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

Ling-3.0-tiny vs MiMo-V2.6-Flash-RL

MiMo-V2.6-Flash-RL scores 61 (C) on agent readiness against Ling-3.0-tiny's 56.2 (C), and leads in 2 of 4 scored categories. Ling-3.0-tiny 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

Ling-3.0-tiny C

Best for Agents on a laptop or small GPU that want a fast local model for tool calls and short reasoning, under MIT

Ahead on

  • Reach, 95 against 65
  • Risk, 50 against 45

Watch for

No inference provider is listed on the Hub and no inclusionAI API serves it

MiMo-V2.6-Flash-RL C

Best for Teams with a GPU server, or a provider account, that want an open agent model with a very long context

Ahead on

  • Agent interface, 85 against 60

Watch for

178 GB at 4 bits, so it needs about 200 GB of memory

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 Ling-3.0-tiny +30

Ling-3.0-tiny 95

Memory earns the full 30, since inclusionAI's own Q4_K_M GGUF is 4.8 GB, about 6 GB with a fifth added. Runtimes earn the full 25, from released Transformers, vLLM, SGLang and llama.cpp, and Ollama, whose v0.40.14 (2 November 2026) pins llama.cpp b11963 with the bailingmoe3 architecture (Ollama v0.40.7 of 20 October already pinned b11738, which has it). LM Studio has nothing. Builds earn 15 for FP8, INT4 and four GGUF precisions from inclusionAI. MIT with nothing added gives terms the full 15 and the ungated repositories give download the full 10. Hosted too scores 0, because the Hub's model page lists no inference provider and no inclusionAI or Ant Group API serving it was found.

MiMo-V2.6-Flash-RL 65

Memory 5, the smallest build at 4 bits or better is ggml-org's MXFP4 GGUF, 168.93 GB with its projector, and 168.93 GB × 1.2 = 202.7 GB, within 256 GB. Xiaomi's own files are 177.74 GB. ggml-org's Q2_K is below 4 bits and isn't read. Runtimes 20, Released vLLM 0.33.0 and SGLang 0.5.23 have the mimo_v2 model and the mimo parsers, Transformers 5.22.2 has a mimo_v2_flash model, and llama.cpp b12242 has the architecture, with a GGUF from its own organisation. Builds 10, one precision from Xiaomi, the 4-bit files it releases. Terms 15, MIT in the card's metadata, an OSI licence. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, the card says it is on Xiaomi's MiMo Open Platform API and on OpenRouter, and Novita is live for it on Hugging Face. 5 + 20 + 10 + 15 + 10 + 5 = 65.

Agent interface MiMo-V2.6-Flash-RL +25

Ling-3.0-tiny 60

Chat template 20, a Jinja file in the repository. Tool-calling format 10, the format is only in the template and the card names the ling3 parser in commands without describing it, while SGLang uses another parser. Trained tool calling 20, several calls a turn and agentic results on the card. Structured output 0, not documented. Reasoning control 0, thinking turns off per request but the card doesn't say where the reasoning comes back. Context 10, 131,072 tokens native.

MiMo-V2.6-Flash-RL 85

Chat template 20, chat_template.jinja in the repository (3,868 characters), with a copy in tokenizer_config.json. Tool-calling format 25, the card names the parser to use in two runtimes: the mimo tool-call parser in SGLang and in vLLM, with auto tool choice, and both ship it in a release. Trained tool calling 20, the card describes reinforcement learning on coding and general agent tasks across several harnesses, and the template renders every call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the template has an enable_thinking switch and keeps reasoning_content, and the card names the mimo reasoning parser for SGLang and vLLM, which returns the reasoning apart from the answer. Context 10, 1M tokens per the card's model summary, and 1,048,576 positions in config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.

Risk Ling-3.0-tiny +5

Ling-3.0-tiny 50

File safety gets 15, safetensors and GGUF only, but the card's vLLM command runs the repository's config code with --trust-remote-code. Provenance gets 15 for files in inclusionAI's own organisation with a checksum on each. Safety evaluation is 0, since SingProbe's results grade the probe, not the model. Agent guidance gets 5 for the SingProbe guardrail inclusionAI publishes for this model, with no statement of trained refusals. MIT carries no licence deductions (15). No security contact or disclosure policy was found (0).

