Head to head · Open-weight models you run yourself · October 2026 research run
LFM3-VL-2B vs Ling-3.1-flash-1019
LFM3-VL-2B scores 61 (C) 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 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 Agents on a laptop, phone or robot that need to read screens, documents, images and video from a model under 2 GB at 4 bits
Ahead on
- Reach, 85 against 60
- Agent interface, 90 against 60
- Documentation and upkeep, 77 against 56
Watch for
vLLM and SGLang support is unreleased, which the card says itself
Best for Teams with a multi-GPU server that want an MIT-licensed flagship for long agent runs and coding
Ahead on
- Risk, 45 against 40
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.
Agent interface LFM3-VL-2B +30
LFM3-VL-2B 90
Chat template 20, chat_template.jinja in the repository (12,129 characters), with roles, images and video, tools, deferred tools, tool results and the thinking switch. Tool-calling format 25, the card and Liquid's tool-use page document tools passed through tools= in apply_chat_template, calls returned as XML and results sent back as tool messages. Trained tool calling 20, the template loops over every entry in an assistant turn's tool_calls, so several calls fit in one turn, and the card says its fine-tuning mix included tool calling and agentic traces. Structured output 5, the card's vLLM and SGLang commands set a structured-output backend and its fine-tuning table has a JSON extraction notebook, but nothing says how to ask this model for JSON. Liquid's API guide documents response_format with lfm3-1.7b as its example. Reasoning control 10, Liquid's chat-template page documents enable_thinking=False in apply_chat_template for LFM3, with reasoning on by default inside think tags, and the card documents preserve_thinking and reports a no-thinking configuration in its benchmarks. Context 10, 128K tokens of input and output per the card, and max_position_embeddings 128000 for the language model in config.json. 20 + 25 + 20 + 5 + 10 + 10 = 90.
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.
Reach LFM3-VL-2B +25
LFM3-VL-2B 85
Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB with its 0.20 GB projector file, and 1.30 GB × 1.2 = 1.6 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 15, Released llama.cpp has the lfm3 architecture and the LFM3-VL projector (src/models/lfm3.cpp and tools/mtmd at b12242), Ollama's library has an lfm3-vl entry, and LM Studio's catalogue lists liquid/lfm3-vl-2b. Builds 15, Liquid's GGUF repository holds Q4_0, Q4_K_M, Q8_0, F16 and BF16 files, and Liquid publishes 4-bit and 8-bit MLX builds and an ONNX repository, all in the LiquidAI organisation. Terms 10, the LFM Open License v1.0, a custom licence that lets anyone use it commercially for free below a threshold of $10M annual revenue. Download 10, the Hub API reports gated false for the model and its GGUF, MLX and ONNX repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, Liquid's own API lists lfm3-vl-2b among its models, so the publisher hosts it. 30 + 15 + 15 + 10 + 10 + 5 = 85.
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.
Documentation and upkeep LFM3-VL-2B +21
LFM3-VL-2B 77
Model card 17, Intended use (questions about video, documents, charts and app screens, finding objects, spatial reasoning), a limitation (not recommended for coding, multi-turn conversations or complex reasoning), architecture (language model, 14-layer vision encoder and projector), parameters (1.92B), context (128K) and 16 languages. Training data 5, A general description: the base's 22T tokens of pre-training with the kinds of data added since LFM2, then vision mid-training on image and video data, supervised fine-tuning, vision reinforcement learning and distillation. Last change 20, the last commit, on 9 November 2026, added the OpenVINO checkpoint to the card's inference table, a day before this check. Responsiveness 20, Of the 5 discussions that aren't pull requests (27 to 31 October 2026), 3 have a reply from a LiquidAI member, which is most. Version policy 5, LFM3 came out in new repositories and the LFM2.5 models stay in theirs. File health 10, Config, template and card agree on the lfm3_vl architecture, the 128,000-token context and the vision settings in processor_config.json. 17 + 5 + 20 + 20 + 5 + 10 = 77.
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.
