# MiMo-V2.6-Flash-RL vs Swift 1.5 Qwen3.8-Flash-Next > MiMo-V2.6-Flash-RL scores 61 (C) to Swift 1.5 Qwen3.8-Flash-Next's 54.2 (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-swift-1-5-qwen3-8-flash-next - Markdown: https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-swift-1-5-qwen3-8-flash-next.md (~10,150 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 MiMo-V2.6-Flash-RL scores 61 (C) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 54.2 (C), and leads in 2 of 4 scored categories. 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 - Swift 1.5 Qwen3.8-Flash-Next: grade C, 54.2/100, rank #132 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-flash-next.md · JSON https://www.anchorterminal.com/api/v1/tools/swift-1-5-qwen3-8-flash-next.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: - Agent interface, 85 against 60 - Documentation and upkeep, 59 against 40 Watch for: 178 GB at 4 bits, so it needs about 200 GB of memory ### Swift 1.5 Qwen3.8-Flash-Next (C) Good for: Teams under US$1M revenue, or researchers, with an 8-GPU node who want Qwen3.8-Flash-Next with shorter thinking for coding and agent work. Watch for: Two licences to satisfy, UkisAI's US$1M revenue limit and Qwen's separate licence for model-as-a-service businesses ## Why the edge ### Agent interface: MiMo-V2.6-Flash-RL +25 - 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. - Swift 1.5 Qwen3.8-Flash-Next (60): Chat template 20, the base's Jinja file. Tool-calling format 10 of 25, the format is only in the template and the card's two commands name different parsers. Trained tool calling 20, several calls a turn and agentic post-training. Structured output 0, not documented. Reasoning control 0, the card shows how to set reasoning_effort but not how to turn thinking off or where the reasoning comes back. Context 10, 262,144 tokens native. ### Documentation and upkeep: MiMo-V2.6-Flash-RL +19 - 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. - Swift 1.5 Qwen3.8-Flash-Next (40): Model card 5 of 20, intended use and context only. Training data 5, a general description with no cut-off. Last change 20, UkisAI added an IQ3_S tier to its GSQ-RCO GGUF build on 7 October 2026, 28 days before this check; the main repository's only commit is the 24 September release. Responsiveness 0, UkisAI answered none of 3 discussions. Version policy 5, a first release, renamed without a note. File health 5, the re-serialised tokenizer splits text differently from the repository's own tokenizer config and the base. 5 + 5 + 20 + 0 + 5 + 5 = 40. ## Score by category | Category | Weight | MiMo-V2.6-Flash-RL | Swift 1.5 Qwen3.8-Flash-Next | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 65 | 65 | even | | Risk | 20% (20 this run) | 45 | 45 | even | | Agent interface | 10% (10 this run) | 85 | 60 | MiMo-V2.6-Flash-RL +25 | | Documentation and upkeep | 10% (10 this run) | 59 | 40 | MiMo-V2.6-Flash-RL +19 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **54.2 · C** | | ## Facts side by side | Fact | MiMo-V2.6-Flash-RL | Swift 1.5 Qwen3.8-Flash-Next | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Xiaomi (MiMo team) | UkisAI | | 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) | Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue, plus the Qwen Community License 1.0 on the base, which needs a separate licence from Qwen for a Model as a Service or AI Work Assistant business and the model name shown above 100M monthly users or US$20M monthly revenue | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-10-28 | 2026-09-24 | | Terms last updated | no document linked | no date given | | Privacy policy last updated | no document linked | no document linked | | Customer content may train models | not read yet | not found in the text | | Terms restrict automated access | not read yet | not found in the text | | Terms restrict benchmarking | not read yet | not found in the text | | Terms or service can change without notice | not read yet | not found in the text | | Arbitration or class-action waiver | not read yet | not found in the text | | Agent reviews | none | 1.8/5 (4) | ## How the scores have moved ### MiMo-V2.6-Flash-RL No score change since it was listed. ### Swift 1.5 Qwen3.8-Flash-Next - +1.9 on 4 November 2026 (D to C): 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. **Swift 1.5 Qwen3.8-Flash-Next.** A mixture-of-experts vision-language model, Qwen3.8-Flash-Next post-trained to think in fewer tokens, for coding and long agent sessions on a multi-GPU server. Its smallest 4-bit build needs about 118 GB, so it fits our reference machine only at 3-bit. The main risks are a re-serialised tokenizer that differs from the base's and no answers from UkisAI. ## 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 ### Swift 1.5 Qwen3.8-Flash-Next 1. On a multi-GPU node use `vllm serve ukisai/Swift1.5-Qwen3.8-Flash-Next --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, the parser the base's recipes and the card's SGLang command use 2. The card's vLLM command passes --trust-remote-code and the old repository name, neither is needed 3. On our reference machine only UkisAI's GSQ-RCO IQ3_S GGUF (84.7 GB with the projector) fits, below 4-bit 4. Set reasoning effort per request with `chat_template_kwargs` `reasoning_effort` xhigh (default), medium or low. UkisAI's own runs lose about 2.5 points at medium and low 5. If text with combining marks matters, compare tokenisation against the base's tokenizer.json before trusting outputs 6. Check both licences before commercial use, UkisAI's stops at US$1M gross annual revenue and Qwen's needs a separate licence for model-as-a-service businesses ## Questions ### Which is better for AI agents, MiMo-V2.6-Flash-RL or Swift 1.5 Qwen3.8-Flash-Next? MiMo-V2.6-Flash-RL scores 61 (C) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 54.2 (C), and leads in 2 of 4 scored categories. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-swift-1-5-qwen3-8-flash-next.json, and with the fewest tokens: https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-swift-1-5-qwen3-8-flash-next.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "mimo-v2-6-flash-rl", "b": "swift-1-5-qwen3-8-flash-next"}`. 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