# Ling-3.1-flash-1019 vs SmolLM3-3B

> SmolLM3-3B scores 61 (C) to Ling-3.1-flash-1019's 54.8 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-smollm3-3b
- Markdown: https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-smollm3-3b.md (~10,000 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 <slug>.grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.<category>. and <a>.vs.<b>. 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

SmolLM3-3B scores 61 (C) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in every scored category. Both do open-weight models you run yourself.

- Ling-3.1-flash-1019: grade C, 54.8/100, rank #126 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/ling-3-1-flash-1019.md · JSON https://www.anchorterminal.com/api/v1/tools/ling-3-1-flash-1019.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

### Ling-3.1-flash-1019 (C)

Good for: Teams with a multi-GPU server that want an MIT-licensed flagship for long agent runs and coding

Watch for: 574 GB in FP8 with no smaller build from inclusionAI, so no single machine under 690 GB of GPU memory runs 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:
- Reach, 85 against 60
- Risk, 65 against 45
- Agent interface, 80 against 60
- Documentation and upkeep, 74 against 56

Watch for: 65,536 tokens trained; 131,072 needs a YaRN edit to config.json


## Why the edge

### Reach: SmolLM3-3B +25

- Ling-3.1-flash-1019 (60): Memory 0, inclusionAI's only build is the FP8 release at 573.78 GB, × 1.2 = 688.5 GB, above 256 GB, and no runtime's library has a build. Runtimes 25, Released model code in Transformers 5.22.2 (bailing_hybrid), vLLM 0.33.0 (BailingMoeV3ForCausalLM, with ling3 tool and reasoning parsers), SGLang 0.5.23 (bailing_moe_v3.py, its bailing_hybrid config and MTP module) and llama.cpp b12242 (bailingmoe3), and Ollama, credited through its pinned llama.cpp as on the Ling-3.0-tiny listing: v0.40.16 sets LLAMA_CPP_VERSION to b12177. Builds 10, the release is FP8, the one quantised precision inclusionAI publishes for it. Terms 15, MIT in the card's metadata, with no use policy or other terms added on the card or in the repository. Download 10, the Hub API's model info gives gated false on 10 November 2026, so the files download with no account and no terms to accept. Hosted 0, the Hub API's provider mapping is empty for this repository on 10 November 2026. 0 + 25 + 10 + 15 + 10 + 0 = 60.
- 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 +20

- Ling-3.1-flash-1019 (45): File safety 15, safetensors only, no .bin, .pt or .pth files (25), less 10 because the card's documented vLLM command passes --trust-remote-code, which runs the repository's configuration_bailing_moe_v3.py, since vLLM 0.33.0 registers no bailing_hybrid config of its own. Provenance 15, files in the inclusionAI organisation that inclusion-ai.org links to, per the Ling-3.0-tiny listing (10). Safety evaluation 0, no safety results for the model on its card or in the training-content summary. Agent guidance 0, the card has nothing on prompt injection, refusals or safeguards, and inclusionAI publishes no guardrail probe for this model as it did for Ling 3.0. Licence risk 15, MIT with no use policy, no field-of-use limits, no threshold and no clause letting inclusionAI change or revoke it. Security programme 0, www.inclusion-ai.org answers 404 for security.txt on 10 November 2026, and the Ling-3.0-tiny listing found no SECURITY.md or disclosure policy in the inclusionAI .github repository. 15 + 15 + 0 + 0 + 15 + 0 = 45.
- 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: SmolLM3-3B +20

- Ling-3.1-flash-1019 (60): Chat template 20, chat_template.jinja in the repository (6,042 characters), which handles tools, the thinking switch and preserved reasoning. Tool-calling format 10, only the template says how tools are declared (JSON in <tools>) and how calls come back (<tool_call>name with <arg_key> and <arg_value> pairs). Trained tool calling 20, the template tells the model it may call one or more functions and loops over every entry in an assistant message's tool_calls, and the card reports gains on agentic benchmarks, AA-Briefcase and AutomationBench-AA among them. Structured output 0, the card doesn't document JSON or JSON Schema output. Reasoning control 0, the card documents turning thinking on or off per request with chat_template_kwargs enable_thinking, and names the ling3 reasoning parser, but doesn't say where the reasoning text comes back. Context 10, 262,144 tokens natively in config.json (max_position_embeddings 262144, rope_scaling null) and per SGLang's cookbook. 20 + 10 + 20 + 0 + 0 + 10 = 60.
- 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.

