# DeepSeek V4.1 Flash vs MiMo-V2.6-Flash-RL > DeepSeek V4.1 Flash and MiMo-V2.6-Flash-RL score within a point of each other for open-weight models you run yourself, 61.5 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-mimo-v2-6-flash-rl - Markdown: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-mimo-v2-6-flash-rl.md (~10,300 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 DeepSeek V4.1 Flash and MiMo-V2.6-Flash-RL score within a point of each other on agent readiness, 61.5 (C) and 61 (C). MiMo-V2.6-Flash-RL leads on reach, agent interface and documentation and upkeep. Both do open-weight models you run yourself. - DeepSeek V4.1 Flash: grade C, 61.5/100, rank #35 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/deepseek-v4-1-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/deepseek-v4-1-flash.json - 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 - 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 ### DeepSeek V4.1 Flash (C) Good for: Teams with a multi-GPU server who want DeepSeek's agent model under MIT, with the hosted API as a fallback. Ahead on: - Risk, 50 against 45 Watch for: 510.3 GB of files and no smaller build anywhere, so it won't fit the 107.4 GB reference machine ### 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 60 - Agent interface, 85 against 75 - Documentation and upkeep, 59 against 48 Watch for: 178 GB at 4 bits, so it needs about 200 GB of memory ## Why the edge ### Documentation and upkeep: MiMo-V2.6-Flash-RL +11 - DeepSeek V4.1 Flash (48): The cards cover architecture, parameters and context but not intended use, limitations, languages or a cut-off, so 8. Training data is a general description with no cut-off, which caps it at 5. The files changed on 16 and 17 October and DeepSeek's deepseek-recipe 0.2.0 changed the V4.1 prompt encoding on 20 October, inside 30 days (20). None of the 40 most recent questions across both repositories had a DeepSeek reply (0). Every release keeps its own repository and the update names what it replaces (10). File health loses 5 for an open, unanswered pull request saying the September template trails the encoding changes. 8 + 5 + 20 + 0 + 10 + 5 = 48. - 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. ### Agent interface: MiMo-V2.6-Flash-RL +10 - DeepSeek V4.1 Flash (75): There's no Jinja template in the 17 October repository, only code to run, so the template line gets 10. DeepSeek documents the DSML tool format both ways and ships its own parser in encoding.py and deepseek-recipe, but names no parser in a runtime (10, read as for DeepSeek-V3.2 and V4-Flash-0731), and one block can carry several calls (20). deepseek-recipe documents JSON Schema output via response_format (15). Thinking turns off in chat mode and takes an effort budget from 1 to 100, with reasoning returned in think tags (10). The 1M context was trained in (10). - 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. ### Reach: MiMo-V2.6-Flash-RL +5 - DeepSeek V4.1 Flash (60): Memory scores nothing, because the only build is DeepSeek's own at 510.3 GB, about 612 GB with the fifth added, and nobody's runtime library holds a smaller one at 4-bit or better. Four runtimes run it in a released version (5 each), vLLM 0.33.0, SGLang 0.5.23, which maps the V4.1 architecture name onto its V4 model, llama.cpp from b11870 (25 October 2026), which added the deepseek41 architecture and its converter, and Ollama 0.40.14, which builds llama.cpp b11963. Transformers 5.22.1 and LM Studio don't (0 each). The release itself is one quantised build, FP8 with FP4 experts (10). MIT with nothing added earns the full terms line (15), the repositories aren't gated (10), and Hugging Face maps three live inference providers for the update (5). 