Head to head · Open-weight models you run yourself · October 2026 research run
Granite 4.2 30B vs Qwen3-Coder-Next
Granite 4.2 30B scores 61 (C) on agent readiness against Qwen3-Coder-Next's 57.6 (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 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 24 GB card
Ahead on
- Reach, 90 against 85
- Risk, 55 against 50
- Agent interface, 85 against 75
- Documentation and upkeep, 87 against 41
Watch for
No safety results published for it
Best for Agents that want a local coding model for long tool-calling loops on a single 64 GB to 128 GB machine.
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
No LICENSE file anywhere and a broken licence link on the card
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 +46
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.
Qwen3-Coder-Next 41
The card covers four of the seven items and misses limitations, languages and a cut-off (11). The technical report names the data sources, the cut-off and the filtering with some sizes (15). The files last changed on 3 February 2026 (4 February in the GGUF repository), 274 days ago, within a year (5). Alibaba's staff answered none of the 40 discussions; the one reply from a Qwen organisation member is not the publisher's (0), and GitHub issues couldn't be counted. It's a first release (5), and reports of a broken shipped parser and the missing licence wait unanswered (5). 11+15+5+0+5+5 = 41.
Agent interface Granite 4.2 30B +10
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.
Qwen3-Coder-Next 75
The template ships in the repository (20). Tool calling is the model's strength, and Alibaba documents how tools are declared, names and ships the qwen3_coder parser and describes the format in its report (25), with several calls per turn in a model trained on 21 harness formats (20). Context is 262,144 tokens native (10). Structured output scores 0, as nothing documents JSON output, and reasoning control scores 0 because the model has no thinking mode to control.
Reach Granite 4.2 30B +5
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.
Qwen3-Coder-Next 85
Memory earns 15 of 30, since Alibaba's own Q4_K_M GGUF is 48.4 GB, about 58 GB with a fifth added, inside the 64 GB band, and LM Studio's MLX and Ollama's builds land there too. Runtimes reach the 25 cap on released vLLM (v0.33.0), SGLang (v0.5.23), llama.cpp, Ollama (its library entry, and llama.cpp b11963 pinned by Ollama 0.40.14 has qwen3next), LM Studio and Transformers. Alibaba's FP8 and GGUF builds give builds 15. The Apache 2.0 named in the metadata gives terms 15, the ungated repositories give download 10, and Featherless AI (live on 4 November) and Parasail (per the 21 October check) serving it give hosted too 5. 15+25+15+15+10+5 = 85.
Risk Granite 4.2 30B +5
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.
Qwen3-Coder-Next 50
Safetensors and GGUF only, with the two shipped parsers outside the documented loading path, gives file safety 25, and files in the Qwen organisation the publisher's GitHub links to, each hashed on the Hub, give provenance 15. Licence risk loses 5 because the card's licence link is broken and no LICENSE file exists anywhere. Safety evaluation, agent guidance and a security programme score 0, since the report's cybersecurity appendix measures skills rather than safe behaviour, nothing guides agent use and no reporting route was found.
Score by category
| Category | Weight this run | Granite 4.2 30B | Qwen3-Coder-Next | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 90 | 85 | Granite 4.2 30B +5 |
| Risk | 20%20 | 55 | 50 | Granite 4.2 30B +5 |
| Agent interface | 10%10 | 85 | 75 | Granite 4.2 30B +10 |
| Documentation and upkeep | 10%10 | 87 | 41 | Granite 4.2 30B +46 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 57.6/100 |
Facts side by side
| Fact | Granite 4.2 30B | Qwen3-Coder-Next |
|---|---|---|
| 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 | 2026-09-04 | 2026-02-03 |
| 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 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.
Qwen3-Coder-Next
A coding model for agent harnesses with 3B active parameters, trained for tool calling across 21 tool chat templates and supported in released vLLM, SGLang and llama.cpp. Alibaba's 4-bit GGUF needs about 58 GB, fitting one 64 GB machine. Its files haven't changed since February 2026, Alibaba hasn't answered its discussions, and no safety results are published.
