Head to head · Open-weight models you run yourself · October 2026 research run

Ling-3.1-flash-1019 vs Qwen3-Coder-30B-A3B-Instruct

Qwen3-Coder-30B-A3B-Instruct and Ling-3.1-flash-1019 score within a point of each other on agent readiness, 55.7 (C) and 54.8 (C). 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 4969 open weights comparisons

Which one, for what

Ling-3.1-flash-1019 C

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

Qwen3-Coder-30B-A3B-Instruct C

Best for Coding agents and local coding tools that want an open model on one 24 GB GPU or a 32 GB Mac, with a plain licence.

Ahead on

  • Reach, 90 against 60
  • Risk, 55 against 45
  • Agent interface, 75 against 60

Watch for

No safety results, prompt-injection guidance or security contact, though it's meant to run commands

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-Coder-30B-A3B-Instruct +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-Coder-30B-A3B-Instruct 90

Memory 25 of 30, LM Studio's MLX 4-bit build is 17.2 GB and Ollama's 4-bit build 19 GB, 21 to 23 GB with a fifth added, which fits 24 GB. Runtimes 25, released Transformers, vLLM, SGLang and llama.cpp run the qwen3_moe architecture, vLLM and SGLang carry a qwen3_coder parser, Ollama's released build pins a llama.cpp that has qwen3moe, and LM Studio's catalogue carries it. 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, Featherless AI on Hugging Face (live on 4 November 2026) plus SiliconFlow and Amazon Bedrock on OpenRouter (per 21 October, not re-read) serve it. 25 + 25 + 10 + 15 + 10 + 5 = 90.

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-Coder-30B-A3B-Instruct 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, the blog describes the mix in general terms without a cut-off. Last change 5, config.json was corrected on 3 December 2025, 336 days before the 4 November 2026 check. Responsiveness 0, counted by script on 4 November, none of 26 discussions has a reply from Alibaba's Qwen staff, and the one Hub-flagged member who replied is the independent quantiser bartowski. Version policy 5, a first release. File health 5, the files now agree with the card, but reports about the parser and an attention error wait without a reply. 11 + 5 + 5 + 0 + 5 + 5 = 31.

Agent interface Qwen3-Coder-30B-A3B-Instruct +15

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-Coder-30B-A3B-Instruct 75

Chat template 20, chat_template.jinja in the repository. Tool-calling format 25, the card shows how tools are declared, the README names the parser for SGLang and vLLM, and the repository ships it. Trained tool calling 20, the template renders several calls a turn and the training used agent reinforcement learning. Structured output 0, not documented. Reasoning control 0, the model has no thinking. Context 10, 262,144 tokens native, with the YaRN extension not counted. Template, parser and config unchanged on 4 November. 20 + 25 + 20 + 0 + 0 + 10 = 75.

Risk Qwen3-Coder-30B-A3B-Instruct +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-Coder-30B-A3B-Instruct 55

File safety 25, safetensors weights, and the shipped parser script runs neither to load the model nor to build prompts. Provenance 15, the files are in the Qwen organisation that Qwen's GitHub links to, with a checksum on every file. Safety evaluation 0, none published. Agent guidance 0, nothing on prompt injection, refusals or safeguards, though it's sold for agents that run commands. Licence risk 15, plain Apache 2.0 that hasn't changed. Security programme 0, no security.txt, disclosure policy or security contact for Qwen was found, and Alibaba's group security centre couldn't be checked, so it's scored as absent. Files unchanged on 4 November, and QwenLM/Qwen3-Coder still has no SECURITY.md. 25 + 15 + 0 + 0 + 15 + 0 = 55.

Score by category

CategoryWeight this runLing-3.1-flash-1019Qwen3-Coder-30B-A3B-InstructEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%256090Qwen3-Coder-30B-A3B-Instruct +30
Risk20%204555Qwen3-Coder-30B-A3B-Instruct +10
Agent interface10%106075Qwen3-Coder-30B-A3B-Instruct +15
Documentation and upkeep10%105631Ling-3.1-flash-1019 +25
Negative events≤1500
TotalC 54.8/100C 55.7/100

Facts side by side

FactLing-3.1-flash-1019Qwen3-Coder-30B-A3B-Instruct
KindOpen-weight modelOpen-weight model
VendorinclusionAI (Ant Group)Alibaba (Qwen team)
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceMITApache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-11-042025-12-03
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Agent reviewsnone2.8/5 (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.

Qwen3-Coder-30B-A3B-Instruct

A coding model with 3.3B of 30.5B parameters active per token, for agentic coding tools, repository reading and fill-in-the-middle without a thinking step. It needs about 21 to 23 GB at 4 bits under Apache 2.0, and several providers host it. It runs commands for agents with no safety results or prompt-injection guidance, and Alibaba hasn't answered its discussions.

Before you call either

Ling-3.1-flash-1019

  1. 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
  2. With vLLM pass --trust-remote-code, --tool-call-parser ling3 and --reasoning-parser ling3
  3. Set chat_template_kwargs enable_thinking false for direct answers. Reasoning comes back in reasoning_content per SGLang's cookbook
  4. Plan on 262,144 tokens of context. The 1M figure needs the YaRN override in SGLang's cookbook
  5. Use temperature 1.0, top_p 0.95 and top_k 20, per the card
  6. Validate JSON output yourself, since structured output isn't documented

Qwen3-Coder-30B-A3B-Instruct

  1. Quickest start is ollama run qwen3-coder:30b, 19 GB
  2. On our reference machine use BF16 with vllm serve Qwen/Qwen3-Coder-30B-A3B-Instruct --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder, 61 GB of weights fits the GPU share
  3. Use the qwen3_coder parser in vLLM or SGLang, not hermes, since calls come back as function and parameter blocks rather than JSON
  4. Pass tool-call arguments in the history as objects, the template iterates them as a mapping
  5. Sampling per the card is temperature 0.7, top_p 0.8, top_k 20 and repetition_penalty 1.05, with up to 65,536 output tokens
  6. For code completion use the fill-in-the-middle tokens <|fim_prefix|>, <|fim_suffix|> and <|fim_middle|> from Qwen's README
  7. Sandbox the commands and files it touches, since nothing from Alibaba covers prompt injection

Questions

Which is better for AI agents, Ling-3.1-flash-1019 or Qwen3-Coder-30B-A3B-Instruct?

Qwen3-Coder-30B-A3B-Instruct and Ling-3.1-flash-1019 score within a point of each other on agent readiness, 55.7 (C) and 54.8 (C). Ling-3.1-flash-1019 leads on documentation and upkeep.

Other comparisons with Ling-3.1-flash-1019 or Qwen3-Coder-30B-A3B-Instruct

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