Risk Ling-3.1-flash-1019 +5
LFM3-VL-2B 40
File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, files in the LiquidAI organisation, which Liquid's docs page for the model and its blog post link to (10). Safety evaluation 0, no safety results on the card or in Liquid's blog post for the LFM3 family, neither of which mentions safety testing. Agent guidance 0, Nothing on prompt injection, an instruction hierarchy, what refusals cover or safeguards to add. Licence risk 10, 15 less 5 for the $10M annual revenue threshold on commercial use. Security programme 0, no way to report a flaw found. 15 + 15 + 0 + 0 + 10 + 0 = 40.
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.
Score by category
| Category | Weight this run | LFM3-VL-2B | Ling-3.1-flash-1019 | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 85 | 60 | LFM3-VL-2B +25 |
| Risk | 20%20 | 40 | 45 | Ling-3.1-flash-1019 +5 |
| Agent interface | 10%10 | 90 | 60 | LFM3-VL-2B +30 |
| Documentation and upkeep | 10%10 | 77 | 56 | LFM3-VL-2B +21 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 54.8/100 |
Facts side by side
| Fact | LFM3-VL-2B | Ling-3.1-flash-1019 |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Liquid AI | inclusionAI (Ant Group) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | LFM Open License v1.0 (free commercial use below $10M annual revenue) | MIT |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-11-09 | 2026-11-04 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
Verdicts
LFM3-VL-2B
A 1.92B vision model that reads images and video on an 8 GB machine, with a documented tool-call format and thinking. It runs today in llama.cpp, Ollama and LM Studio, while vLLM and SGLang support is still unreleased. Transformers needs repository code, and there are no safety results, security contact or data sources.
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
LFM3-VL-2B
- Start it with
llama-server -hf LiquidAI/LFM3-VL-2B-GGUF:Q4_K_M --jinjaorollama run lfm3-vl, with llama.cpp b11960 or later - Use temperature 0.1, top_k 50 and repetition penalty 1.1, per the card
- Video is sampled at 2 frames a second up to 768 frames, and each frame costs context. Video input in llama.cpp needs ffmpeg
- Don't plan on vLLM or SGLang until a release carries LFM3-VL
- Validate JSON output yourself
- Liquid doesn't recommend it for coding, multi-turn conversations or complex reasoning
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, LFM3-VL-2B or Ling-3.1-flash-1019?
LFM3-VL-2B scores 61 (C) 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 risk.
Other comparisons with LFM3-VL-2B or Ling-3.1-flash-1019
- Agents-A1-4B vs LFM3-VL-2B
- Agents-A1-4B vs Ling-3.1-flash-1019
- Apertus v1.5 8B vs LFM3-VL-2B
- Apertus v1.5 8B vs Ling-3.1-flash-1019
- Apodex-1.1-mini vs LFM3-VL-2B
