Head to head · Open-weight models you run yourself · October 2026 research run
EXAONE 4.5 33B vs MiMo-V2.6 Distill Qwen 9B
EXAONE 4.5 33B and MiMo-V2.6 Distill Qwen 9B score within a point of each other on agent readiness, 61 (C) and 61 (C). MiMo-V2.6 Distill Qwen 9B leads on reach and risk. 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 Researchers and students working on documents, Korean or multimodal agents who don't need commercial rights
Ahead on
- Agent interface, 85 against 70
- Documentation and upkeep, 50 against 40
Watch for
Research and education only. No commercial use of the model or its output
Best for Researchers and agents that want a small coding and terminal-agent model to fine-tune further on a 24 GB GPU, or to run at 4-bit through Ollama in about 8 GB.
Ahead on
- Reach, 80 against 65
- Risk, 65 against 50
Watch for
Only BF16 from Xiaomi, about 23 GB to run; the smaller builds are Ollama's
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.
Reach MiMo-V2.6 Distill Qwen 9B +15
EXAONE 4.5 33B 65
Memory 20, the smallest build at 4 bits is LG's IQ4_XS GGUF, 18.03 GB with the 2.57 GB projector, and 20.60 GB × 1.2 = 24.7 GB, within 32 GB. Without the projector, for text only, it would fit 24 GB. Runtimes 15, Released vLLM 0.33.0 has exaone4_5.py, Transformers 5.22.2 has the exaone4_5 model, and llama.cpp b12242 has the architecture, with LG's own GGUF. Three at 5 each. Builds 15, LG publishes GGUF at five quantised precisions, an AWQ build and an FP8 build. Terms 5, Research and educational use only. Download 10, the Hub API reports gated false for all four repositories on 10 November 2026, so the files download with no account and no click-through, though the licence says downloading is acceptance. Hosted 0, Hugging Face maps no inference provider to it, and the card names no API that serves it. 20 + 15 + 15 + 5 + 10 + 0 = 65.
MiMo-V2.6 Distill Qwen 9B 80
Memory 30 of 30, Ollama's library build at q4_K_M is 6.6 GB, 7.9 GB with a fifth added, while Xiaomi's only build is BF16 at 18.85 GB. Runtimes 25, it keeps Qwen3.5-9B's architecture, which released vLLM, SGLang, Transformers and llama.cpp run, and Ollama's own library carries this model (entry updated 3 November 2026; the 21 October check found none). Builds 0, Xiaomi publishes no quantised build. Terms 15, MIT on the card over an Apache 2.0 base, both with nothing added. Download 10, not gated. Hosted 0, no provider serves this distill.
Risk MiMo-V2.6 Distill Qwen 9B +15
EXAONE 4.5 33B 50
File safety 25, safetensors and GGUF only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the LGAI-EXAONE organisation (LG AI Research on the Hub), which the card's GitHub repository shares a name with and the licence in the repository is LG's (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions: the limitation section lists inappropriate, biased, incorrect and outdated answers and tells users not to try to induce harmful output. Licence risk 5, 15 less 5 for field-of-use limits beyond the law (no commercial use, no building competing models) and 5 for terms the licensor can change or revoke (LG may amend the agreement at any time, and the licence is revocable). Security programme 0, no way to report a flaw on the card beyond a support contact, and www.lgresearch.ai has no security.txt. 25 + 15 + 0 + 5 + 5 + 0 = 50.
MiMo-V2.6 Distill Qwen 9B 65
File safety 25, safetensors only and no repository code runs. Provenance 15, the files are in Xiaomi's MiMo organisation, which mimo.xiaomi.com links to, with a checksum on every file. Safety evaluation 0, the technical report has no safety results. Agent guidance 5, the card's Use Restrictions section (28 October 2026) gives general cautions, with nothing on prompt injection, refusals or safeguards. Licence risk 15, MIT and Apache 2.0 with no conditions, and the card says its restrictions add none. Security programme 5 of 10, Xiaomi's company security centre takes reports, but nothing links it to MiMo and no advisories were found.
