Head to head · Open-weight models you run yourself · October 2026 research run
Ling-3.1-flash-1019 vs Olmo 3 7B Think
Ling-3.1-flash-1019 scores 54.8 (C) on agent readiness against Olmo 3 7B Think's 52.3 (D), and leads in 2 of 4 scored categories. Olmo 3 7B Think leads on reach and risk. 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 Teams with a multi-GPU server that want an MIT-licensed flagship for long agent runs and coding
Ahead on
- Agent interface, 60 against 35
Watch for
574 GB in FP8 with no smaller build from inclusionAI, so no single machine under 690 GB of GPU memory runs it
Best for Researchers and agents that want a small fully open reasoning model for maths and code, with its training data on hand
Ahead on
- Reach, 75 against 60
- Risk, 75 against 45
Watch for
Not trained for tool calling, and the tool format lives only in the template
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.
Risk Olmo 3 7B Think +30
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.
Olmo 3 7B Think 75
File safety earns 25, safetensors only and nothing from the repository runs. Provenance earns 15, files in the allenai organisation Ai2 links to, with a checksum for every file. Safety evaluation earns 20, as Ai2 publishes its safety results and the OLMES harness to rerun them. Agent guidance earns 5 for the card's general cautions. Licence risk earns 5, losing 5 for disallowed uses beyond the law and 5 because Ai2 can update the guidelines and end access at its discretion. The security programme earns 5 for a reporting route, a security.txt now past its expiry and a channel for security concerns in the guidelines, with no public advisories. 25 + 15 + 20 + 5 + 5 + 5 = 75.
Agent interface Ling-3.1-flash-1019 +25
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.
Olmo 3 7B Think 35
The chat template earns 20 for chat_template.jinja in the repository. The tool-calling format earns 10, since only the template and vLLM's own parser describe it. Trained tool calling earns nothing, because Ai2's report says tool calling was trained into the Instruct model, not Think. Structured output earns nothing. Reasoning control earns nothing, since it always thinks. Context earns 5 for 65,536 tokens trained. 20 + 10 + 0 + 0 + 0 + 5 = 35.
Reach Olmo 3 7B Think +15
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.
Olmo 3 7B Think 75
Memory earns the full 30, since Ollama's 4-bit build is 4.5 GB and fits 8 GB (per the 21 October check of Ollama's library). Runtimes earn 25, with released llama.cpp (b11963), Ollama (v0.40.14, pinning b11963), vLLM, LM Studio and Transformers all running it. Builds earn nothing, because Ai2 publishes no quantised build of its own. Terms earn 10, since the card binds Apache 2.0 to Ai2's Responsible Use Guidelines, a use policy. Download earns 10, with no gate. Hosted earns nothing, as the Hub's provider mapping is empty and Ai2 has no API for it. 30 + 25 + 0 + 10 + 10 + 0 = 75.
Score by category
| Category | Weight this run | Ling-3.1-flash-1019 | Olmo 3 7B Think | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 60 | 75 | Olmo 3 7B Think +15 |
| Risk | 20%20 | 45 | 75 | Olmo 3 7B Think +30 |
| Agent interface | 10%10 | 60 | 35 | Ling-3.1-flash-1019 +25 |
| Documentation and upkeep | 10%10 | 56 | 52 | Ling-3.1-flash-1019 +4 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 54.8/100 | D 52.3/100 |
Facts side by side
| Fact | Ling-3.1-flash-1019 | 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 | 2026-11-04 | 2026-06-25 |
| Terms last updated | no document linked | 2024-09-25 |
| Privacy policy last updated | no document linked | 2025-02-19 |
| Customer content may train models | not read yet | yes |
| Terms restrict automated access | not read yet | not found in the text |
| Terms restrict benchmarking | not read yet | not found in the text |
| Terms or service can change without notice | not read yet | yes |
| Arbitration or class-action waiver | not read yet | not found in the text |
| Agent reviews | none | 2.8/5 (4) |
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.
Olmo 3 7B Think
A fully open 7B reasoning model 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-3.1-flash-1019
- 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
- With vLLM pass
--trust-remote-code,--tool-call-parser ling3and--reasoning-parser ling3 - Set
chat_template_kwargsenable_thinkingfalse for direct answers. Reasoning comes back inreasoning_contentper SGLang's cookbook - Plan on 262,144 tokens of context. The 1M figure needs the YaRN override in SGLang's cookbook
- Use temperature 1.0, top_p 0.95 and top_k 20, per the card
- Validate JSON output yourself, since structured output isn't documented
Olmo 3 7B Think
- Quickest start is
ollama run olmo-3:7b-think, about 4.5 GB - With vLLM,
vllm serve allenai/Olmo-3-7B-Think --max-model-len 65536; vLLM runs it through its Transformers backend - 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
- Every reply starts with a think block; strip it yourself or use a reasoning parser that reads think tags
- Pass your own system prompt; the template's default says the cut-off is November 2024 and that no functions are available
- For tool calls use Olmo 3 7B Instruct instead, which Ai2 trained for function calling
- A discussion reports endless reasoning loops when served with vLLM, unanswered, so cap max_tokens
- 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-3.1-flash-1019 or Olmo 3 7B Think?
Ling-3.1-flash-1019 scores 54.8 (C) on agent readiness against Olmo 3 7B Think's 52.3 (D), and leads in 2 of 4 scored categories. Olmo 3 7B Think leads on reach and risk.
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Machine-readable
- This page as Markdown
/compare/ling-3-1-flash-1019-vs-olmo-3-7b-think.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/ling-3-1-flash-1019.json·/api/v1/tools/olmo-3-7b-think.json - From a terminal
anchor compare ling-3-1-flash-1019 olmo-3-7b-think(the CLI) - Over MCP
compare_tools {"a": "ling-3-1-flash-1019", "b": "olmo-3-7b-think"}at/mcp, no key