- Apodex-1.1-mini vs Ling-3.1-flash-1019
- Arete-27B vs LFM3-VL-2B
- Arete-27B vs Ling-3.1-flash-1019
- BigBang-v1 vs LFM3-VL-2B
- BigBang-v1 vs Ling-3.1-flash-1019
- Carbon-X vs LFM3-VL-2B
- Carbon-X vs Ling-3.1-flash-1019
- Cosmos-Reason2-2B vs LFM3-VL-2B
- Cosmos-Reason2-2B vs Ling-3.1-flash-1019
- Darwin-36B-Opus vs LFM3-VL-2B
- Darwin-36B-Opus vs Ling-3.1-flash-1019
- DeepSeek-V3.2 vs LFM3-VL-2B
- DeepSeek-V3.2 vs Ling-3.1-flash-1019
- DeepSeek V4.1 Flash vs LFM3-VL-2B
- DeepSeek V4.1 Flash vs Ling-3.1-flash-1019
- DeepSeek-V4-Flash-0731 vs LFM3-VL-2B
- DeepSeek-V4-Flash-0731 vs Ling-3.1-flash-1019
- DiffusionGemma 26B A4B IT vs LFM3-VL-2B
- DiffusionGemma 26B A4B IT vs Ling-3.1-flash-1019
- Gemma 4 12B IT vs LFM3-VL-2B
- Gemma 4 12B IT vs Ling-3.1-flash-1019
- Gemma 4 26B A4B IT vs LFM3-VL-2B
- Gemma 4 26B A4B IT vs Ling-3.1-flash-1019
- Gemma 4 31B IT vs LFM3-VL-2B
- Gemma 4 31B IT vs Ling-3.1-flash-1019
- Gemma 4 E2B IT vs LFM3-VL-2B
- Gemma 4 E2B IT vs Ling-3.1-flash-1019
- Gemma 4 E4B IT vs LFM3-VL-2B
- Gemma 4 E4B IT vs Ling-3.1-flash-1019
- GLM-4.5-Air vs LFM3-VL-2B
- GLM-4.5-Air vs Ling-3.1-flash-1019
- GLM-4.7-Flash vs LFM3-VL-2B
- GLM-4.7-Flash vs Ling-3.1-flash-1019
- GLM-5.3-Flash vs LFM3-VL-2B
- GLM-5.3-Flash vs Ling-3.1-flash-1019
- GLM-5.3 vs LFM3-VL-2B
- GLM-5.3 vs Ling-3.1-flash-1019
- gpt-oss-120b vs LFM3-VL-2B
- gpt-oss-120b vs Ling-3.1-flash-1019
- gpt-oss-20b vs LFM3-VL-2B
- gpt-oss-20b vs Ling-3.1-flash-1019
- Granite 4.2 30B vs LFM3-VL-2B
- Granite 4.2 30B vs Ling-3.1-flash-1019
- Granite 4.2 3B vs LFM3-VL-2B
- Granite 4.2 3B vs Ling-3.1-flash-1019
- Granite 4.2 8B vs LFM3-VL-2B
- Granite 4.2 8B vs Ling-3.1-flash-1019
- Hy-Memory-30B-A3B vs LFM3-VL-2B
- Hy-Memory-30B-A3B vs Ling-3.1-flash-1019
- Hy3 vs LFM3-VL-2B
- Hy3 vs Ling-3.1-flash-1019
- Inkling-Small vs LFM3-VL-2B
- Inkling-Small vs Ling-3.1-flash-1019
- Inkling vs LFM3-VL-2B
- Inkling vs Ling-3.1-flash-1019
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs LFM3-VL-2B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Ling-3.1-flash-1019
- Kimi K3.1 Pro vs LFM3-VL-2B
- Kimi K3.1 Pro vs Ling-3.1-flash-1019
- Kimi K3 vs LFM3-VL-2B
- Kimi K3 vs Ling-3.1-flash-1019
- Kolibri-1 vs LFM3-VL-2B
- Kolibri-1 vs Ling-3.1-flash-1019
- Kronos vs LFM3-VL-2B
- Kronos vs Ling-3.1-flash-1019
- Laguna M.1 vs LFM3-VL-2B
- Laguna M.1 vs Ling-3.1-flash-1019
- Laguna S 2.1 vs LFM3-VL-2B
- Laguna S 2.1 vs Ling-3.1-flash-1019
- Laguna XS 2.1 vs LFM3-VL-2B
- Laguna XS 2.1 vs Ling-3.1-flash-1019
- LBS-27B vs LFM3-VL-2B
- LBS-27B vs Ling-3.1-flash-1019
- LFM2.5-2.6B vs LFM3-VL-2B
- LFM2.5-2.6B vs Ling-3.1-flash-1019
- LFM3-1.7B vs LFM3-VL-2B
- LFM3-1.7B vs Ling-3.1-flash-1019
- LFM3-Audio-2B vs LFM3-VL-2B
- LFM3-Audio-2B vs Ling-3.1-flash-1019
- LFM3-Omni-2B vs LFM3-VL-2B
