Head to head · Open-weight models you run yourself · October 2026 research run
Ling-3.1-flash-1019 vs Qwen3.6-27B
Qwen3.6-27B scores 66.7 (B) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in 3 of 4 scored categories. Ling-3.1-flash-1019 leads on documentation and upkeep. 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
Best for Teams with a multi-GPU server that want an MIT-licensed flagship for long agent runs and coding
Ahead on
- Documentation and upkeep, 56 against 31
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 Agents that want an open coding and tool-calling model with vision on one 24 to 32 GB GPU or Mac, with plain licence terms.
Ahead on
- Reach, 90 against 60
- Risk, 55 against 45
- Agent interface, 70 against 60
Watch for
No safety results, prompt-injection guidance or security contact
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.
Reach Qwen3.6-27B +30
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.
Qwen3.6-27B 90
Memory 25 of 30, Ollama's text-only 4-bit build is 17 GB and the vision build 18 GB, about 20 to 22 GB with a fifth added, which fits 24 GB. Runtimes 25, released versions of llama.cpp, Ollama, vLLM, SGLang, LM Studio and Transformers all run the qwen3_5 architecture. Builds 10 of 15, Alibaba's only quantised build is FP8. Terms 15, Apache 2.0 with nothing added. Download 10, neither repository is gated. Hosted 5, three inference providers are live on Hugging Face and Alibaba's Model Studio serves it per the card.
Documentation and upkeep Ling-3.1-flash-1019 +25
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.
Qwen3.6-27B 31
Model card 11 of 20, intended use, architecture, parameters and context are there, but limitations, languages and a knowledge cut-off aren't. Training data 5, only a general family description without a cut-off, in the GitHub README. Last change 5, the files last changed on 24 April 2026, 191 days before this check, and Qwen's GitHub repository holds only a README and licence, so no reference code of Alibaba's changed later. Responsiveness 0, Qwen answered none of the 40 most recent questions and reports, recounted on 1 November. Version policy 5, earlier and later 27B models stay in their own repositories without dated notes. File health 5, the files agree with the card but template and tool-calling reports wait without a reply.
Risk Qwen3.6-27B +10
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.
Qwen3.6-27B 55
File safety 25, safetensors only and nothing from the repository runs to load it or build prompts. Provenance 15, the files are in the Qwen organisation that Qwen's GitHub links to, and every file has a hash on the Hub. Safety evaluation 0, none published on the card or README. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 15, plain Apache 2.0 that hasn't changed. Security programme 0, no security.txt, SECURITY.md or reporting route was found, and Alibaba's group security centre couldn't be checked, so it's scored as absent.
Agent interface Qwen3.6-27B +10
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.
Qwen3.6-27B 70
Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, the card names the qwen3_coder parser but the declaration and call format live only in the template. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented. Reasoning control 10, thinking turns off per request and comes back in think tags split out by the qwen3 parser. Context 10, 262,144 tokens native.
Score by category
| Category | Weight this run | Ling-3.1-flash-1019 | Qwen3.6-27B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 60 | 90 | Qwen3.6-27B +30 |
| Risk | 20%20 | 45 | 55 | Qwen3.6-27B +10 |
| Agent interface | 10%10 | 60 | 70 | Qwen3.6-27B +10 |
| Documentation and upkeep | 10%10 | 56 | 31 | Ling-3.1-flash-1019 +25 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 54.8/100 | B 66.7/100 |
Facts side by side
| Fact | Ling-3.1-flash-1019 | Qwen3.6-27B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | inclusionAI (Ant Group) | Alibaba (Qwen team) |
| 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 | 2026-04-22 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 3/5 (4) |
How the scores have moved
Ling-3.1-flash-1019
No score change since it was listed.
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.
Qwen3.6-27B
A capable dense cousin for agentic coding and tool calls with images and video in, running in every major runtime. Ollama's 4-bit build needs about 22 GB, and three providers host it. The main risk is silence, with no safety results, no agent guidance, no answers in its repository and Qwen3.8-27B now ahead of it.
