Head to head · Open-weight models you run yourself · October 2026 research run
Apertus v1.5 8B vs MiMo-V2.6-Flash-RL
Apertus v1.5 8B and MiMo-V2.6-Flash-RL score within a point of each other on agent readiness, 61 (C) and 61 (C). MiMo-V2.6-Flash-RL leads on reach. 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 Agents that want a fully open, multilingual 8B with image and audio input on one 24 GB GPU, and can run a publisher fork of vLLM.
Ahead on
- Risk, 75 against 45
- Documentation and upkeep, 84 against 59
Watch for
No released runtime supports it, only Swiss AI's forks of vLLM and Transformers
Best 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 40
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.
Risk Apertus v1.5 8B +30
Apertus v1.5 8B 75
File safety 25, safetensors only and no code in the repository. Provenance 15, the files are in the swiss-ai organisation the report links, with a checksum on every file. Safety evaluation 10 of 20, published safety benchmarks run with Swiss AI's open harnesses and early access for three outside evaluators, but the manual evaluation is Swiss AI's own and can't be rerun. Agent guidance 10, the report says what the refusals cover, how far jailbreaks get and that capability limits are the main defence, with a personal-data filter to apply, but nothing on prompt injection for deployers. Licence risk 10, 5 off for the use policy. Security programme 5, ETH Zurich's disclosure policy, with no advisories found.
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.
Reach MiMo-V2.6-Flash-RL +25
Apertus v1.5 8B 40
Memory 25 of 30, the BF16 files are 18.4 GB, or 22.1 GB with a fifth added, which fits 24 GB. Runtimes 0, no released vLLM (to 0.33.0), SGLang (to 0.5.23), Transformers (to 5.22.1), llama.cpp, Ollama or LM Studio runs version 1.5 yet, only Swiss AI's forks. Builds 0, Swiss AI publishes no quantised build. Terms 10 of 15, Apache 2.0 with an acceptable use policy added at the gate. Download 5, a click-through that needs an account and your contact details. Hosted 0, no inference provider or publisher API was found, and Swiss AI's documentation page names none on 3 November 2026.
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.
Documentation and upkeep Apertus v1.5 8B +25
Apertus v1.5 8B 84
Model card 14 of 20, languages and a knowledge cut-off are missing. Training data 20, every dataset is public and listed with versions, licences and counts. Last change 20, the card changed on 14 October 2026, 20 days before 3 November, and Swiss AI added its own evaluation-results files on 26 October and 3 November. Responsiveness 20, Swiss AI answered all three questions, unchanged on 3 November 2026. Version policy 5, earlier models stay in their own repositories without a dated note. File health 5, the gated config couldn't be checked against the card, and the card and the policy disagree on the output filter.
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.
Score by category
| Category | Weight this run | Apertus v1.5 8B | MiMo-V2.6-Flash-RL | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 40 | 65 | MiMo-V2.6-Flash-RL +25 |
| Risk | 20%20 | 75 | 45 | Apertus v1.5 8B +30 |
| Agent interface | 10%10 | 85 | 85 | even |
| Documentation and upkeep | 10%10 | 84 | 59 | Apertus v1.5 8B +25 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 61/100 |
Facts side by side
| Fact | Apertus v1.5 8B | MiMo-V2.6-Flash-RL |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Swiss AI Initiative (EPFL and ETH Zurich) | Xiaomi (MiMo team) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0, with the Apertus Acceptable Use Policy (v1.5, 14 July 2026) accepted at the download gate | MIT (metadata only, no LICENSE file) |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-14 | 2026-10-28 |
| Terms last updated | not read yet | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 2.8/5 (4) | none |
Verdicts
Apertus v1.5 8B
A fully open 8B cousin, training data included, for multilingual chat and tool calls with images and audio in. It needs about 22 GB in BF16 and Swiss AI's own vLLM or Transformers fork, since no released runtime supports it. The main risks are a gated download with a policy to pass on and tools that fail with thinking on.
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
Apertus v1.5 8B
- Accept the terms on the card and set HF_TOKEN before downloading
- Use Swiss AI's Docker image ghcr.io/swiss-ai/vllm_apertus_1.5_release, since stock vLLM and Transformers don't load Apertus1p5ForConditionalGeneration yet
- Serve with
--chat-template-content-format string --enable-auto-tool-choice --tool-call-parser apertusfor tools, and leave thinking off when you need tools - For thinking, set
--reasoning-parser apertusand--default-chat-template-kwargs.enable_thinking true, and budget max tokens generously - Audio is resampled to 24 kHz and costs 40 tokens a second, and video must be passed as frames
- Don't cast the loaded model with .half(), the image and audio tokenizers must stay in F32
- If you redistribute the weights, the policy says you must pass it on and get the recipient's express assent
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
Questions
Which is better for AI agents, Apertus v1.5 8B or MiMo-V2.6-Flash-RL?
Apertus v1.5 8B and MiMo-V2.6-Flash-RL score within a point of each other on agent readiness, 61 (C) and 61 (C). MiMo-V2.6-Flash-RL leads on reach.
Other comparisons with Apertus v1.5 8B or MiMo-V2.6-Flash-RL
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- MiMo-V2.6-Flash-RL vs Recursive Flint
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- MiMo-V2.6-Flash-RL vs WeVisOne-VL-2B
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Machine-readable
- This page as Markdown
/compare/apertus-v1-5-8b-vs-mimo-v2-6-flash-rl.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/apertus-v1-5-8b.json·/api/v1/tools/mimo-v2-6-flash-rl.json - From a terminal
anchor compare apertus-v1-5-8b mimo-v2-6-flash-rl(the CLI) - Over MCP
compare_tools {"a": "apertus-v1-5-8b", "b": "mimo-v2-6-flash-rl"}at/mcp, no key