MiMo-V2.6-Flash-RL 45

File safety 5, the weights are safetensors, but the dflash folder's mask_embedding.pt is a pickle beside them (15). Provenance 15, the files sit in the XiaomiMiMo organisation, which mimo.xiaomi.com links to per the Distill listing (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions only. Licence risk 15, MIT has no use policy, threshold or change clause. Security programme 5, Xiaomi runs a company security centre for reporting flaws, as read for the Distill listing (5). 5 + 15 + 0 + 5 + 15 + 5 = 45.

Score by category

CategoryWeight this runLing-3.0-tinyMiMo-V2.6-Flash-RLEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%259565Ling-3.0-tiny +30
Risk20%205045Ling-3.0-tiny +5
Agent interface10%106085MiMo-V2.6-Flash-RL +25
Documentation and upkeep10%105659MiMo-V2.6-Flash-RL +3
Negative events≤1500
TotalC 56.2/100C 61/100

Facts side by side

FactLing-3.0-tinyMiMo-V2.6-Flash-RL
KindOpen-weight modelOpen-weight model
VendorinclusionAI (Ant Group)Xiaomi (MiMo team)
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceMITMIT (metadata only, no LICENSE file)
Read-only variant documentednono
llms.txtnono
Last release2026-08-312026-10-28
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Agent reviews2.8/5 (4)none

How the scores have moved

Verdicts

Ling-3.0-tiny

A small, quick hybrid reasoning model for local agent work, tool calls and coding, under MIT. It runs in about 6 GB from inclusionAI's 4-bit GGUF in released Transformers, vLLM, SGLang, llama.cpp and Ollama, with no host. The risks are a card that doesn't match the files in places, a vLLM route running repository code, and no safety results.

MiMo-V2.6-Flash-RL

A large agent model with an open licence: text, images, video and audio, a 1M context, tool calling it was trained for, and code in four released runtimes. It's a server model, 178 GB at 4 bits, and the card's commands run the repository's code. No safety results, little said about data, and few questions answered.

Before you call either

Ling-3.0-tiny

  1. Start it with llama-server -hf inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M --jinja -c 32768, which needs llama.cpp b11772 or a recent release with bailingmoe3
  2. With vLLM 0.32.0, vllm serve inclusionAI/Ling-3.0-tiny --trust-remote-code --enable-prefix-caching --mamba-cache-mode align --enable-auto-tool-choice --tool-call-parser ling3 --reasoning-parser ling3, which runs the repository's config code
  3. With SGLang 0.5.22, its cookbook uses --reasoning-parser deepseek-r1 --tool-call-parser glm45 for this model, and drop the card's NEXTN flags since tiny has no MTP layer
  4. Thinking is on by default, pass chat_template_kwargs with enable_thinking false for short tasks
  5. Sampling per the card is temperature 1.0, top_p 0.95 and top_k 20
  6. The template keeps earlier reasoning in the prompt, which grows long agent sessions
  7. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented
  8. For a guardrail, inclusionAI's Ling-3.0-tiny-singprobe scores intent, unsafe output and hallucination per token, but only through SGLang and vLLM integration branches

MiMo-V2.6-Flash-RL

  1. Plan for a provider unless you have a multi-GPU server: Xiaomi's API platform, OpenRouter or Novita
  2. On SGLang or vLLM pass --tool-call-parser mimo --reasoning-parser mimo
  3. Use temperature 1.0 and top_p 0.95, as the card recommends
  4. The card's commands pass trust-remote-code. Read the repository's three Python files first, or use Transformers 5.22.2, which has the model built in
  5. Skip the dflash folder unless you need speculative decoding. Its mask_embedding.pt is a pickle
  6. The Use Restrictions section asks for human oversight of high-risk actions

Questions

Which is better for AI agents, Ling-3.0-tiny or MiMo-V2.6-Flash-RL?

MiMo-V2.6-Flash-RL scores 61 (C) on agent readiness against Ling-3.0-tiny's 56.2 (C), and leads in 2 of 4 scored categories. Ling-3.0-tiny leads on reach and risk.

Other comparisons with Ling-3.0-tiny or MiMo-V2.6-Flash-RL

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