Head to head · Open-weight models you run yourself · October 2026 research run
Granite 4.2 8B vs Spark-X2.6-4B
Granite 4.2 8B and Spark-X2.6-4B score within a point of each other on agent readiness, 61 (C) and 61 (C). 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
- Reach, 95 against 85
- Risk, 55 against 50
- Agent interface, 85 against 70
- Documentation and upkeep, 92 against 51
Watch for
No safety results published for it
Best for Agents that want a tiny local model for tool loops and long documents on a laptop, CPU or small GPU.
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
No safety results and no agent or prompt-injection guidance
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 +41
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.
Spark-X2.6-4B 51
Model card 11 of 20, intended use, parameters, context and languages, with no limitations, cut-off or architecture description of its own. Training data 0, the X2.6 card describes none. Last change 20, the card changed on 1 November 2026. Responsiveness 10, SparkLLM members answered 11 of 40 discussions. Version policy 5, X2.5 stays in place with no note. File health 5, the tokenizer config's 131,072 disagrees with the 1M context.
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.
Spark-X2.6-4B 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 spark25 parser name only in the SGLang command. 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, a low or high effort is set in the template and the qwen3 parser returns the reasoning apart. Context 10, 1,048,576 tokens with no scaling.
Reach Granite 4.2 8B +10
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.
Spark-X2.6-4B 85
Memory 30, SparkLLM's Q4_K_M GGUF is 3.19 GB, about 3.8 GB with a fifth added, which fits 8 GB. Runtimes 15 of 25, released llama.cpp, SGLang (to v0.5.23) and Ollama run the spark2_5 architecture X2.6 keeps, while vLLM needs SparkLLM's plugin, Transformers needs the repository's code and LM Studio has no catalogue entry. Builds 15, SparkLLM publishes FP8, INT8, INT4 and GGUF builds. Terms 15, Apache 2.0 with nothing added, on its own base. Download 10, nothing is gated. Hosted 0, no provider or SparkLLM API serves it.
Risk Granite 4.2 8B +5
Granite 4.2 8B 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.
Spark-X2.6-4B 50
File safety 15 of 25, safetensors and GGUF only, but the card's Transformers and vLLM routes run the repository's Python. Provenance 15, the files are in SparkLLM's own Hub organisation, which its GitHub docs name, and every file has a checksum, though neither organisation is verified and no company site was found. Safety evaluation 0 and agent guidance 0, none published. Licence risk 15, plain Apache 2.0 that hasn't changed. Security programme 5 of 10, a private reporting route and email in SparkLLM's community repository, with no public advisories.
Score by category
| Category | Weight this run | Granite 4.2 8B | Spark-X2.6-4B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 95 | 85 | Granite 4.2 8B +10 |
| Risk | 20%20 | 55 | 50 | Granite 4.2 8B +5 |
| Agent interface | 10%10 | 85 | 70 | Granite 4.2 8B +15 |
| Documentation and upkeep | 10%10 | 92 | 51 | Granite 4.2 8B +41 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 61/100 |
Facts side by side
| Fact | Granite 4.2 8B | Spark-X2.6-4B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | IBM | SparkLLM (XHToken) |
| 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 | 2026-10-20 |
| 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.
Spark-X2.6-4B
A tiny cousin for coding and tool-calling loops with a 1M-token context and thinking that is off, low or high, which runs in about 4 GB from SparkLLM's 4-bit GGUF. Use llama.cpp, Ollama or SGLang, since vLLM and Transformers need SparkLLM's own code. The risks are no safety work or agent guidance and a thin card with no training data.
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
Spark-X2.6-4B
- Quickest start is
llama-server -hf XHToken/Spark-X2.6-4B-GGUF:Q4_K_M --jinja, which needs llama.cpp b10828 or later, orollama run SparkLLM/Spark-X2.6-4B(8.2 GB, Ollama 0.34.1 or later) - For parsed tool calls serve it with SGLang 0.5.22 or later,
--tool-call-parser spark25 --reasoning-parser qwen3, since the calls come back as tool_call blocks with arg_key and arg_value pairs - Thinking is on by default. Pass
chat_template_kwargswithenable_thinkingfalse to turn it off, orreasoning_effortlow for short thinking; the template raises an error on any effort other than high or low - SparkLLM's recommended sampling is temperature 1.0, top_p 0.95 and top_k -1
- The template always opens with its own default system line, with the effort instruction, and puts your system message after the tool list
- The template raises an error if tool_call arguments are a JSON string, so pass them as an object
- Set the context yourself, the card's 1,048,576-token example needs far more memory than the weights
Questions
Which is better for AI agents, Granite 4.2 8B or Spark-X2.6-4B?