Agent interface EXAONE 4.5 33B +15
EXAONE 4.5 33B 85
Chat template 20, chat_template.jinja in the repository (5,930 characters). Tool-calling format 25, the card's vLLM command names the parser, hermes with auto tool choice, and its agentic example calls tools from MCP servers through the OpenAI-compatible API, so declaration and return follow that API. Trained tool calling 20, the card reports agentic tool use among its language results and shows an agent run with several tool servers, 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, enable_thinking is on by default and the card says to set it false for the non-reasoning mode, with an example of each. Context 10, 262,144 tokens per the card's configuration table and config.json, and the card's serve commands run at that length. 20 + 25 + 20 + 0 + 10 + 10 = 85.
MiMo-V2.6 Distill Qwen 9B 70
Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, the format is only in the template and the card names no tool parser. Trained tool calling 20, several calls a turn and agentic fine-tuning. Structured output 0, not documented. Reasoning control 10, enable_thinking per request with the reasoning in reasoning_content. Context 10, 262,144 tokens from the base's native length, though the card doesn't say.
Documentation and upkeep EXAONE 4.5 33B +10
EXAONE 4.5 33B 50
Model card 20, Uses (the quickstart's question answering, document and agent examples), limitations, the architecture, the parameters, the context, the languages (in the metadata) and a knowledge cut-off of December 2024 are all on the card. Training data 5, A knowledge cut-off and one line on excluding personal, harmful and biased content, with no sources or sizes. Last change 10, the main repository last changed on 6 July 2026 (dependencies moved to official releases), 127 days before this check, and the FP8 repository on 9 August, 93 days. Responsiveness 0, none of the 5 most recent discussions that aren't pull requests (13 April to 4 May 2026) has a reply from an LGAI-EXAONE member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, EXAONE 4.0 and 4.0.1 stay in their own repositories and 4.5 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 262,144 tokens, and a July commit moved the dependencies to released versions. 20 + 5 + 10 + 0 + 5 + 10 = 50.
MiMo-V2.6 Distill Qwen 9B 40
Model card 5 of 20, intended use and parameters only. Training data 5, a domain mix with token counts but no sources or cut-off. Last change 20, README updates on 28 October 2026. Responsiveness 0, Xiaomi answered none of the 14 discussions. Version policy 5, a first release. File health 5, no LICENSE file, a config that lists an MTP layer the weights lack, and template and tool-call reports waiting without a reply.
Score by category
| Category | Weight this run | EXAONE 4.5 33B | MiMo-V2.6 Distill Qwen 9B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 65 | 80 | MiMo-V2.6 Distill Qwen 9B +15 |
| Risk | 20%20 | 50 | 65 | MiMo-V2.6 Distill Qwen 9B +15 |
| Agent interface | 10%10 | 85 | 70 | EXAONE 4.5 33B +15 |
| Documentation and upkeep | 10%10 | 50 | 40 | EXAONE 4.5 33B +10 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 61/100 |
Facts side by side
| Fact | EXAONE 4.5 33B | MiMo-V2.6 Distill Qwen 9B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | LG AI Research | Xiaomi (MiMo team) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | EXAONE AI Model License Agreement 1.2 - NC (research and education only) | MIT (metadata only, no LICENSE file); built on Qwen3.5-9B under Apache 2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-07-06 | 2026-09-21 |
| Terms last updated | not read yet | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 2.3/5 (4) |
How the scores have moved
EXAONE 4.5 33B
No score change since it was listed.
Verdicts
EXAONE 4.5 33B
A well-documented vision-language model with a full set of builds, tool calling, switchable reasoning, a 256K context and a stated knowledge cut-off. The licence decides whether you can use it: research and education only, output included, and LG can change the terms. Nothing answered on the repository and no safety results.
