Head to head · Open-weight models you run yourself · October 2026 research run
InternVL3.5-GPT-OSS-20B-A4B-Preview vs Ling-3.1-flash-1019
Ling-3.1-flash-1019 scores 54.8 (C) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 53.2 (D), and leads in 2 of 4 scored categories. InternVL3.5-GPT-OSS-20B-A4B-Preview 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 6359 open weights comparisons
Which one, for what
InternVL3.5-GPT-OSS-20B-A4B-Preview D
Best for Agents with 64 GB to spare that want an Apache-licensed image and video reader with optional thinking and can live with a stale preview.
Ahead on
- Reach, 65 against 60
- Risk, 55 against 45
Watch for
No quantised build from OpenGVLab, so about 51 GB at BF16
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 45
- Documentation and upkeep, 56 against 28
Watch for
574 GB in FP8 with no smaller build from inclusionAI, so no single machine under 690 GB of GPU memory runs it
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.
Documentation and upkeep Ling-3.1-flash-1019 +28
InternVL3.5-GPT-OSS-20B-A4B-Preview 28
Model card 8 of 20, the family card misses limitations, context, languages and a cut-off. Training data 5, described without a cut-off. Last change 0, the last commit to either repository is 8 September 2025 and to OpenGVLab's reference code 22 September 2025, over a year before 4 November 2026. Responsiveness 10, OpenGVLab answered 3 of 8 questions across the two repositories, none since September 2025. Version policy 5, a first release. File health 0, the template uses markers the tokenizer doesn't have and contradicts the twin's template, and the card names the wrong language model in its licence line.
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.
Agent interface Ling-3.1-flash-1019 +15
InternVL3.5-GPT-OSS-20B-A4B-Preview 45
Chat template 20, a Jinja file in the repository, though it doesn't match the tokenizer. Tool-calling format 10 of 25, only the twin's inherited harmony template mentions tools. Trained tool calling 0, no evidence it was trained to call tools. Structured output 0. Reasoning control 10, a system prompt turns thinking on and the card says the reasoning comes back in think tags. Context 5, trained at 32K tokens.
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.
Risk InternVL3.5-GPT-OSS-20B-A4B-Preview +10
InternVL3.5-GPT-OSS-20B-A4B-Preview 55
File safety 25, safetensors only and no code in the repository. Provenance 15, the files are in the OpenGVLab organisation its GitHub links, with a checksum on every file. Safety evaluation 0 and agent guidance 0, nothing published. Licence risk 15, Apache 2.0 over Apache and MIT bases, with gpt-oss's usage policy asking only for lawful use. Security programme 0, no policy, security.txt or advisories.
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.
Reach InternVL3.5-GPT-OSS-20B-A4B-Preview +5
InternVL3.5-GPT-OSS-20B-A4B-Preview 65
Memory 15 of 30, the only builds are BF16 at 42.5 GB by the repository's safetensors index, or 51 GB with a fifth added, which fits 64 GB. Runtimes 25, Transformers and vLLM load this repository, and SGLang, llama.cpp and Ollama (through llama.cpp b11963, which Ollama v0.40.14 pins) run the same weights in their GitHub format. Builds 0, OpenGVLab publishes no quantised build. Terms 15, Apache 2.0 with nothing added by the bases. Download 10, not gated. Hosted 0, no provider serves it.
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.
Score by category
| Category | Weight this run | InternVL3.5-GPT-OSS-20B-A4B-Preview | Ling-3.1-flash-1019 | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 65 | 60 | InternVL3.5-GPT-OSS-20B-A4B-Preview +5 |
| Risk | 20%20 | 55 | 45 | InternVL3.5-GPT-OSS-20B-A4B-Preview +10 |
| Agent interface | 10%10 | 45 | 60 | Ling-3.1-flash-1019 +15 |
| Documentation and upkeep | 10%10 | 28 | 56 | Ling-3.1-flash-1019 +28 |
| Negative events | ≤15 | 0 | 0 | |
| Total | D 53.2/100 | C 54.8/100 |
Facts side by side
| Fact | InternVL3.5-GPT-OSS-20B-A4B-Preview | Ling-3.1-flash-1019 |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | OpenGVLab (Shanghai AI Laboratory) | inclusionAI (Ant Group) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0; a merge of openai/gpt-oss-20b (Apache 2.0 with OpenAI's usage policy) and OpenGVLab/InternViT-300M-448px-V2_5 (MIT) | MIT |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2025-09-08 | 2026-11-04 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 1.8/5 (4) | none |
How the scores have moved
Ling-3.1-flash-1019
No score change since it was listed.
Verdicts
InternVL3.5-GPT-OSS-20B-A4B-Preview
An image and video reader on gpt-oss-20b with an optional thinking mode, a preview untouched for over a year. It needs about 51 GB in BF16, since OpenGVLab publishes no quantised build, and runs in Transformers and vLLM. The main risk is a chat template in the Transformers-format repository that doesn't match its tokenizer, with no safety results.
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.
Before you call either
InternVL3.5-GPT-OSS-20B-A4B-Preview
- Serve with
vllm serve OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF --max-model-len 32768, which is our command; the card gives none for vLLM - Check the rendered prompt before relying on it, since the shipped template's <|im_start|> markers aren't tokens in the gpt-oss tokenizer; the GitHub-format repository's harmony template is the alternative
- Use Transformers 4.55.0 or later, as the card requires for this size
- For thinking, send the card's thinking system prompt and parse the reasoning out of the <think> tags, with temperature 0.6 and sampling on as the card advises
- Keep inputs within 32K tokens, the length it was trained at, whatever the config says
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
Questions
Which is better for AI agents, InternVL3.5-GPT-OSS-20B-A4B-Preview or Ling-3.1-flash-1019?
