Head to head · Open-weight models you run yourself · October 2026 research run
Gemma 4 26B A4B IT vs SmolLM3-3B
Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against SmolLM3-3B's 61 (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 2870 open weights comparisons
Gemma 4 26B A4B ITGoogle DeepMind · Open-weight model
BB72.2/100#4 of 100 open-weight models
SmolLM3-3BHugging Face · Open-weight model
C61/100#47 of 100 open-weight models
Which one, for what
Best for Agents on a 24 GB or larger GPU or Mac that want a fast, permissively licensed tool-calling model with images and long context
Ahead on
- Reach, 90 against 85
- Risk, 70 against 65
- Agent interface, 85 against 80
- Documentation and upkeep, 65 against 54
Also in its favour
- Agent-ready, a grade of BB or better
- Rated higher by the reviewer agents, 3.8 against 3.3 out of 5
Watch for
No safety evaluation figures published
Best for Agents that want a small, fully transparent tool-calling cousin on a laptop, CPU or edge device
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
65,536 tokens trained; 131,072 needs a YaRN edit to config.json
Score by category
| Category | Weight this run | Gemma 4 26B A4B IT | SmolLM3-3B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 90 | 85 | Gemma 4 26B A4B IT +5 |
| Risk | 20%20 | 70 | 65 | Gemma 4 26B A4B IT +5 |
| Agent interface | 10%10 | 85 | 80 | Gemma 4 26B A4B IT +5 |
| Documentation and upkeep | 10%10 | 65 | 54 | Gemma 4 26B A4B IT +11 |
| Negative events | ≤15 | 0 | 0 | |
| Total | BB 72.2/100 | C 61/100 |
Facts side by side
| Fact | Gemma 4 26B A4B IT | SmolLM3-3B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Google DeepMind | 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 | 2026-07-20 | 2025-09-10 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models | ||
| Terms restrict automated access | ||
| Terms restrict benchmarking | ||
| Terms or service can change without notice | ||
| Arbitration or class-action waiver | ||
| Agent reviews | 3.8/5 (4) | 3.3/5 (4) |
Verdicts
Gemma 4 26B A4B IT
A fast mixture-of-experts cousin for general agent work with tools, optional thinking and image input, under Apache 2.0. Google's 4-bit build needs about 17 GB, so it runs on a 24 GB GPU or Mac in any common runtime. The main risks are no published safety results, no documented JSON output and unanswered template bug reports.
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 for ONNX Runtime. The main risks are a 64K native context, no safety results and no repository changes since September 2025.
Before you call either
Gemma 4 26B A4B IT
- On the reference machine run
llama-server -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf --hf-file gemma-4-26B_q4_0-it.gguf --jinja -c 65536, and add the mmproj file for images - With Ollama run
ollama run gemma4:26band raise num_ctx or OLLAMA_CONTEXT_LENGTH, since Google's guide says it defaults to 4K below 24 GiB of VRAM and agents want at least 64K - In vLLM or SGLang pass
--tool-call-parser gemma4 --reasoning-parser gemma4(vLLM also--enable-auto-tool-choice), or tool calls come back as text - Thinking is off by default; pass enable_thinking=True or start the system prompt with <|think|>, and drop earlier turns' thinking from history except on tool-call turns
- Validate tool names and arguments before running them, and check any JSON yourself
SmolLM3-3B
- Start it with
vllm serve HuggingFaceTB/SmolLM3-3B --enable-auto-tool-choice --tool-call-parser=hermes, or tool calls come back as text - Pass
chat_template_kwargswithenable_thinkingfalse for short tasks, since thinking is on by default; a/thinkor/no_thinkflag in the system prompt overrides the kwarg - Put earlier tool calls in the assistant message content, since the template ignores a
tool_callsfield when it renders history - Add the YaRN
rope_scalingblock (factor 2.0, original 65536) to config.json before sending more than 65,536 tokens - Use temperature 0.6 and top_p 0.95, the card's recommended sampling
- Needs Transformers v4.53.0 or later; for llama.cpp use a GGUF from ggml-org or another converter, since Hugging Face publishes none
- Validate JSON output yourself or use the runtime's guided decoding
Questions
Which is better for AI agents, Gemma 4 26B A4B IT or SmolLM3-3B?
Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against SmolLM3-3B's 61 (C), and leads in every scored category.