Before you call either
Granite 4.2 30B
- Start it with
ollama run granite4.2:30b, orllama-server -hf ibm-granite/granite-4.2-30b-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-Coder-Next
- To start with Ollama, run
ollama run qwen3-coder-next(Q4_K_M, 52 GB of files per the 21 October reading) - On our reference machine Alibaba's Q8_0 GGUF (84.8 GB, about 102 GB in use) also fits, run with
llama-server -m Qwen3-Coder-Next-Q8_0/Qwen3-Coder-Next-Q8_0-00001-of-00004.gguf --jinja -ngl 99 -fa on -c 131072 - Serve with
vllm serve Qwen/Qwen3-Coder-Next --enable-auto-tool-choice --tool-call-parser qwen3_coder(vLLM 0.15.0 or later), using the built-in parser rather than the repository's qwen3coder_tool_parser_vllm.py, which fails to import on newer vLLM per discussion 36 - The card's commands use tensor parallel 2 though the text says 4 GPUs, and it suggests cutting the context to 32,768 if the server fails to start
- There's no thinking mode, so don't pass
enable_thinkingor wait for reasoning_content - Sampling per the card is temperature 1.0, top_p 0.95 and top_k 40
- The GGUF card's llama.cpp example path omits the quant from the file name; the real files are named like Qwen3-Coder-Next-Q4_K_M-00001-of-00004.gguf
- Validate any JSON it returns, since structured output isn't documented
Questions
Which is better for AI agents, Granite 4.2 30B or Qwen3-Coder-Next?
Granite 4.2 30B scores 61 (C) on agent readiness against Qwen3-Coder-Next's 57.6 (C), and leads in every scored category.
Other comparisons with Granite 4.2 30B or Qwen3-Coder-Next
- Agents-A1-4B vs Granite 4.2 30B
- Agents-A1-4B vs Qwen3-Coder-Next
- Apertus v1.5 8B vs Granite 4.2 30B
- Apertus v1.5 8B vs Qwen3-Coder-Next
- Apodex-1.1-mini vs Granite 4.2 30B
- Apodex-1.1-mini vs Qwen3-Coder-Next
- Arete-27B vs Granite 4.2 30B
- Arete-27B vs Qwen3-Coder-Next
- BigBang-v1 vs Granite 4.2 30B
- BigBang-v1 vs Qwen3-Coder-Next
- Carbon-X vs Granite 4.2 30B
- Carbon-X vs Qwen3-Coder-Next
- Cosmos-Reason2-2B vs Granite 4.2 30B
- Cosmos-Reason2-2B vs Qwen3-Coder-Next
- Darwin-36B-Opus vs Granite 4.2 30B
- Darwin-36B-Opus vs Qwen3-Coder-Next
- DeepSeek-V3.2 vs Granite 4.2 30B
- DeepSeek-V3.2 vs Qwen3-Coder-Next
- DeepSeek V4.1 Flash vs Granite 4.2 30B
- DeepSeek V4.1 Flash vs Qwen3-Coder-Next
- DeepSeek-V4-Flash-0731 vs Granite 4.2 30B
- DeepSeek-V4-Flash-0731 vs Qwen3-Coder-Next