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
Qwen3.6-27B
- Quickest start is
ollama run qwen3.6:27b, about 18 GB of files with the vision projector - Alibaba's FP8 build with the card's parsers is
vllm serve Qwen/Qwen3.6-27B-FP8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder, vllm 0.19.0 or later - Use the qwen3_coder tool parser, the template writes calls as function and parameter blocks inside tool_call
- Thinking is on by default, pass
"chat_template_kwargs": {"enable_thinking": false}for short tasks, and/thinkand/nothinkaren't supported "preserve_thinking": truein chat_template_kwargs keeps earlier reasoning in context, which helps long agent sessions but grows the prompt- Sampling per the card is temperature 1.0, top_p 0.95, top_k 20 when thinking, and temperature 0.7, top_p 0.8, presence_penalty 1.5 without
- Keep at least 128K of context if you rely on thinking, and turn on YaRN only beyond 262,144 tokens
- Validate any JSON it returns or use the runtime's guided decoding, since structured output isn't documented
- Qwen3.8-27B (listed as qwen3-8-27b) is the newer model at this size on the same runtimes
Questions
Which is better for AI agents, Ling-3.1-flash-1019 or Qwen3.6-27B?
Qwen3.6-27B scores 66.7 (B) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in 3 of 4 scored categories. Ling-3.1-flash-1019 leads on documentation and upkeep.
Other comparisons with Ling-3.1-flash-1019 or Qwen3.6-27B
- Agents-A1-4B vs Ling-3.1-flash-1019
- Agents-A1-4B vs Qwen3.6-27B
- Apertus v1.5 8B vs Ling-3.1-flash-1019
- Apertus v1.5 8B vs Qwen3.6-27B
- Apodex-1.1-mini vs Ling-3.1-flash-1019
- Apodex-1.1-mini vs Qwen3.6-27B
- Arete-27B vs Ling-3.1-flash-1019
- Arete-27B vs Qwen3.6-27B
- BigBang-v1 vs Ling-3.1-flash-1019
- BigBang-v1 vs Qwen3.6-27B
- Carbon-X vs Ling-3.1-flash-1019
- Carbon-X vs Qwen3.6-27B
- Cola2 vs Ling-3.1-flash-1019
- Cola2 vs Qwen3.6-27B
- Cosmos-Reason2-2B vs Ling-3.1-flash-1019
- Cosmos-Reason2-2B vs Qwen3.6-27B
- Darwin-36B-Opus vs Ling-3.1-flash-1019
- Darwin-36B-Opus vs Qwen3.6-27B
- DeepSeek-V3.2 vs Ling-3.1-flash-1019
- DeepSeek-V3.2 vs Qwen3.6-27B
- DeepSeek V4.1 Flash vs Ling-3.1-flash-1019
- DeepSeek V4.1 Flash vs Qwen3.6-27B
- DeepSeek-V4-Flash-0731 vs Ling-3.1-flash-1019
- DeepSeek-V4-Flash-0731 vs Qwen3.6-27B
- Devstral Small 2 vs Ling-3.1-flash-1019
- Devstral Small 2 vs Qwen3.6-27B
- DiffusionGemma 26B A4B IT vs Ling-3.1-flash-1019
- DiffusionGemma 26B A4B IT vs Qwen3.6-27B
- EXAONE 4.5 33B vs Ling-3.1-flash-1019
- EXAONE 4.5 33B vs Qwen3.6-27B
- Gemma 4 12B IT vs Ling-3.1-flash-1019
- Gemma 4 12B IT vs Qwen3.6-27B