- LFM3-Omni-2B vs Ling-3.1-flash-1019
- LFM3-VL-2B vs Ling-3.0-tiny
- LFM3-VL-2B vs MiMo-V2.6 Distill Qwen 9B
- LFM3-VL-2B vs MiniCPM-o 4.5
- LFM3-VL-2B vs MiniCPM-V 4.7 1B
- LFM3-VL-2B vs MiniCPM-V 4.7 35B-A3B
- LFM3-VL-2B vs MiniCPM5-2B
- LFM3-VL-2B vs MiniMax-M2.5
- LFM3-VL-2B vs MiniMax-M2.7
- LFM3-VL-2B vs MiniMax-M3.1-Flash
- LFM3-VL-2B vs Mistral Large 4
- LFM3-VL-2B vs Muse Glimmer 30B
- LFM3-VL-2B vs Nanbeige4.3-33B
- LFM3-VL-2B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- LFM3-VL-2B vs Nemotron 3.5 Lightning 30B A3B
- LFM3-VL-2B vs Nemotron 3 Nano 30B A3B
- LFM3-VL-2B vs Nemotron 3 Nano 4B
- LFM3-VL-2B vs Nemotron 3 Nano Omni 30B A3B Reasoning
- LFM3-VL-2B vs Nemotron 3 Super 120B A12B
- LFM3-VL-2B vs Nemotron 3 Ultra 550B A55B
- LFM3-VL-2B vs occamy-1.0
- LFM3-VL-2B vs Olmo 3 7B Think
- LFM3-VL-2B vs Ornith-1.5-122B
- LFM3-VL-2B vs Ornith-1.5-35B-A3B
- LFM3-VL-2B vs Ornith-1.5-397B
- LFM3-VL-2B vs Ornith-1.5-9B
- LFM3-VL-2B vs Qwen-AgentWorld-35B-A3B
- LFM3-VL-2B vs Qwen3-30B-A3B-Instruct-2507
- LFM3-VL-2B vs Qwen3-4B-Instruct-2507
- LFM3-VL-2B vs Qwen3.5-0.8B
- LFM3-VL-2B vs Qwen3.5-122B-A10B
- LFM3-VL-2B vs Qwen3.5-2B
- LFM3-VL-2B vs Qwen3.5-397B-A17B
- LFM3-VL-2B vs Qwen3.5-4B
- LFM3-VL-2B vs Qwen3.5-9B
- LFM3-VL-2B vs Qwen3.6-27B
- LFM3-VL-2B vs Qwen3.6-35B-A3B
- LFM3-VL-2B vs Qwen3.8-27B
- LFM3-VL-2B vs Qwen3.8-Flash-Next
- LFM3-VL-2B vs Qwen3-Coder-30B-A3B-Instruct
- LFM3-VL-2B vs Qwen3-Coder-Next
- LFM3-VL-2B vs Qwen3-Omni-30B-A3B-Instruct
- LFM3-VL-2B vs Qwen3-VL-2B-Instruct
- LFM3-VL-2B vs Qwen3-VL-30B-A3B-Instruct
- LFM3-VL-2B vs Qwen3-VL-32B-Instruct
- LFM3-VL-2B vs Qwen3-VL-4B-Instruct
- LFM3-VL-2B vs Qwen3-VL-8B-Instruct
- LFM3-VL-2B vs Qwopus3.8-27B-Flash-V2
- LFM3-VL-2B vs Recursive Flint
- LFM3-VL-2B vs Ripple-65B Preview
- LFM3-VL-2B vs SmolLM3-3B
- LFM3-VL-2B vs Spark-X2.6-4B
- LFM3-VL-2B vs Swift 1.5 Qwen3.8-27B
- LFM3-VL-2B vs Swift 1.5 Qwen3.8-Flash-Next
- LFM3-VL-2B vs ThinkingCap (Qwen3.8 27B)
- LFM3-VL-2B vs Baidu Unlimited-OCR-2
- Ling-3.0-tiny vs Ling-3.1-flash-1019
- Ling-3.1-flash-1019 vs MiMo-V2.6 Distill Qwen 9B
- 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 Nanbeige4.3-33B
- 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 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 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 SmolLM3-3B
- Ling-3.1-flash-1019 vs Spark-X2.6-4B
- 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
Machine-readable
- This page as Markdown
/compare/lfm3-vl-2b-vs-ling-3-1-flash-1019.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/lfm3-vl-2b.json·/api/v1/tools/ling-3-1-flash-1019.json - From a terminal
anchor compare lfm3-vl-2b ling-3-1-flash-1019(the CLI) - Over MCP
compare_tools {"a": "lfm3-vl-2b", "b": "ling-3-1-flash-1019"}at/mcp, no key