- DiffusionGemma 26B A4B IT vs Granite 4.2 30B
- DiffusionGemma 26B A4B IT vs Qwen3-Coder-Next
- Gemma 4 12B IT vs Granite 4.2 30B
- Gemma 4 12B IT vs Qwen3-Coder-Next
- Gemma 4 26B A4B IT vs Granite 4.2 30B
- Gemma 4 26B A4B IT vs Qwen3-Coder-Next
- Gemma 4 31B IT vs Granite 4.2 30B
- Gemma 4 31B IT vs Qwen3-Coder-Next
- Gemma 4 E2B IT vs Granite 4.2 30B
- Gemma 4 E2B IT vs Qwen3-Coder-Next
- Gemma 4 E4B IT vs Granite 4.2 30B
- Gemma 4 E4B IT vs Qwen3-Coder-Next
- GLM-4.5-Air vs Granite 4.2 30B
- GLM-4.5-Air vs Qwen3-Coder-Next
- GLM-4.7-Flash vs Granite 4.2 30B
- GLM-4.7-Flash vs Qwen3-Coder-Next
- GLM-5.3-Flash vs Granite 4.2 30B
- GLM-5.3-Flash vs Qwen3-Coder-Next
- GLM-5.3 vs Granite 4.2 30B
- GLM-5.3 vs Qwen3-Coder-Next
- gpt-oss-120b vs Granite 4.2 30B
- gpt-oss-120b vs Qwen3-Coder-Next
- gpt-oss-20b vs Granite 4.2 30B
- gpt-oss-20b vs Qwen3-Coder-Next
- Granite 4.2 30B vs Granite 4.2 3B
- Granite 4.2 30B vs Granite 4.2 8B
- Granite 4.2 30B vs Hy-Memory-30B-A3B
- Granite 4.2 30B vs Hy3
- Granite 4.2 30B vs Inkling
- Granite 4.2 30B vs Inkling-Small
- Granite 4.2 30B vs InternVL3.5-GPT-OSS-20B-A4B-Preview
- Granite 4.2 30B vs Kimi K3
- Granite 4.2 30B vs Kimi K3.1 Pro
- Granite 4.2 30B vs Kolibri-1
- Granite 4.2 30B vs Kronos
- Granite 4.2 30B vs Laguna M.1
- Granite 4.2 30B vs Laguna S 2.1
- Granite 4.2 30B vs Laguna XS 2.1
- Granite 4.2 30B vs LBS-27B
- Granite 4.2 30B vs LFM2.5-2.6B
- Granite 4.2 30B vs LFM3-1.7B
- Granite 4.2 30B vs LFM3-Audio-2B
- Granite 4.2 30B vs LFM3-Omni-2B
- Granite 4.2 30B vs LFM3-VL-2B
- Granite 4.2 30B vs Ling-3.0-tiny
- Granite 4.2 30B vs Ling-3.1-flash-1019
- Granite 4.2 30B vs MiMo-V2.6 Distill Qwen 9B
- Granite 4.2 30B vs MiniCPM-o 4.5
- Granite 4.2 30B vs MiniCPM-V 4.7 1B
- Granite 4.2 30B vs MiniCPM-V 4.7 35B-A3B
- Granite 4.2 30B vs MiniCPM5-2B
- Granite 4.2 30B vs MiniMax-M2.5
- Granite 4.2 30B vs MiniMax-M2.7
- Granite 4.2 30B vs MiniMax-M3.1-Flash
- Granite 4.2 30B vs Mistral Large 4
- Granite 4.2 30B vs Muse Glimmer 30B
- Granite 4.2 30B vs Nanbeige4.3-33B
- Granite 4.2 30B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- Granite 4.2 30B vs Nemotron 3.5 Lightning 30B A3B
- Granite 4.2 30B vs Nemotron 3 Nano 30B A3B
- Granite 4.2 30B vs Nemotron 3 Nano 4B
- Granite 4.2 30B vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Granite 4.2 30B vs Nemotron 3 Super 120B A12B
- Granite 4.2 30B vs Nemotron 3 Ultra 550B A55B