Granite 4.2 8B and Spark-X2.6-4B score within a point of each other on agent readiness, 61 (C) and 61 (C).
Other comparisons with Granite 4.2 8B or Spark-X2.6-4B
- Agents-A1-4B vs Granite 4.2 8B
- Agents-A1-4B vs Spark-X2.6-4B
- Apertus v1.5 8B vs Granite 4.2 8B
- Apertus v1.5 8B vs Spark-X2.6-4B
- Apodex-1.1-mini vs Granite 4.2 8B
- Apodex-1.1-mini vs Spark-X2.6-4B
- Arete-27B vs Granite 4.2 8B
- Arete-27B vs Spark-X2.6-4B
- BigBang-v1 vs Granite 4.2 8B
- BigBang-v1 vs Spark-X2.6-4B
- Carbon-X vs Granite 4.2 8B
- Carbon-X vs Spark-X2.6-4B
- Cosmos-Reason2-2B vs Granite 4.2 8B
- Cosmos-Reason2-2B vs Spark-X2.6-4B
- Darwin-36B-Opus vs Granite 4.2 8B
- Darwin-36B-Opus vs Spark-X2.6-4B
- DeepSeek-V3.2 vs Granite 4.2 8B
- DeepSeek-V3.2 vs Spark-X2.6-4B
- DeepSeek V4.1 Flash vs Granite 4.2 8B
- DeepSeek V4.1 Flash vs Spark-X2.6-4B
- DeepSeek-V4-Flash-0731 vs Granite 4.2 8B
- DeepSeek-V4-Flash-0731 vs Spark-X2.6-4B
- DiffusionGemma 26B A4B IT vs Granite 4.2 8B
- DiffusionGemma 26B A4B IT vs Spark-X2.6-4B
- Gemma 4 12B IT vs Granite 4.2 8B
- Gemma 4 12B IT vs Spark-X2.6-4B
- Gemma 4 26B A4B IT vs Granite 4.2 8B
- Gemma 4 26B A4B IT vs Spark-X2.6-4B
- Gemma 4 31B IT vs Granite 4.2 8B
- Gemma 4 31B IT vs Spark-X2.6-4B
- Gemma 4 E2B IT vs Granite 4.2 8B
- Gemma 4 E2B IT vs Spark-X2.6-4B
- Gemma 4 E4B IT vs Granite 4.2 8B
- Gemma 4 E4B IT vs Spark-X2.6-4B
- GLM-4.5-Air vs Granite 4.2 8B
- GLM-4.5-Air vs Spark-X2.6-4B
- GLM-4.7-Flash vs Granite 4.2 8B
- GLM-4.7-Flash vs Spark-X2.6-4B
- GLM-5.3-Flash vs Granite 4.2 8B
- GLM-5.3-Flash vs Spark-X2.6-4B
- GLM-5.3 vs Granite 4.2 8B
- GLM-5.3 vs Spark-X2.6-4B
- gpt-oss-120b vs Granite 4.2 8B
- gpt-oss-120b vs Spark-X2.6-4B
- gpt-oss-20b vs Granite 4.2 8B
- gpt-oss-20b vs Spark-X2.6-4B
- Granite 4.2 30B vs Granite 4.2 8B
- Granite 4.2 30B vs Spark-X2.6-4B
- Granite 4.2 3B vs Granite 4.2 8B
- Granite 4.2 3B vs Spark-X2.6-4B
- 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 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 SmolLM3-3B
- 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 Spark-X2.6-4B
- Hy3 vs Spark-X2.6-4B
- Inkling-Small vs Spark-X2.6-4B
- Inkling vs Spark-X2.6-4B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Spark-X2.6-4B
- Kimi K3.1 Pro vs Spark-X2.6-4B
- Kimi K3 vs Spark-X2.6-4B
- Kolibri-1 vs Spark-X2.6-4B
- Kronos vs Spark-X2.6-4B
- Laguna M.1 vs Spark-X2.6-4B
- Laguna S 2.1 vs Spark-X2.6-4B
- Laguna XS 2.1 vs Spark-X2.6-4B
- LBS-27B vs Spark-X2.6-4B
- LFM2.5-2.6B vs Spark-X2.6-4B
- LFM3-1.7B vs Spark-X2.6-4B