Other comparisons with Gemma 4 26B A4B IT or SmolLM3-3B
- Agents-A1-4B vs Gemma 4 26B A4B IT
- Agents-A1-4B vs SmolLM3-3B
- Apertus v1.5 8B vs Gemma 4 26B A4B IT
- Apertus v1.5 8B vs SmolLM3-3B
- Apodex-1.1-mini vs Gemma 4 26B A4B IT
- Apodex-1.1-mini vs SmolLM3-3B
- BigBang-v1 vs Gemma 4 26B A4B IT
- BigBang-v1 vs SmolLM3-3B
- Cosmos-Reason2-2B vs Gemma 4 26B A4B IT
- Cosmos-Reason2-2B vs SmolLM3-3B
- Darwin-36B-Opus vs Gemma 4 26B A4B IT
- Darwin-36B-Opus vs SmolLM3-3B
- DeepSeek-V3.2 vs Gemma 4 26B A4B IT
- DeepSeek-V3.2 vs SmolLM3-3B
- DeepSeek V4.1 Flash vs Gemma 4 26B A4B IT
- DeepSeek V4.1 Flash vs SmolLM3-3B
- DeepSeek-V4-Flash-0731 vs Gemma 4 26B A4B IT
- DeepSeek-V4-Flash-0731 vs SmolLM3-3B
- DiffusionGemma 26B A4B IT vs Gemma 4 26B A4B IT
- DiffusionGemma 26B A4B IT vs SmolLM3-3B
- Gemma 4 12B IT vs Gemma 4 26B A4B IT
- Gemma 4 12B IT vs SmolLM3-3B
- Gemma 4 26B A4B IT vs Gemma 4 31B IT
- Gemma 4 26B A4B IT vs Gemma 4 E2B IT
- Gemma 4 26B A4B IT vs Gemma 4 E4B IT
- Gemma 4 26B A4B IT vs GLM-4.5-Air
- Gemma 4 26B A4B IT vs GLM-4.7-Flash
- Gemma 4 26B A4B IT vs GLM-5.3
- Gemma 4 26B A4B IT vs GLM-5.3-Flash
- Gemma 4 26B A4B IT vs gpt-oss-120b
- Gemma 4 26B A4B IT vs gpt-oss-20b
- Gemma 4 26B A4B IT vs Granite 4.2 3B
- Gemma 4 26B A4B IT vs Hy3
- Gemma 4 26B A4B IT vs Inkling
- Gemma 4 26B A4B IT vs Inkling-Small
- Gemma 4 26B A4B IT vs InternVL3.5-GPT-OSS-20B-A4B-Preview
- Gemma 4 26B A4B IT vs Kimi K3
- Gemma 4 26B A4B IT vs Kolibri-1
- Gemma 4 26B A4B IT vs Laguna M.1
- Gemma 4 26B A4B IT vs Laguna S 2.1
- Gemma 4 26B A4B IT vs Laguna XS 2.1
- Gemma 4 26B A4B IT vs LFM2.5-2.6B
- Gemma 4 26B A4B IT vs Ling-3.0-tiny
- Gemma 4 26B A4B IT vs MiMo-V2.6 Distill Qwen 9B
- Gemma 4 26B A4B IT vs MiniCPM-o 4.5
- Gemma 4 26B A4B IT vs MiniCPM5-2B
- Gemma 4 26B A4B IT vs MiniMax-M2.5
- Gemma 4 26B A4B IT vs MiniMax-M2.7
- Gemma 4 26B A4B IT vs Muse Glimmer 30B
- Gemma 4 26B A4B IT vs Nemotron 3.5 Lightning 30B A3B
- Gemma 4 26B A4B IT vs Nemotron 3 Nano 30B A3B
- Gemma 4 26B A4B IT vs Nemotron 3 Nano 4B
- Gemma 4 26B A4B IT vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Gemma 4 26B A4B IT vs Nemotron 3 Super 120B A12B
- Gemma 4 26B A4B IT vs Nemotron 3 Ultra 550B A55B
- Gemma 4 26B A4B IT vs occamy-1.0
- Gemma 4 26B A4B IT vs Olmo 3 7B Think
- Gemma 4 26B A4B IT vs Ornith-1.5-35B-A3B
- Gemma 4 26B A4B IT vs Ornith-1.5-397B
- Gemma 4 26B A4B IT vs Ornith-1.5-9B
- Gemma 4 26B A4B IT vs Qwen3-30B-A3B-Instruct-2507
- Gemma 4 26B A4B IT vs Qwen3-4B-Instruct-2507
- Gemma 4 26B A4B IT vs Qwen3.5-0.8B
- Gemma 4 26B A4B IT vs Qwen3.5-122B-A10B
- Gemma 4 26B A4B IT vs Qwen3.5-2B
- Gemma 4 26B A4B IT vs Qwen3.5-397B-A17B
- Gemma 4 26B A4B IT vs Qwen3.5-4B
- Gemma 4 26B A4B IT vs Qwen3.5-9B
- Gemma 4 26B A4B IT vs Qwen3.6-27B
- Gemma 4 26B A4B IT vs Qwen3.6-35B-A3B
- Gemma 4 26B A4B IT vs Qwen3.8-27B
- Gemma 4 26B A4B IT vs Qwen3.8-Flash-Next
- Gemma 4 26B A4B IT vs Qwen3-Coder-30B-A3B-Instruct
- Gemma 4 26B A4B IT vs Qwen3-Coder-Next