- Granite 4.2 30B vs occamy-1.0
- Granite 4.2 30B vs Olmo 3 7B Think
- Granite 4.2 30B vs Ornith-1.5-122B
- Granite 4.2 30B vs Ornith-1.5-35B-A3B
- Granite 4.2 30B vs Ornith-1.5-397B
- Granite 4.2 30B vs Ornith-1.5-9B
- Granite 4.2 30B vs Qwen-AgentWorld-35B-A3B
- Granite 4.2 30B vs Qwen3-30B-A3B-Instruct-2507
- Granite 4.2 30B vs Qwen3-4B-Instruct-2507
- Granite 4.2 30B vs Qwen3.5-0.8B
- Granite 4.2 30B vs Qwen3.5-122B-A10B
- Granite 4.2 30B vs Qwen3.5-2B
- Granite 4.2 30B vs Qwen3.5-397B-A17B
- Granite 4.2 30B vs Qwen3.5-4B
- Granite 4.2 30B vs Qwen3.5-9B
- Granite 4.2 30B vs Qwen3.6-27B
- Granite 4.2 30B vs Qwen3.6-35B-A3B
- Granite 4.2 30B vs Qwen3.8-27B
- Granite 4.2 30B vs Qwen3.8-Flash-Next
- Granite 4.2 30B vs Qwen3-Coder-30B-A3B-Instruct
- Granite 4.2 30B vs Qwen3-Omni-30B-A3B-Instruct
- Granite 4.2 30B vs Qwen3-VL-2B-Instruct
- Granite 4.2 30B vs Qwen3-VL-30B-A3B-Instruct
- Granite 4.2 30B vs Qwen3-VL-32B-Instruct
- Granite 4.2 30B vs Qwen3-VL-4B-Instruct
- Granite 4.2 30B vs Qwen3-VL-8B-Instruct
- Granite 4.2 30B vs Qwopus3.8-27B-Flash-V2
- Granite 4.2 30B vs Recursive Flint
- Granite 4.2 30B vs Ripple-65B Preview
- Granite 4.2 30B vs SmolLM3-3B
- Granite 4.2 30B vs Spark-X2.6-4B
- Granite 4.2 30B vs Swift 1.5 Qwen3.8-27B
- Granite 4.2 30B vs Swift 1.5 Qwen3.8-Flash-Next
- Granite 4.2 30B vs ThinkingCap (Qwen3.8 27B)
- Granite 4.2 30B vs Baidu Unlimited-OCR-2
- Granite 4.2 3B vs Qwen3-Coder-Next
- Granite 4.2 8B vs Qwen3-Coder-Next
- Hy-Memory-30B-A3B vs Qwen3-Coder-Next
- Hy3 vs Qwen3-Coder-Next
- Inkling-Small vs Qwen3-Coder-Next
- Inkling vs Qwen3-Coder-Next
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3-Coder-Next
- Kimi K3.1 Pro vs Qwen3-Coder-Next
- Kimi K3 vs Qwen3-Coder-Next
- Kolibri-1 vs Qwen3-Coder-Next
- Kronos vs Qwen3-Coder-Next
- Laguna M.1 vs Qwen3-Coder-Next
- Laguna S 2.1 vs Qwen3-Coder-Next
- Laguna XS 2.1 vs Qwen3-Coder-Next
- LBS-27B vs Qwen3-Coder-Next
- LFM2.5-2.6B vs Qwen3-Coder-Next
- LFM3-1.7B vs Qwen3-Coder-Next
- LFM3-Audio-2B vs Qwen3-Coder-Next
- LFM3-Omni-2B vs Qwen3-Coder-Next
- LFM3-VL-2B vs Qwen3-Coder-Next
- Ling-3.0-tiny vs Qwen3-Coder-Next
- Ling-3.1-flash-1019 vs Qwen3-Coder-Next
- MiMo-V2.6 Distill Qwen 9B vs Qwen3-Coder-Next
- MiniCPM-o 4.5 vs Qwen3-Coder-Next
- MiniCPM-V 4.7 1B vs Qwen3-Coder-Next
- MiniCPM-V 4.7 35B-A3B vs Qwen3-Coder-Next
- MiniCPM5-2B vs Qwen3-Coder-Next
- MiniMax-M2.5 vs Qwen3-Coder-Next