- Gemma 4 26B A4B IT vs Ling-3.1-flash-1019
- Gemma 4 26B A4B IT vs Qwen3.6-27B
- Gemma 4 31B IT vs Ling-3.1-flash-1019
- Gemma 4 31B IT vs Qwen3.6-27B
- Gemma 4 E2B IT vs Ling-3.1-flash-1019
- Gemma 4 E2B IT vs Qwen3.6-27B
- Gemma 4 E4B IT vs Ling-3.1-flash-1019
- Gemma 4 E4B IT vs Qwen3.6-27B
- GLM-4.5-Air vs Ling-3.1-flash-1019
- GLM-4.5-Air vs Qwen3.6-27B
- GLM-4.7-Flash vs Ling-3.1-flash-1019
- GLM-4.7-Flash vs Qwen3.6-27B
- GLM-5.3-Flash vs Ling-3.1-flash-1019
- GLM-5.3-Flash vs Qwen3.6-27B
- GLM-5.3 vs Ling-3.1-flash-1019
- GLM-5.3 vs Qwen3.6-27B
- gpt-oss-120b vs Ling-3.1-flash-1019
- gpt-oss-120b vs Qwen3.6-27B
- gpt-oss-20b vs Ling-3.1-flash-1019
- gpt-oss-20b vs Qwen3.6-27B
- Granite 4.2 30B vs Ling-3.1-flash-1019
- Granite 4.2 30B vs Qwen3.6-27B
- Granite 4.2 3B vs Ling-3.1-flash-1019
- Granite 4.2 3B vs Qwen3.6-27B
- Granite 4.2 8B vs Ling-3.1-flash-1019
- Granite 4.2 8B vs Qwen3.6-27B
- Hy-Memory-30B-A3B vs Ling-3.1-flash-1019
- Hy-Memory-30B-A3B vs Qwen3.6-27B
- Hy3 vs Ling-3.1-flash-1019
- Hy3 vs Qwen3.6-27B
- Inkling-Small vs Ling-3.1-flash-1019
- Inkling-Small vs Qwen3.6-27B
- Inkling vs Ling-3.1-flash-1019
- Inkling vs Qwen3.6-27B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Ling-3.1-flash-1019
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3.6-27B
- Kimi K3.1 Pro vs Ling-3.1-flash-1019
- Kimi K3.1 Pro vs Qwen3.6-27B
- Kimi K3 vs Ling-3.1-flash-1019
- Kimi K3 vs Qwen3.6-27B
- Kolibri-1 vs Ling-3.1-flash-1019
- Kolibri-1 vs Qwen3.6-27B
- Kronos vs Ling-3.1-flash-1019
- Kronos vs Qwen3.6-27B
- Laguna M.1 vs Ling-3.1-flash-1019
- Laguna M.1 vs Qwen3.6-27B
- Laguna S 2.1 vs Ling-3.1-flash-1019
- Laguna S 2.1 vs Qwen3.6-27B
- Laguna XS 2.1 vs Ling-3.1-flash-1019
- Laguna XS 2.1 vs Qwen3.6-27B
- LBS-27B vs Ling-3.1-flash-1019
- LBS-27B vs Qwen3.6-27B
- LFM2.5-2.6B vs Ling-3.1-flash-1019
- LFM2.5-2.6B vs Qwen3.6-27B
- LFM3-1.7B vs Ling-3.1-flash-1019
- LFM3-1.7B vs Qwen3.6-27B
- LFM3-Audio-2B vs Ling-3.1-flash-1019
- LFM3-Audio-2B vs Qwen3.6-27B
- LFM3-Omni-2B vs Ling-3.1-flash-1019
- LFM3-Omni-2B vs Qwen3.6-27B
- LFM3-VL-2B vs Ling-3.1-flash-1019
- LFM3-VL-2B vs Qwen3.6-27B
- Ling-3.0-tiny vs Ling-3.1-flash-1019
- Ling-3.0-tiny vs Qwen3.6-27B
- Ling-3.1-flash-1019 vs LongCat-2.5
- Ling-3.1-flash-1019 vs MiMo-V2.6 Distill Qwen 9B
- Ling-3.1-flash-1019 vs MiMo-V2.6-Flash-RL
- Ling-3.1-flash-1019 vs MiniCPM-o 4.5
- Ling-3.1-flash-1019 vs MiniCPM-V 4.7 1B
- 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 Qwen3.6-35B-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
- LongCat-2.5 vs Qwen3.6-27B
- MiMo-V2.6 Distill Qwen 9B vs Qwen3.6-27B