### Documentation and upkeep: SmolLM3-3B +18

- Ling-3.1-flash-1019 (56): Model card 11, Intended use (complex reasoning, multi-step tasks and agentic workflows), architecture, parameters (560B total, 25B active) and context (1M claimed) are on the card. Training data 15, the training-content summary the card links names the source categories (Common Crawl and other public datasets, commercially licensed data, the Ant Spider crawler, synthetic data), the size band (over 10 trillion text tokens), data collected up to about July 2026 and the filtering, without listing datasets with sizes. Last change 20, the newest commit, on 4 November 2026, edited the card, six days before this check, and the files went up on 27 October. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (both opened on 27 October 2026) has a reply from an inclusionAI member. Version policy 5, the update went into a repository of its own and Ling-3.1-flash and Ling 3.0 stay in theirs. File health 5, the card states a 1M-token context while config.json gives 262,144 with no rope scaling, and its serve commands name the earlier repository as the model path. 11 + 15 + 20 + 0 + 5 + 5 = 56.
- 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.

## Score by category

| Category | Weight | Ling-3.1-flash-1019 | SmolLM3-3B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 60 | 85 | SmolLM3-3B +25 |
| Risk | 20% (20 this run) | 45 | 65 | SmolLM3-3B +20 |
| Agent interface | 10% (10 this run) | 60 | 80 | SmolLM3-3B +20 |
| Documentation and upkeep | 10% (10 this run) | 56 | 74 | SmolLM3-3B +18 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **54.8 · C** | **61 · C** | |

## Facts side by side

| Fact | Ling-3.1-flash-1019 | SmolLM3-3B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | inclusionAI (Ant Group) | Hugging Face |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-11-04 | 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

### Ling-3.1-flash-1019

No score change since it was listed.

### SmolLM3-3B

- no change on 3 November 2026: product

## Verdicts

**Ling-3.1-flash-1019.** inclusionAI's newest flagship, a 560B hybrid mixture of experts under plain MIT that vLLM, SGLang, Transformers and llama.cpp run in released versions. It ships only as 574 GB of FP8, so it needs a multi-GPU server. The card overstates the context, documents little about tool calls, and has no safety results.

**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

### Ling-3.1-flash-1019

1. Serve it with SGLang 0.5.23 or later on 8 H200 or 4 GB300 GPUs, per the card, and use this repository's name as the model path, not the earlier one the card's command shows
2. With vLLM pass `--trust-remote-code`, `--tool-call-parser ling3` and `--reasoning-parser ling3`
3. Set `chat_template_kwargs` `enable_thinking` false for direct answers. Reasoning comes back in `reasoning_content` per SGLang's cookbook
4. Plan on 262,144 tokens of context. The 1M figure needs the YaRN override in SGLang's cookbook
5. Use temperature 1.0, top_p 0.95 and top_k 20, per the card
6. Validate JSON output yourself, since structured output isn't documented

### 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, Ling-3.1-flash-1019 or SmolLM3-3B?

SmolLM3-3B scores 61 (C) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in every scored category.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-smollm3-3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-smollm3-3b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "ling-3-1-flash-1019", "b": "smollm3-3b"}`. From a terminal: `anchor compare ling-3-1-flash-1019 smollm3-3b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/ling-3-1-flash-1019.json and https://www.anchorterminal.com/api/v1/tools/smollm3-3b.json

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- [SmolLM3-3B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/smollm3-3b-vs-wevisone-vl-4b.md)
- [SmolLM3-3B vs ZAYA1-8B](https://www.anchorterminal.com/compare/smollm3-3b-vs-zaya1-8b.md)

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