- MiniMax-M2.7 vs Qwen3-Coder-Next
- MiniMax-M3.1-Flash vs Qwen3-Coder-Next
- Mistral Large 4 vs Qwen3-Coder-Next
- Muse Glimmer 30B vs Qwen3-Coder-Next
- Nanbeige4.3-33B vs Qwen3-Coder-Next
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-Coder-Next
- Nemotron 3.5 Lightning 30B A3B vs Qwen3-Coder-Next
- Nemotron 3 Nano 30B A3B vs Qwen3-Coder-Next
- Nemotron 3 Nano 4B vs Qwen3-Coder-Next
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3-Coder-Next
- Nemotron 3 Super 120B A12B vs Qwen3-Coder-Next
- Nemotron 3 Ultra 550B A55B vs Qwen3-Coder-Next
- occamy-1.0 vs Qwen3-Coder-Next
- Olmo 3 7B Think vs Qwen3-Coder-Next
- Ornith-1.5-122B vs Qwen3-Coder-Next
- Ornith-1.5-35B-A3B vs Qwen3-Coder-Next
- Ornith-1.5-397B vs Qwen3-Coder-Next
- Ornith-1.5-9B vs Qwen3-Coder-Next
- Qwen-AgentWorld-35B-A3B vs Qwen3-Coder-Next
- Qwen3-30B-A3B-Instruct-2507 vs Qwen3-Coder-Next
- Qwen3-4B-Instruct-2507 vs Qwen3-Coder-Next
- Qwen3.5-0.8B vs Qwen3-Coder-Next
- Qwen3.5-122B-A10B vs Qwen3-Coder-Next
- Qwen3.5-2B vs Qwen3-Coder-Next
- Qwen3.5-397B-A17B vs Qwen3-Coder-Next
- Qwen3.5-4B vs Qwen3-Coder-Next
- Qwen3.5-9B vs Qwen3-Coder-Next
- Qwen3.6-27B vs Qwen3-Coder-Next
- Qwen3.6-35B-A3B vs Qwen3-Coder-Next
- Qwen3.8-27B vs Qwen3-Coder-Next
- Qwen3.8-Flash-Next vs Qwen3-Coder-Next
- Qwen3-Coder-30B-A3B-Instruct vs Qwen3-Coder-Next
- Qwen3-Coder-Next vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3-Coder-Next vs Qwen3-VL-2B-Instruct
- Qwen3-Coder-Next vs Qwen3-VL-30B-A3B-Instruct
- Qwen3-Coder-Next vs Qwen3-VL-32B-Instruct
- Qwen3-Coder-Next vs Qwen3-VL-4B-Instruct
- Qwen3-Coder-Next vs Qwen3-VL-8B-Instruct
- Qwen3-Coder-Next vs Qwopus3.8-27B-Flash-V2
- Qwen3-Coder-Next vs Recursive Flint
- Qwen3-Coder-Next vs Ripple-65B Preview
- Qwen3-Coder-Next vs SmolLM3-3B
- Qwen3-Coder-Next vs Spark-X2.6-4B
- Qwen3-Coder-Next vs Swift 1.5 Qwen3.8-27B
- Qwen3-Coder-Next vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3-Coder-Next vs ThinkingCap (Qwen3.8 27B)
- Qwen3-Coder-Next vs Baidu Unlimited-OCR-2
Machine-readable
- This page as Markdown
/compare/granite-4-2-30b-vs-qwen3-coder-next.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/granite-4-2-30b.json·/api/v1/tools/qwen3-coder-next.json - From a terminal
anchor compare granite-4-2-30b qwen3-coder-next(the CLI) - Over MCP
compare_tools {"a": "granite-4-2-30b", "b": "qwen3-coder-next"}at/mcp, no key