# Granite 4.2 8B vs SmolLM3-3B > Granite 4.2 8B and SmolLM3-3B score within a point of each other for open-weight models you run yourself, 61 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/granite-4-2-8b-vs-smollm3-3b - Markdown: https://www.anchorterminal.com/compare/granite-4-2-8b-vs-smollm3-3b.md (~9,500 tokens) - Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: https://www.anchorterminal.com/llms-full.txt) - API: https://www.anchorterminal.com/api/v1/index.json - MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key) - DNS: dig +short TXT .grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.. and .vs.. under the zone, and the same under grade.letme.dev) - Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data) - Updated: 2026-11-10 Granite 4.2 8B and SmolLM3-3B score within a point of each other on agent readiness, 61 (C) and 61 (C). SmolLM3-3B leads on risk. Both do open-weight models you run yourself. - Granite 4.2 8B: grade C, 61/100, rank #66 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/granite-4-2-8b.md · JSON https://www.anchorterminal.com/api/v1/tools/granite-4-2-8b.json - SmolLM3-3B: grade C, 61/100, rank #55 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/smollm3-3b.md · JSON https://www.anchorterminal.com/api/v1/tools/smollm3-3b.json - Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md - All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md ## Which one, for what ### Granite 4.2 8B (C) Good 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 - Agent interface, 85 against 80 - Documentation and upkeep, 92 against 74 Watch for: No safety results published for it ### SmolLM3-3B (C) Good for: Agents that want a small, fully transparent tool-calling cousin on a laptop, CPU or edge device Ahead on: - Risk, 65 against 55 Watch for: 65,536 tokens trained; 131,072 needs a YaRN edit to config.json ## Why the edge ### Documentation and upkeep: Granite 4.2 8B +18 - 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. - SmolLM3-3B (74): Model card 14, it covers limitations, architecture, parameters, context and languages but has no intended-use section and leaves the June 2025 cut-off to the chat template. Training data 20, the datasets, mixture weights and post-training set are published with sources and sizes. Last change 20, Hugging Face's ONNX build changed its tokenizer config on 28 October 2026, though the main repository hasn't changed since 10 September 2025. Responsiveness 10, staff answered 14 of 36 discussions, none since November 2025. Version policy 5, a first release never replaced, with no dated notes. File health 5, template and tokenizer reports wait with no reply. ### 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. - SmolLM3-3B (85): Memory 30, the smallest 4-bit build Hugging Face publishes is its own ONNX q4f16 at 2.1 GB, about 2.5 GB with the fifth added, and the full BF16 files that the counted runtimes load come to about 7.4 GB, both under 16 GB. Runtimes 20, released Transformers carries the model code, llama.cpp knows the architecture, and vLLM and SGLang run it through their Transformers backends in released versions; Ollama has no library entry and LM Studio's catalogue has none, so those two earn nothing. Builds 10, Hugging Face's ONNX repository has 4-bit builds, one of the checklist's kinds, and int8 builds, which are neither FP8 nor GGUF. They run in ONNX Runtime and Transformers.js rather than the runtimes above. Terms 15, plain Apache 2.0, scored by its terms since the repository holds no LICENSE file. Download 10, not gated. Hosted 0, no inference provider is listed for it on the Hub and none of the provider lists checked carries it. ### Risk: SmolLM3-3B +10 - 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. - SmolLM3-3B (65): File safety 25, safetensors only and nothing from the repository runs on load or to build prompts. Provenance 15, the HuggingFaceTB organisation linked from Hugging Face's launch post, with a SHA-256 for each shard. Safety evaluation 0, none published. Agent guidance 5, only the general cautions in the card's Limitations section. Licence risk 15, Apache 2.0 with nothing added and a working licence link. Security programme 5, huggingface.co has a valid security.txt and Transformers has a security policy; whether advisories are handled in public couldn't be checked from our session, so that half is scored as absent. ### Agent interface: Granite 4.2 8B +5 - 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. - SmolLM3-3B (80): Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card says how tools are declared, how calls come back and which vLLM parser to use. Trained tool calling 20, the template invites one or more calls per turn, each in its own block. Structured output 0, nothing documents asking the weights for JSON. Reasoning control 10, thinking turns off per request through the kwarg or a system-prompt flag, and the reasoning comes back in think tags. Context 5, trained at 65,536 tokens, with 131,072 only through YaRN. ## Score by category | Category | Weight | Granite 4.2 8B | SmolLM3-3B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 95 | 85 | Granite 4.2 8B +10 | | Risk | 20% (20 this run) | 55 | 65 | SmolLM3-3B +10 | | Agent interface | 10% (10 this run) | 85 | 80 | Granite 4.2 8B +5 | | Documentation and upkeep | 10% (10 this run) | 92 | 74 | Granite 4.2 8B +18 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **61 · C** | | ## Facts side by side | Fact | Granite 4.2 8B | SmolLM3-3B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | IBM | Hugging Face | | 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 | 2025-09-10 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | no document linked | | Agent reviews | none | 3.3/5 (4) | ## How the scores have moved ### Granite 4.2 8B No score change since it was listed. ### SmolLM3-3B - no change on 3 November 2026: product ## 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. **SmolLM3-3B.** A tiny, fully open cousin with documented tool calling and switchable thinking, for local subtasks and routing on laptops and CPUs. It needs about 8 GB in BF16, or under 3 GB in Hugging Face's 4-bit ONNX build. The main risks are a 64K native context, no safety results and no staff reply in its discussions since November 2025. ## Before you call either ### Granite 4.2 8B 1. Start it with `ollama run granite4.2:8b`, or `llama-server -hf ibm-granite/granite-4.2-8b-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 ### SmolLM3-3B 1. Start it with `vllm serve HuggingFaceTB/SmolLM3-3B --enable-auto-tool-choice --tool-call-parser=hermes`, or tool calls come back as text 2. Pass `chat_template_kwargs` with `enable_thinking` false for short tasks, since thinking is on by default; a `/think` or `/no_think` flag in the system prompt overrides the kwarg 3. Put earlier tool calls in the assistant message content, since the template ignores a `tool_calls` field when it renders history 4. Add the YaRN `rope_scaling` block (factor 2.0, original 65536) to config.json before sending more than 65,536 tokens 5. Use temperature 0.6 and top_p 0.95, the card's recommended sampling 6. Needs Transformers v4.53.0 or later; for llama.cpp use a GGUF from ggml-org or another converter, since Hugging Face publishes none 7. Validate JSON output yourself or use the runtime's guided decoding ## Questions ### Which is better for AI agents, Granite 4.2 8B or SmolLM3-3B? Granite 4.2 8B and SmolLM3-3B score within a point of each other on agent readiness, 61 (C) and 61 (C). SmolLM3-3B leads on risk. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/granite-4-2-8b-vs-smollm3-3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/granite-4-2-8b-vs-smollm3-3b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "granite-4-2-8b", "b": "smollm3-3b"}`. 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How: https://www.anchorterminal.com/agents/#mcp