0 + 20 + 10 + 15 + 10 + 5 = 60. - 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. ### Risk: DeepSeek V4.1 Flash +5 - DeepSeek V4.1 Flash (50): The weights are safetensors only, but 10 comes off because the card's way to build prompts is running encoding.py from the repository, since there's no Jinja template. The files sit in DeepSeek's own organisation with a hash for every file. Nothing is published on safety and there's no guidance for agent use, so those two lines score 0. The security programme earns 5 for the security@deepseek.com reporting address in DeepSeek's site footer, read as for DeepSeek-V3.2, with no security.txt and no public advisories. The MIT licence carries no use policy, threshold or revocation, so licence risk keeps all 15. - 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 | Category | Weight | DeepSeek V4.1 Flash | MiMo-V2.6-Flash-RL | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 60 | 65 | MiMo-V2.6-Flash-RL +5 | | Risk | 20% (20 this run) | 50 | 45 | DeepSeek V4.1 Flash +5 | | Agent interface | 10% (10 this run) | 75 | 85 | MiMo-V2.6-Flash-RL +10 | | Documentation and upkeep | 10% (10 this run) | 48 | 59 | MiMo-V2.6-Flash-RL +11 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61.5 · C** | **61 · C** | | ## Facts side by side | Fact | DeepSeek V4.1 Flash | MiMo-V2.6-Flash-RL | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | DeepSeek | Xiaomi (MiMo team) | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | MIT | MIT (metadata only, no LICENSE file) | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-10-17 | 2026-10-28 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | no document linked | | Agent reviews | 2.8/5 (4) | none | ## How the scores have moved ### DeepSeek V4.1 Flash - +2.5 on 4 November 2026: product - -3.8 on 21 October 2026 (B to C): checklist ### MiMo-V2.6-Flash-RL No score change since it was listed. ## Verdicts **DeepSeek V4.1 Flash.** A large multimodal agent model with a 1M-token context, documented tool calls, JSON Schema output and a reasoning budget, under MIT. It needs about 612 GB of memory across several GPUs and runs in vLLM, SGLang and llama.cpp. The main risks are prompts built from DeepSeek's code instead of a template, no published safety results and no DeepSeek replies. **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 ### DeepSeek V4.1 Flash 1. DeepSeek's reference run is its inference folder, convert.py with --model-parallel 8 --expert-dtype fp4, then torchrun --nproc-per-node 8 generate.py --ckpt-path "$SAVE_PATH" --config config.json --interactive 2. In vLLM 0.32.0 the parsers are named deepseek_v41 for both --tool-call-parser and --reasoning-parser. That's vLLM's naming, DeepSeek doesn't document a vLLM command 3. In llama.cpp, convert with convert_hf_to_gguf.py from b11870 or later, which added the deepseek41 architecture on 25 October 2026; no GGUF is published by DeepSeek 4. Build prompts with encoding.py or pip install deepseek-recipe, not the September repository's chat_template.jinja, which an open pull request says trails the 16 October encoding 5. Tool calls come back in a DSML calls block with a space before calls, invoke and parameter, unlike V4 6. Set reasoning_effort to 1 to 100, or low, high or max (50, 75, 100), in thinking mode only. Default is 75 7. Sample at temperature 1.0 and top_p 1.0 and allow 256K or more output tokens, as the card recommends 8. Without the hardware, call DeepSeek's API as deepseek-flash, which serves this exact update ### 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, DeepSeek V4.1 Flash or MiMo-V2.6-Flash-RL? DeepSeek V4.1 Flash and MiMo-V2.6-Flash-RL score within a point of each other on agent readiness, 61.5 (C) and 61 (C). MiMo-V2.6-Flash-RL leads on reach, agent interface and documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-mimo-v2-6-flash-rl.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-mimo-v2-6-flash-rl.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "deepseek-v4-1-flash", "b": "mimo-v2-6-flash-rl"}`. From a terminal: `anchor compare deepseek-v4-1-flash mimo-v2-6-flash-rl` - Each listing in full: https://www.anchorterminal.com/api/v1/tools/deepseek-v4-1-flash.json and https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-flash-rl.json ## Other comparisons with DeepSeek V4.1 Flash or MiMo-V2.6-Flash-RL - [Agents-A1-4B vs DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v4-1-flash.md) - [Agents-A1-4B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/agents-a1-4b-vs-mimo-v2-6-flash-rl.md) - [Apertus v1.5 8B vs DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-1-flash.md) - [Apertus v1.5 8B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-mimo-v2-6-flash-rl.md) - [Apodex-1.1-mini vs DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-deepseek-v4-1-flash.md) - [Apodex-1.1-mini vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-mimo-v2-6-flash-rl.md) - 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