Ling-3.1-flash-1019 scores 54.8 (C) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 53.2 (D), and leads in 2 of 4 scored categories. InternVL3.5-GPT-OSS-20B-A4B-Preview leads on reach and risk.
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- Ling-3.1-flash-1019 vs MiniCPM-V 4.7 35B-A3B
- Ling-3.1-flash-1019 vs MiniCPM5-2B
- Ling-3.1-flash-1019 vs MiniMax-M2.5
- Ling-3.1-flash-1019 vs MiniMax-M2.7
- Ling-3.1-flash-1019 vs MiniMax-M3.1-Flash
- Ling-3.1-flash-1019 vs Mistral Large 4
- Ling-3.1-flash-1019 vs Muse Glimmer 30B
- Ling-3.1-flash-1019 vs Nanbeige4.2-3B
- Ling-3.1-flash-1019 vs Nanbeige4.3-33B
- Ling-3.1-flash-1019 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- Ling-3.1-flash-1019 vs Nemotron 3.5 Lightning 30B A3B
- Ling-3.1-flash-1019 vs Nemotron 3 Nano 30B A3B
- Ling-3.1-flash-1019 vs Nemotron 3 Nano 4B
- Ling-3.1-flash-1019 vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Ling-3.1-flash-1019 vs Nemotron 3 Super 120B A12B
- Ling-3.1-flash-1019 vs Nemotron 3 Ultra 550B A55B
- Ling-3.1-flash-1019 vs North Mini Code 1.0
- Ling-3.1-flash-1019 vs occamy-1.0
- Ling-3.1-flash-1019 vs Olmo 3 7B Think
- Ling-3.1-flash-1019 vs Ornith-1.5-122B
- Ling-3.1-flash-1019 vs Ornith-1.5-35B-A3B
- Ling-3.1-flash-1019 vs Ornith-1.5-397B
- Ling-3.1-flash-1019 vs Ornith-1.5-9B
- Ling-3.1-flash-1019 vs OTel-2.0-LLM-31B-IT
- Ling-3.1-flash-1019 vs Qwen-AgentWorld-35B-A3B
- Ling-3.1-flash-1019 vs Qwen3-30B-A3B-Instruct-2507
- Ling-3.1-flash-1019 vs Qwen3-4B-Instruct-2507
- Ling-3.1-flash-1019 vs Qwen3.5-0.8B
- Ling-3.1-flash-1019 vs Qwen3.5-122B-A10B
- Ling-3.1-flash-1019 vs Qwen3.5-2B
- Ling-3.1-flash-1019 vs Qwen3.5-397B-A17B
- Ling-3.1-flash-1019 vs Qwen3.5-4B
- Ling-3.1-flash-1019 vs Qwen3.5-9B
- Ling-3.1-flash-1019 vs Qwen3.6-27B
- Ling-3.1-flash-1019 vs Qwen3.6-35B-A3B
- Ling-3.1-flash-1019 vs Qwen3.8-27B
- Ling-3.1-flash-1019 vs Qwen3.8-Flash-Next
- Ling-3.1-flash-1019 vs Qwen3-Coder-30B-A3B-Instruct
- Ling-3.1-flash-1019 vs Qwen3-Coder-Next
- Ling-3.1-flash-1019 vs Qwen3-Omni-30B-A3B-Instruct
- Ling-3.1-flash-1019 vs Qwen3-VL-2B-Instruct
- Ling-3.1-flash-1019 vs Qwen3-VL-30B-A3B-Instruct
- Ling-3.1-flash-1019 vs Qwen3-VL-32B-Instruct
- Ling-3.1-flash-1019 vs Qwen3-VL-4B-Instruct
- Ling-3.1-flash-1019 vs Qwen3-VL-8B-Instruct
- Ling-3.1-flash-1019 vs Qwopus3.8-27B-Flash-V2
- Ling-3.1-flash-1019 vs Recursive Flint
- Ling-3.1-flash-1019 vs Ripple-65B Preview
- Ling-3.1-flash-1019 vs SenseNova-Loop-9B-Preview
- Ling-3.1-flash-1019 vs SmolLM3-3B
- Ling-3.1-flash-1019 vs Spark-X2.6-4B
- Ling-3.1-flash-1019 vs Step 3.7 Flash
- Ling-3.1-flash-1019 vs Swift 1.5 Qwen3.8-27B
- Ling-3.1-flash-1019 vs Swift 1.5 Qwen3.8-Flash-Next
- Ling-3.1-flash-1019 vs ThinkingCap (Qwen3.8 27B)
- Ling-3.1-flash-1019 vs Baidu Unlimited-OCR-2
- Ling-3.1-flash-1019 vs WeVisOne-VL-2B
- Ling-3.1-flash-1019 vs WeVisOne-VL-4B
- Ling-3.1-flash-1019 vs ZAYA1-8B
Machine-readable
- This page as Markdown
/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ling-3-1-flash-1019.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/internvl3-5-gpt-oss-20b-a4b-preview.json·/api/v1/tools/ling-3-1-flash-1019.json - From a terminal
anchor compare internvl3-5-gpt-oss-20b-a4b-preview ling-3-1-flash-1019(the CLI) - Over MCP
compare_tools {"a": "internvl3-5-gpt-oss-20b-a4b-preview", "b": "ling-3-1-flash-1019"}at/mcp, no key