# Ling-3.0-tiny vs Olmo 3 7B Think

> Ling-3.0-tiny scores 55 (C) to Olmo 3 7B Think's 52.3 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-olmo-3-7b-think
- Markdown: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-olmo-3-7b-think.md (~6,200 tokens)
- Slim: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-olmo-3-7b-think.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-olmo-3-7b-think.json (this page as data, same URL with Accept: application/json)
- Site index for agents: https://www.anchorterminal.com/llms.txt (full text: 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)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-10-21

Ling-3.0-tiny scores 55 (C) on agent readiness against Olmo 3 7B Think's 52.3 (D), and leads in 4 of 5 scored categories. Olmo 3 7B Think leads on risk. Both do open-weight models you run yourself.

- Ling-3.0-tiny: grade C, 55/100, rank #81 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/ling-3-0-tiny.md · JSON https://www.anchorterminal.com/api/v1/tools/ling-3-0-tiny.json
- Olmo 3 7B Think: grade D, 52.3/100, rank #86 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/olmo-3-7b-think.md · JSON https://www.anchorterminal.com/api/v1/tools/olmo-3-7b-think.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### Ling-3.0-tiny (C)

Good for: Agents on a laptop or small GPU that want a fast local model for tool calls and short reasoning, under MIT

Ahead on:
- Reach, 90 against 75
- Agent interface, 60 against 35

Watch for: No hosted provider today, and Ollama support is only an unmerged pull request

### Olmo 3 7B Think (D)

Good for: Researchers and agents that want a small fully open reasoning model for maths and code, with its training data on hand

Ahead on:
- Risk, 75 against 50

Watch for: Not trained for tool calling, and the tool format lives only in the template


## Score by category

| Category | Weight | Ling-3.0-tiny | Olmo 3 7B Think | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 31 | 28 | Ling-3.0-tiny +3 |
| Reach | 25% (25 this run) | 90 | 75 | Ling-3.0-tiny +15 |
| Risk | 20% (20 this run) | 50 | 75 | Olmo 3 7B Think +25 |
| Agent interface | 10% (10 this run) | 60 | 35 | Ling-3.0-tiny +25 |
| Documentation and upkeep | 10% (10 this run) | 56 | 52 | Ling-3.0-tiny +4 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **55 · C** | **52.3 · D** | |

## Facts side by side

| Fact | Ling-3.0-tiny | Olmo 3 7B Think |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | inclusionAI (Ant Group) | Ai2 (Allen Institute for AI) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | Apache-2.0, with Ai2's Responsible Use Guidelines named on the card |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-08-31 | 2026-06-25 |
| Terms last updated | no document linked |  |
| Privacy policy last updated | 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 | 2.8/5 (4) | 2.8/5 (4) |

## Verdicts

**Ling-3.0-tiny.** A small, quick hybrid reasoning cousin for local agent work, tool calls and coding, under MIT. It runs in about 6 GB from inclusionAI's 4-bit GGUF in released Transformers, vLLM, SGLang and llama.cpp, with no host. The main risks are a card that doesn't match the files in places, the vLLM route running repository code, and no model safety results.

**Olmo 3 7B Think.** A fully open 7B reasoning cousin for maths, code and step-by-step answers, with every dataset and checkpoint published and its safety results rerunnable. Ollama's 4-bit build needs about 5.4 GB. It always thinks, wasn't trained for tool calls, has 65K of context, and Ai2 hasn't answered its discussions.

## Before you call either

### Ling-3.0-tiny

1. Start it with `llama-server -hf inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M --jinja -c 32768`, which needs llama.cpp b11772 or a recent release with bailingmoe3
2. With vLLM 0.32.0, `vllm serve inclusionAI/Ling-3.0-tiny --trust-remote-code --enable-prefix-caching --mamba-cache-mode align --enable-auto-tool-choice --tool-call-parser ling3 --reasoning-parser ling3`, which runs the repository's config code
3. With SGLang 0.5.22, its cookbook uses `--reasoning-parser deepseek-r1 --tool-call-parser glm45` for this model, and drop the card's NEXTN flags since tiny has no MTP layer
4. Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false for short tasks
5. Sampling per the card is temperature 1.0, top_p 0.95 and top_k 20
6. The template keeps earlier reasoning in the prompt, which grows long agent sessions
7. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented
8. For a guardrail, inclusionAI's Ling-3.0-tiny-singprobe scores intent, unsafe output and hallucination per token, but only through SGLang and vLLM integration branches

### Olmo 3 7B Think

1. Quickest start is `ollama run olmo-3:7b-think`, about 4.5 GB
2. With vLLM, `vllm serve allenai/Olmo-3-7B-Think --max-model-len 65536`; vLLM runs it through its Transformers backend
3. Sample at temperature 0.6 and top_p 0.95 with up to 32,768 output tokens, as the card recommends, since thinking traces are long
4. Every reply starts with a think block; strip it yourself or use a reasoning parser that reads think tags
5. Pass your own system prompt; the template's default says the cut-off is November 2024 and that no functions are available
6. For tool calls use Olmo 3 7B Instruct instead, which Ai2 trained for function calling
7. A discussion reports endless reasoning loops when served with vLLM, unanswered, so cap max_tokens
8. Check the use against Ai2's Responsible Use Guidelines, which bar automated social-media posting and undisclosed automated content outside research

## Questions

### Which is better for AI agents, Ling-3.0-tiny or Olmo 3 7B Think?

Ling-3.0-tiny scores 55 (C) on agent readiness against Olmo 3 7B Think's 52.3 (D), and leads in 4 of 5 scored categories. Olmo 3 7B Think leads on risk.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-olmo-3-7b-think.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-olmo-3-7b-think.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "ling-3-0-tiny", "b": "olmo-3-7b-think"}`. From a terminal: `anchor compare ling-3-0-tiny olmo-3-7b-think`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/ling-3-0-tiny.json and https://www.anchorterminal.com/api/v1/tools/olmo-3-7b-think.json

## Other comparisons with Ling-3.0-tiny or Olmo 3 7B Think

- [Agents-A1-4B vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/agents-a1-4b-vs-ling-3-0-tiny.md)
- [Agents-A1-4B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/agents-a1-4b-vs-olmo-3-7b-think.md)
- [Apertus v1.5 8B vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ling-3-0-tiny.md)
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- [Ling-3.0-tiny vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-thinkingcap-qwen3-8-27b.md)
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- [Olmo 3 7B Think vs SmolLM3-3B](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-smollm3-3b.md)
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- [Olmo 3 7B Think vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-swift-1-5-qwen3-8-27b.md)
- [Olmo 3 7B Think vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-swift-1-5-qwen3-8-flash-next.md)
- [Olmo 3 7B Think vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-thinkingcap-qwen3-8-27b.md)

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