- LFM3-Audio-2B vs Spark-X2.6-4B
- LFM3-Omni-2B vs Spark-X2.6-4B
- LFM3-VL-2B vs Spark-X2.6-4B
- Ling-3.0-tiny vs Spark-X2.6-4B
- Ling-3.1-flash-1019 vs Spark-X2.6-4B
- MiMo-V2.6 Distill Qwen 9B vs Spark-X2.6-4B
- MiniCPM-o 4.5 vs Spark-X2.6-4B
- MiniCPM-V 4.7 1B vs Spark-X2.6-4B
- MiniCPM-V 4.7 35B-A3B vs Spark-X2.6-4B
- MiniCPM5-2B vs Spark-X2.6-4B
- MiniMax-M2.5 vs Spark-X2.6-4B
- MiniMax-M2.7 vs Spark-X2.6-4B
- MiniMax-M3.1-Flash vs Spark-X2.6-4B
- Mistral Large 4 vs Spark-X2.6-4B
- Muse Glimmer 30B vs Spark-X2.6-4B
- Nanbeige4.3-33B vs Spark-X2.6-4B
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Spark-X2.6-4B
- Nemotron 3.5 Lightning 30B A3B vs Spark-X2.6-4B
- Nemotron 3 Nano 30B A3B vs Spark-X2.6-4B
- Nemotron 3 Nano 4B vs Spark-X2.6-4B
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Spark-X2.6-4B
- Nemotron 3 Super 120B A12B vs Spark-X2.6-4B
- Nemotron 3 Ultra 550B A55B vs Spark-X2.6-4B
- occamy-1.0 vs Spark-X2.6-4B
- Olmo 3 7B Think vs Spark-X2.6-4B
- Ornith-1.5-122B vs Spark-X2.6-4B
- Ornith-1.5-35B-A3B vs Spark-X2.6-4B
- Ornith-1.5-397B vs Spark-X2.6-4B
- Ornith-1.5-9B vs Spark-X2.6-4B
- Qwen-AgentWorld-35B-A3B vs Spark-X2.6-4B
- Qwen3-30B-A3B-Instruct-2507 vs Spark-X2.6-4B
- Qwen3-4B-Instruct-2507 vs Spark-X2.6-4B
- Qwen3.5-0.8B vs Spark-X2.6-4B
- Qwen3.5-122B-A10B vs Spark-X2.6-4B
- Qwen3.5-2B vs Spark-X2.6-4B
- Qwen3.5-397B-A17B vs Spark-X2.6-4B
- Qwen3.5-4B vs Spark-X2.6-4B
- Qwen3.5-9B vs Spark-X2.6-4B
- Qwen3.6-27B vs Spark-X2.6-4B
- Qwen3.6-35B-A3B vs Spark-X2.6-4B
- Qwen3.8-27B vs Spark-X2.6-4B
- Qwen3.8-Flash-Next vs Spark-X2.6-4B
- Qwen3-Coder-30B-A3B-Instruct vs Spark-X2.6-4B
- Qwen3-Coder-Next vs Spark-X2.6-4B
- Qwen3-Omni-30B-A3B-Instruct vs Spark-X2.6-4B
- Qwen3-VL-2B-Instruct vs Spark-X2.6-4B
- Qwen3-VL-30B-A3B-Instruct vs Spark-X2.6-4B
- Qwen3-VL-32B-Instruct vs Spark-X2.6-4B
- Qwen3-VL-4B-Instruct vs Spark-X2.6-4B
- Qwen3-VL-8B-Instruct vs Spark-X2.6-4B
- Qwopus3.8-27B-Flash-V2 vs Spark-X2.6-4B
- Recursive Flint vs Spark-X2.6-4B
- Ripple-65B Preview vs Spark-X2.6-4B
- SmolLM3-3B vs Spark-X2.6-4B
- Spark-X2.6-4B vs Swift 1.5 Qwen3.8-27B
- Spark-X2.6-4B vs Swift 1.5 Qwen3.8-Flash-Next
- Spark-X2.6-4B vs ThinkingCap (Qwen3.8 27B)
- Spark-X2.6-4B vs Baidu Unlimited-OCR-2
Machine-readable
- This page as Markdown
/compare/granite-4-2-8b-vs-spark-x2-6-4b.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/spark-x2-6-4b.json - From a terminal
anchor compare granite-4-2-8b spark-x2-6-4b(the CLI) - Over MCP
compare_tools {"a": "granite-4-2-8b", "b": "spark-x2-6-4b"}at/mcp, no key