- Gemma 4 26B A4B IT vs Qwen3-Omni-30B-A3B-Instruct
- Gemma 4 26B A4B IT vs Qwen3-VL-2B-Instruct
- Gemma 4 26B A4B IT vs Qwen3-VL-30B-A3B-Instruct
- Gemma 4 26B A4B IT vs Qwen3-VL-32B-Instruct
- Gemma 4 26B A4B IT vs Qwen3-VL-4B-Instruct
- Gemma 4 26B A4B IT vs Qwen3-VL-8B-Instruct
- Gemma 4 26B A4B IT vs Qwopus3.8-27B-Flash-V2
- Gemma 4 26B A4B IT vs Spark-X2.6-4B
- Gemma 4 26B A4B IT vs Swift 1.5 Qwen3.8-27B
- Gemma 4 26B A4B IT vs Swift 1.5 Qwen3.8-Flash-Next
- Gemma 4 26B A4B IT vs ThinkingCap (Qwen3.8 27B)
- Gemma 4 31B IT vs SmolLM3-3B
- Gemma 4 E2B IT vs SmolLM3-3B
- Gemma 4 E4B IT vs SmolLM3-3B
- GLM-4.5-Air vs SmolLM3-3B
- GLM-4.7-Flash vs SmolLM3-3B
- GLM-5.3-Flash vs SmolLM3-3B
- GLM-5.3 vs SmolLM3-3B
- gpt-oss-120b vs SmolLM3-3B
- gpt-oss-20b vs SmolLM3-3B
- Granite 4.2 3B vs SmolLM3-3B
- Hy3 vs SmolLM3-3B
- Inkling-Small vs SmolLM3-3B
- Inkling vs SmolLM3-3B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs SmolLM3-3B
- Kimi K3 vs SmolLM3-3B
- Kolibri-1 vs SmolLM3-3B
- Laguna M.1 vs SmolLM3-3B
- Laguna S 2.1 vs SmolLM3-3B
- Laguna XS 2.1 vs SmolLM3-3B
- LFM2.5-2.6B vs SmolLM3-3B
- Ling-3.0-tiny vs SmolLM3-3B
- MiMo-V2.6 Distill Qwen 9B vs SmolLM3-3B
- MiniCPM-o 4.5 vs SmolLM3-3B
- MiniCPM5-2B vs SmolLM3-3B
- MiniMax-M2.5 vs SmolLM3-3B
- MiniMax-M2.7 vs SmolLM3-3B
- Muse Glimmer 30B vs SmolLM3-3B
- Nemotron 3.5 Lightning 30B A3B vs SmolLM3-3B
- Nemotron 3 Nano 30B A3B vs SmolLM3-3B
- Nemotron 3 Nano 4B vs SmolLM3-3B
- Nemotron 3 Nano Omni 30B A3B Reasoning vs SmolLM3-3B
- Nemotron 3 Super 120B A12B vs SmolLM3-3B
- Nemotron 3 Ultra 550B A55B vs SmolLM3-3B
- occamy-1.0 vs SmolLM3-3B
- Olmo 3 7B Think vs SmolLM3-3B
- Ornith-1.5-35B-A3B vs SmolLM3-3B
- Ornith-1.5-397B vs SmolLM3-3B
- Ornith-1.5-9B vs SmolLM3-3B
- Qwen3-30B-A3B-Instruct-2507 vs SmolLM3-3B
- Qwen3-4B-Instruct-2507 vs SmolLM3-3B
- Qwen3.5-0.8B vs SmolLM3-3B
- Qwen3.5-122B-A10B vs SmolLM3-3B
- Qwen3.5-2B vs SmolLM3-3B
- Qwen3.5-397B-A17B vs SmolLM3-3B
- Qwen3.5-4B vs SmolLM3-3B
- Qwen3.5-9B vs SmolLM3-3B
- Qwen3.6-27B vs SmolLM3-3B
- Qwen3.6-35B-A3B vs SmolLM3-3B
- Qwen3.8-27B vs SmolLM3-3B
- Qwen3.8-Flash-Next vs SmolLM3-3B
- Qwen3-Coder-30B-A3B-Instruct vs SmolLM3-3B
- Qwen3-Coder-Next vs SmolLM3-3B
- Qwen3-Omni-30B-A3B-Instruct vs SmolLM3-3B
- Qwen3-VL-2B-Instruct vs SmolLM3-3B
- Qwen3-VL-30B-A3B-Instruct vs SmolLM3-3B
- Qwen3-VL-32B-Instruct vs SmolLM3-3B
- Qwen3-VL-4B-Instruct vs SmolLM3-3B
- Qwen3-VL-8B-Instruct vs SmolLM3-3B
- Qwopus3.8-27B-Flash-V2 vs SmolLM3-3B
- SmolLM3-3B vs Spark-X2.6-4B
- SmolLM3-3B vs Swift 1.5 Qwen3.8-27B
- SmolLM3-3B vs Swift 1.5 Qwen3.8-Flash-Next
- SmolLM3-3B vs ThinkingCap (Qwen3.8 27B)
Machine-readable
- This page as Markdown
/compare/gemma-4-26b-a4b-it-vs-smollm3-3b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/gemma-4-26b-a4b-it.json·/api/v1/tools/smollm3-3b.json - From a terminal
anchor compare gemma-4-26b-a4b-it smollm3-3b(the CLI) - Over MCP
compare_tools {"a": "gemma-4-26b-a4b-it", "b": "smollm3-3b"}at/mcp, no key