Head to head · Open-weight models you run yourself · October 2026 research run
MiMo-V2.6-Flash-RL vs Nemotron 3.5 Lightning 30B A3B
Nemotron 3.5 Lightning 30B A3B scores 64.7 (B) on agent readiness against MiMo-V2.6-Flash-RL's 61 (C), and leads in every scored category. 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 GPU server, or a provider account, that want an open agent model with a very long context
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
178 GB at 4 bits, so it needs about 200 GB of memory
Nemotron 3.5 Lightning 30B A3B B
Best for Agents on one 24 to 32 GB GPU or Mac that want a fast tool-calling reasoning model with long context and a permissive licence
Ahead on
- Reach, 85 against 65
- Risk, 75 against 45
- Agent interface, 90 against 85
- Documentation and upkeep, 75 against 59
Watch for
No safety evaluation results published
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.
Risk Nemotron 3.5 Lightning 30B A3B +30
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.
Nemotron 3.5 Lightning 30B A3B 75
File safety earns 25, safetensors only with no Python files, and nothing from the repository runs to load it or parse its output. Provenance earns 15, files in NVIDIA's linked organisation with a hash for every file. Safety evaluation earns 0, since NVIDIA says it evaluated content safety but publishes no results. Agent guidance earns 10, a subcard that names prompt injection and the safeguards to add, without an instruction hierarchy. Licence risk keeps all 15 under OpenMDW-1.1. The security programme earns 10 for the reporting route on the card and NVIDIA's public bulletins.
Reach Nemotron 3.5 Lightning 30B A3B +20
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.
Nemotron 3.5 Lightning 30B A3B 85
Memory earns 25, since ggml-org's 18.9 GB Q4_0 GGUF and Ollama's 19 GB nvfp4 build both need under 24 GB with the fifth added. Runtimes earn the full 25 from released vLLM, SGLang, llama.cpp, Transformers, Ollama and LM Studio. Builds earn 10, as NVIDIA's only quantised build is NVFP4. Terms earn 10, because OpenMDW-1.1 is permissive with no threshold or use policy but isn't an OSI licence. Download earns 10, with no gate or account. Hosted earns 5, since NVIDIA's own API serves it and Fireworks hosts it.
Documentation and upkeep Nemotron 3.5 Lightning 30B A3B +16
MiMo-V2.6-Flash-RL 59
Model card 14, Intended use, the architecture, the parameters, the context and the languages (in the metadata) are on the card. Training data 5, A description of the training method with no data sources, sizes or cut-off. Last change 20, the last commits, on 28 October 2026, updated the card, 13 days before this check. Responsiveness 5, Of the 13 most recent discussions that aren't pull requests (21 September to 6 November 2026), 3 have a reply from a XiaomiMiMo member, which is a few. Version policy 5, MiMo-V2.5 stays in its own repositories and V2.6 came out in new ones, with no dated note on the older cards that we read. File health 10, Config, template and card agree on the architecture and the 1M context, and two early faults (invalid JSON in the draft model's config, preprocessor values) were fixed in commits that say so. 14 + 5 + 20 + 5 + 5 + 10 = 59.
Nemotron 3.5 Lightning 30B A3B 75
The model card earns 20, all seven items with limitations in the linked subcard. Training data earns 20, datasets listed with sources and token counts, cut-offs and filtering, and the corpora published. Last change earns 20, counting NVIDIA's own inference code as for the other Nemotron listings. The repositories last changed on 10 September, 53 days ago, and TensorRT-LLM, which the card's serving section runs, changed its Nemotron-H model code on 14 October, 19 days ago. Responsiveness earns 5, one NVIDIA reply across 14 questions. Version policy earns 5 as a first release. File health earns 5, since reports about the card's recipes wait without a reply and the llama.cpp recipe names a quantisation the linked repository doesn't hold.
Agent interface Nemotron 3.5 Lightning 30B A3B +5
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.
Nemotron 3.5 Lightning 30B A3B 90
The chat template earns 20, a Jinja file in the repository. The tool-calling format earns 25, since the card names the qwen3_coder parser and shows a tool call declared and read back. Trained tool calling earns 20, with tool-use training and several calls per turn in the template. Structured output earns 5, claimed from training with no instructions. Reasoning control earns 10, thinking off per request with runtime parsers that return the reasoning apart. Context earns 10 for up to 1M tokens.
Score by category
| Category | Weight this run | MiMo-V2.6-Flash-RL | Nemotron 3.5 Lightning 30B A3B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 65 | 85 | Nemotron 3.5 Lightning 30B A3B +20 |
| Risk | 20%20 | 45 | 75 | Nemotron 3.5 Lightning 30B A3B +30 |
| Agent interface | 10%10 | 85 | 90 | Nemotron 3.5 Lightning 30B A3B +5 |
| Documentation and upkeep | 10%10 | 59 | 75 | Nemotron 3.5 Lightning 30B A3B +16 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | B 64.7/100 |
Facts side by side
| Fact | MiMo-V2.6-Flash-RL | Nemotron 3.5 Lightning 30B A3B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Xiaomi (MiMo team) | NVIDIA |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT (metadata only, no LICENSE file) | OpenMDW-1.1 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-28 | 2026-08-11 |
| 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
MiMo-V2.6-Flash-RL
No score change since it was listed.