MiMo-V2.6 Distill Qwen 9B
A 9B cousin tuned on MiMo-V2.6's agent work for coding, terminal tasks and visual coding, released as a base for RL research. Ollama's library build needs about 8 GB at 4-bit, while Xiaomi ships only BF16. The main risks are no safety results while a seventh of its tuning is offensive security, and nobody at Xiaomi answering reports.
Before you call either
EXAONE 4.5 33B
- Don't use it, or anything it writes, commercially. The licence covers the output too
- Serve it with vLLM and
--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser hermes - Set
enable_thinkingfalse for a direct answer. Thinking is on by default - Use temperature 1.0, top_p 0.95 and presence_penalty 1.5 in general, and temperature 0.6 with top_k 20 for documents and Korean
- For llama.cpp, load a projector file from LG's GGUF repository with the weights for image input
- Its knowledge stops at December 2024
MiMo-V2.6 Distill Qwen 9B
- Quickest start is
ollama run mimo-v2.6:9b, Ollama's 6.6 GB q4_K_M build with text and image input - Serve it as the card does,
sglang serve --model-path XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B --reasoning-parser mimo, and add--tool-call-parser mimofor parsed tool calls - SGLang's mimo reasoning parser expects thinking to be asked for, so pass
chat_template_kwargswithenable_thinkingtrue, as the card does, and readreasoning_content - Calls come back as function and parameter blocks inside tool_call, the same shape vLLM's and SGLang's qwen3_coder parsers read
- The generation config samples at temperature 0.6, top_k 20 and top_p 0.95
- Images and video go in through Qwen3.5's processor; the template's audio tokens have no audio encoder behind them
- The MTP head isn't in the files, so skip MTP speculative decoding even though config.json lists a layer
- It's an SFT checkpoint meant for further RL, and discussions report runaway generation on llama.cpp, so cap max_tokens
- If you redistribute the weights, include the Apache 2.0 text of Qwen3.5-9B, which section 4(a) asks for and the repository lacks
Questions
Which is better for AI agents, EXAONE 4.5 33B or MiMo-V2.6 Distill Qwen 9B?
EXAONE 4.5 33B and MiMo-V2.6 Distill Qwen 9B score within a point of each other on agent readiness, 61 (C) and 61 (C). MiMo-V2.6 Distill Qwen 9B leads on reach and risk.
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- MiMo-V2.6 Distill Qwen 9B vs Recursive Flint
- MiMo-V2.6 Distill Qwen 9B vs Ripple-65B Preview
- MiMo-V2.6 Distill Qwen 9B vs SenseNova-Loop-9B-Preview
- MiMo-V2.6 Distill Qwen 9B vs SmolLM3-3B
- MiMo-V2.6 Distill Qwen 9B vs Spark-X2.6-4B
- MiMo-V2.6 Distill Qwen 9B vs Step 3.7 Flash
- MiMo-V2.6 Distill Qwen 9B vs Swift 1.5 Qwen3.8-27B
- MiMo-V2.6 Distill Qwen 9B vs Swift 1.5 Qwen3.8-Flash-Next
- MiMo-V2.6 Distill Qwen 9B vs ThinkingCap (Qwen3.8 27B)
- MiMo-V2.6 Distill Qwen 9B vs Baidu Unlimited-OCR-2
- MiMo-V2.6 Distill Qwen 9B vs WeVisOne-VL-2B
- MiMo-V2.6 Distill Qwen 9B vs WeVisOne-VL-4B
- MiMo-V2.6 Distill Qwen 9B vs ZAYA1-8B
Machine-readable
- This page as Markdown
/compare/exaone-4-5-33b-vs-mimo-v2-6-distill-qwen-9b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/exaone-4-5-33b.json·/api/v1/tools/mimo-v2-6-distill-qwen-9b.json - From a terminal
anchor compare exaone-4-5-33b mimo-v2-6-distill-qwen-9b(the CLI) - Over MCP
compare_tools {"a": "exaone-4-5-33b", "b": "mimo-v2-6-distill-qwen-9b"}at/mcp, no key