.8-27B vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3.8-27B vs ThinkingCap (Qwen3.8 27B)
- Qwen3.8-27B vs Baidu Unlimited-OCR-2
- Qwen3.8-27B vs WeVisOne-VL-2B
- Qwen3.8-27B vs WeVisOne-VL-4B
- Qwen3.8-27B vs ZAYA1-8B
Machine-readable
- This page as Markdown
/compare/granite-4-2-8b-vs-qwen3-8-27b.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/qwen3-8-27b.json - From a terminal
anchor compare granite-4-2-8b qwen3-8-27b(the CLI) - Over MCP
compare_tools {"a": "granite-4-2-8b", "b": "qwen3-8-27b"}at/mcp, no key