Verdicts
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.
Nemotron 3.5 Lightning 30B A3B
A small agent cousin, 3B active parameters, with trained parallel tool calls, a thinking switch and up to 1M tokens of context, under a permissive licence. It runs in about 23 GB from the GGUF or Ollama builds, and NVIDIA's NVFP4 build needs NVIDIA GPUs. The main risks are no published safety results and one answered question across two repositories.
Before you call either
MiMo-V2.6-Flash-RL
- Plan for a provider unless you have a multi-GPU server: Xiaomi's API platform, OpenRouter or Novita
- On SGLang or vLLM pass
--tool-call-parser mimo --reasoning-parser mimo - Use temperature 1.0 and top_p 0.95, as the card recommends
- 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
- Skip the dflash folder unless you need speculative decoding. Its mask_embedding.pt is a pickle
- The Use Restrictions section asks for human oversight of high-risk actions
Nemotron 3.5 Lightning 30B A3B
- Quickest start is
ollama run nemotron-3.5-lightning, about 19 to 25 GB of files depending on the platform - On the reference machine use
llama-server -hf ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_0 --jinja, since the card's Q4_K_M tag doesn't exist there and NVFP4 needs NVIDIA GPUs - With vLLM serve it with
--reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder(SGLang uses--reasoning-parser nemotron_3) - Use
temperature=1.0andtop_p=0.95, as the card says - Thinking is on by default. Pass
chat_template_kwargswithenable_thinkingfalse for short tasks - For coding agents pass
force_nonempty_content: trueinchat_template_kwargs, which vLLM's nemotron_v3 parser reads so a cut-off reasoning trace still returns content - Go past 262,144 tokens only with the card's flags (
VLLM_ALLOW_LONG_MAX_MODEL_LEN=1,--max-model-len 1048576) and enough memory
Questions
Which is better for AI agents, MiMo-V2.6-Flash-RL or Nemotron 3.5 Lightning 30B A3B?
Nemotron 3.5 Lightning 30B A3B scores 64.7 (B) on agent readiness against MiMo-V2.6-Flash-RL's 61 (C), and leads in every scored category.
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- Nemotron 3.5 Lightning 30B A3B vs Qwen3-Omni-30B-A3B-Instruct
- Nemotron 3.5 Lightning 30B A3B vs Qwen3-VL-2B-Instruct
- Nemotron 3.5 Lightning 30B A3B vs Qwen3-VL-30B-A3B-Instruct
- Nemotron 3.5 Lightning 30B A3B vs Qwen3-VL-32B-Instruct
- Nemotron 3.5 Lightning 30B A3B vs Qwen3-VL-4B-Instruct
- Nemotron 3.5 Lightning 30B A3B vs Qwen3-VL-8B-Instruct
- Nemotron 3.5 Lightning 30B A3B vs Qwopus3.8-27B-Flash-V2
- Nemotron 3.5 Lightning 30B A3B vs Recursive Flint
- Nemotron 3.5 Lightning 30B A3B vs Ripple-65B Preview
- Nemotron 3.5 Lightning 30B A3B vs SenseNova-Loop-9B-Preview
- Nemotron 3.5 Lightning 30B A3B vs SmolLM3-3B
- Nemotron 3.5 Lightning 30B A3B vs Spark-X2.6-4B
- Nemotron 3.5 Lightning 30B A3B vs Step 3.7 Flash
- Nemotron 3.5 Lightning 30B A3B vs Swift 1.5 Qwen3.8-27B
- Nemotron 3.5 Lightning 30B A3B vs Swift 1.5 Qwen3.8-Flash-Next
- Nemotron 3.5 Lightning 30B A3B vs ThinkingCap (Qwen3.8 27B)
- Nemotron 3.5 Lightning 30B A3B vs Baidu Unlimited-OCR-2
- Nemotron 3.5 Lightning 30B A3B vs WeVisOne-VL-2B
- Nemotron 3.5 Lightning 30B A3B vs WeVisOne-VL-4B
- Nemotron 3.5 Lightning 30B A3B vs ZAYA1-8B
Machine-readable
- This page as Markdown
/compare/mimo-v2-6-flash-rl-vs-nemotron-3-5-lightning-30b-a3b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/mimo-v2-6-flash-rl.json·/api/v1/tools/nemotron-3-5-lightning-30b-a3b.json - From a terminal
anchor compare mimo-v2-6-flash-rl nemotron-3-5-lightning-30b-a3b(the CLI) - Over MCP
compare_tools {"a": "mimo-v2-6-flash-rl", "b": "nemotron-3-5-lightning-30b-a3b"}at/mcp, no key