- MiMo-V2.6-Flash-RL vs Qwen3.6-27B
- MiniCPM-o 4.5 vs Qwen3.6-27B
- MiniCPM-V 4.7 1B vs Qwen3.6-27B
- MiniCPM-V 4.7 35B-A3B vs Qwen3.6-27B
- MiniCPM5-2B vs Qwen3.6-27B
- MiniMax-M2.5 vs Qwen3.6-27B
- MiniMax-M2.7 vs Qwen3.6-27B
- MiniMax-M3.1-Flash vs Qwen3.6-27B
- Mistral Large 4 vs Qwen3.6-27B
- Muse Glimmer 30B vs Qwen3.6-27B
- Nanbeige4.2-3B vs Qwen3.6-27B
- Nanbeige4.3-33B vs Qwen3.6-27B
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.6-27B
- Nemotron 3.5 Lightning 30B A3B vs Qwen3.6-27B
- Nemotron 3 Nano 30B A3B vs Qwen3.6-27B
- Nemotron 3 Nano 4B vs Qwen3.6-27B
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3.6-27B
- Nemotron 3 Super 120B A12B vs Qwen3.6-27B
- Nemotron 3 Ultra 550B A55B vs Qwen3.6-27B
- North Mini Code 1.0 vs Qwen3.6-27B
- occamy-1.0 vs Qwen3.6-27B
- Olmo 3 7B Think vs Qwen3.6-27B
- Ornith-1.5-122B vs Qwen3.6-27B
- Ornith-1.5-35B-A3B vs Qwen3.6-27B
- Ornith-1.5-397B vs Qwen3.6-27B
- Ornith-1.5-9B vs Qwen3.6-27B
- OTel-2.0-LLM-31B-IT vs Qwen3.6-27B
- Qwen-AgentWorld-35B-A3B vs Qwen3.6-27B
- Qwen3-30B-A3B-Instruct-2507 vs Qwen3.6-27B
- Qwen3-4B-Instruct-2507 vs Qwen3.6-27B
- Qwen3.5-0.8B vs Qwen3.6-27B
- Qwen3.5-122B-A10B vs Qwen3.6-27B
- Qwen3.5-2B vs Qwen3.6-27B
- Qwen3.5-397B-A17B vs Qwen3.6-27B
- Qwen3.5-4B vs Qwen3.6-27B
- Qwen3.5-9B vs Qwen3.6-27B
- Qwen3.6-27B vs Qwen3.6-35B-A3B
- Qwen3.6-27B vs Qwen3.8-27B
- Qwen3.6-27B vs Qwen3.8-Flash-Next
- Qwen3.6-27B vs Qwen3-Coder-30B-A3B-Instruct
- Qwen3.6-27B vs Qwen3-Coder-Next
- Qwen3.6-27B vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3.6-27B vs Qwen3-VL-2B-Instruct
- Qwen3.6-27B vs Qwen3-VL-30B-A3B-Instruct
- Qwen3.6-27B vs Qwen3-VL-32B-Instruct
- Qwen3.6-27B vs Qwen3-VL-4B-Instruct
- Qwen3.6-27B vs Qwen3-VL-8B-Instruct
- Qwen3.6-27B vs Qwopus3.8-27B-Flash-V2
- Qwen3.6-27B vs Recursive Flint
- Qwen3.6-27B vs Ripple-65B Preview
- Qwen3.6-27B vs SenseNova-Loop-9B-Preview
- Qwen3.6-27B vs SmolLM3-3B
- Qwen3.6-27B vs Spark-X2.6-4B
- Qwen3.6-27B vs Step 3.7 Flash
- Qwen3.6-27B vs Swift 1.5 Qwen3.8-27B
- Qwen3.6-27B vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3.6-27B vs ThinkingCap (Qwen3.8 27B)
- Qwen3.6-27B vs Baidu Unlimited-OCR-2
- Qwen3.6-27B vs WeVisOne-VL-2B
- Qwen3.6-27B vs WeVisOne-VL-4B
- Qwen3.6-27B vs ZAYA1-8B
Machine-readable
- This page as Markdown
/compare/ling-3-1-flash-1019-vs-qwen3-6-27b.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/qwen3-6-27b.json - From a terminal
anchor compare ling-3-1-flash-1019 qwen3-6-27b(the CLI) - Over MCP
compare_tools {"a": "ling-3-1-flash-1019", "b": "qwen3-6-27b"}at/mcp, no key