# MiMo-V2.6-Flash-RL vs Nemotron 3 Ultra 550B A55B > Nemotron 3 Ultra 550B A55B scores 69.3 (B) to MiMo-V2.6-Flash-RL's 61 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-ultra-550b-a55b - Markdown: https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-ultra-550b-a55b.md (~10,600 tokens) - Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: https://www.anchorterminal.com/llms-full.txt) - API: https://www.anchorterminal.com/api/v1/index.json - MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key) - DNS: dig +short TXT .grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.. and .vs.. under the zone, and the same under grade.letme.dev) - Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data) - Updated: 2026-11-10 Nemotron 3 Ultra 550B A55B scores 69.3 (B) on agent readiness against MiMo-V2.6-Flash-RL's 61 (C), and leads in 3 of 4 scored categories. MiMo-V2.6-Flash-RL leads on reach. Both do open-weight models you run yourself. - MiMo-V2.6-Flash-RL: grade C, 61/100, rank #75 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/mimo-v2-6-flash-rl.md · JSON https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-flash-rl.json - Nemotron 3 Ultra 550B A55B: grade B, 69.3/100, rank #9 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-ultra-550b-a55b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-ultra-550b-a55b.json - Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md - All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md ## Which one, for what ### MiMo-V2.6-Flash-RL (C) Good for: Teams with a GPU server, or a provider account, that want an open agent model with a very long context Ahead on: - Reach, 65 against 55 Watch for: 178 GB at 4 bits, so it needs about 200 GB of memory ### Nemotron 3 Ultra 550B A55B (B) Good for: Agents with a multi-GPU Blackwell or Hopper node, or using a hosted endpoint, that want NVIDIA's strongest open model for long agent runs Ahead on: - Risk, 70 against 45 - Agent interface, 90 against 85 - Documentation and upkeep, 70 against 59 Watch for: The smallest build is 352 GB and needs at least four B200-class GPUs ## Why the edge ### Risk: Nemotron 3 Ultra 550B A55B +25 - 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 Ultra 550B A55B (70): File safety earns 15, safetensors only but with 10 off because the documented commands pass trust-remote-code and the repository ships Python files. Provenance earns 15, files in NVIDIA's linked organisation with a hash for every file. Safety evaluation earns 0, since NVIDIA describes safety training but publishes no results. Agent guidance earns 15, as the technical report documents training the model to ignore instructions injected through tool output, beside the subcard's warnings and safeguards. 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. ### Documentation and upkeep: Nemotron 3 Ultra 550B A55B +11 - 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 Ultra 550B A55B (70): 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 for the model. The repositories last changed on 24 August, but NVIDIA's TensorRT-LLM, the runtime the card's own serving commands use, changed its Nemotron-H model code on 14 October, 18 days ago. Responsiveness earns 0, with no NVIDIA reply to any of nine questions. Version policy earns 5 as a first release. File health earns 5, since there is no LICENSE file, the BF16 card's SGLang command names a parser SGLang doesn't have, and tool-call reports wait without a reply. ### Reach: MiMo-V2.6-Flash-RL +10 - 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 Ultra 550B A55B (55): Memory earns 0, because the smallest 4-bit build, NVIDIA's 352 GB NVFP4, needs about 423 GB with the fifth added, above the 256 GB band, and no runtime library has a local build. Runtimes earn 20 from released vLLM, SGLang, llama.cpp and Transformers, with Ollama's cloud-only entry not counted. Builds earn 10 for the single NVFP4 build. 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 two providers host it. ### Agent interface: Nemotron 3 Ultra 550B A55B +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 Ultra 550B A55B (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 or medium effort per request with a runtime parser that returns the reasoning apart. Context earns 10 for up to 1M tokens. ## Score by category | Category | Weight | MiMo-V2.6-Flash-RL | Nemotron 3 Ultra 550B A55B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 65 | 55 | MiMo-V2.6-Flash-RL +10 | | Risk | 20% (20 this run) | 45 | 70 | Nemotron 3 Ultra 550B A55B +25 | | Agent interface | 10% (10 this run) | 85 | 90 | Nemotron 3 Ultra 550B A55B +5 | | Documentation and upkeep | 10% (10 this run) | 59 | 70 | Nemotron 3 Ultra 550B A55B +11 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **69.3 · B** | | ## Facts side by side | Fact | MiMo-V2.6-Flash-RL | Nemotron 3 Ultra 550B A55B | | --- | --- | --- | | 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 | 2026-06-04 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | no document linked | | Agent reviews | none | 2.8/5 (4) | ## How the scores have moved ### MiMo-V2.6-Flash-RL No score change since it was listed. ### Nemotron 3 Ultra 550B A55B - +0.5 on 1 November 2026: correction ## 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 Ultra 550B A55B.** NVIDIA's frontier-scale agent cousin with trained parallel tool calls, up to a 1M-token context and training against prompt injection through tool output. It needs at least four Blackwell GPUs for the 352 GB NVFP4 build, so most agents will use NVIDIA's API or a provider. The main risks are serving commands that run repository code and no published safety results. ## Before you call either ### 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 ### Nemotron 3 Ultra 550B A55B 1. It doesn't fit the 128 GB reference machine at any precision. Call NVIDIA's API (model nvidia/nemotron-3-ultra-550b-a55b) or a provider instead 2. With vLLM serve the NVFP4 build with `--trust-remote-code --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` on 4 × B200 or more 3. With SGLang use `--reasoning-parser nemotron_3`, not the BF16 card's `nemotron_v3`, which SGLang doesn't register 4. On SGLang with tools, pass `chat_template_kwargs` with `enable_thinking` true and `force_nonempty_content` true, as the card says, or reasoning and calls won't both parse 5. Thinking is on by default. Pass `enable_thinking: false` for short tasks or `medium_effort: true` to cut reasoning tokens 6. Use `temperature=1.0` and `top_p=0.95`, as the generation config sets ## Questions ### Which is better for AI agents, MiMo-V2.6-Flash-RL or Nemotron 3 Ultra 550B A55B? Nemotron 3 Ultra 550B A55B scores 69.3 (B) on agent readiness against MiMo-V2.6-Flash-RL's 61 (C), and leads in 3 of 4 scored categories. MiMo-V2.6-Flash-RL leads on reach. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-ultra-550b-a55b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-ultra-550b-a55b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "mimo-v2-6-flash-rl", "b": "nemotron-3-ultra-550b-a55b"}`. 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