Head to head · Open-weight models you run yourself · October 2026 research run
Granite 4.2 8B vs MiMo-V2.6-Flash-RL
Granite 4.2 8B and MiMo-V2.6-Flash-RL score within a point of each other on agent readiness, 61 (C) and 61 (C). 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 documented, signed, Apache 2.0 reasoning model with tool calling on a 16 GB machine
Ahead on
- Reach, 95 against 65
- Risk, 55 against 45
- Documentation and upkeep, 92 against 59
Watch for
No safety results published for it
Best for Teams with a GPU server, or a provider account, that want an open agent model with a very long context
No category where it leads by five points or more, and no fact that sets it apart.
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.
Documentation and upkeep Granite 4.2 8B +33
Granite 4.2 8B 92
Model card 17, Intended use (best for), limitations (ethical considerations and limitations), architecture, parameters (8B), context length (128K native) and 12 tested languages are on the card. Training data 20, IBM's disclosure file for the 8B lists the datasets used in pre-training and post-training as machine-readable metadata, and the card describes the three training stages. Last change 20, the last commit, on 20 October 2026, replaced model.safetensors.index.json (titled "granite-4.2 index file fix"), 21 days before this check. Responsiveness 20, Of the 5 discussions that aren't pull requests (13 September to 5 November 2026), 4 have a reply from an ibm-granite member. Version policy 5, Granite 4.1 8B stays in its own repository and 4.2 came out in new ones. File health 10, Config, template and card agree on the architecture, the 131,072-token context and the thinking switch. 17 + 20 + 20 + 20 + 5 + 10 = 92.
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.
Reach Granite 4.2 8B +30
Granite 4.2 8B 95
Memory 30, IBM's own 4-bit MLX build granite-4.2-8b-q4-mlx is 4.95 GB, and 4.95 GB × 1.2 = 5.9 GB, within 8 GB. Its Q4_0 GGUF is 5.06 GB and Ollama's library build granite4.2:8b (5.3 GB) fits 8 GB too. Runtimes 25, Released versions run the granite architecture in llama.cpp (granite, b12242), vLLM (GraniteForCausalLM in the v0.33.0 registry), SGLang (granite.py at v0.5.23), Transformers (v5.22.2) and Ollama (its own granite4.2 library entry, 8b tag). Builds 15, IBM publishes FP8, MXFP4 and NVFP4 safetensors builds, a GGUF repository with Q4_0 to Q8_0 and BF16 files, and 4-bit, 8-bit and BF16 MLX builds, so quantised builds at two precisions or more. Terms 15, Apache 2.0, an OSI-approved licence, with no use policy or other terms added on the card. Download 10, the Hub API reports gated false for the BF16, FP8, MXFP4, NVFP4, GGUF and MLX repositories, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps one inference provider (DeepInfra, live), short of the two the line needs. 30 + 25 + 15 + 15 + 10 + 0 = 95.
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 Granite 4.2 8B +10
Granite 4.2 8B 55
File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the ibm-granite organisation, which IBM's Granite docs and the GitHub README link to (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions. Licence risk 15, Apache 2.0 has no use policy, field-of-use limit, threshold or change clause, and the card's licence link resolves. Security programme 5, www.ibm.com/.well-known/security.txt names IBM PSIRT, read for the 3B listing on 4 November 2026 (5). 15 + 15 + 0 + 5 + 15 + 5 = 55.
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 this run | Granite 4.2 8B | MiMo-V2.6-Flash-RL | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 95 | 65 | Granite 4.2 8B +30 |
| Risk | 20%20 | 55 | 45 | Granite 4.2 8B +10 |
| Agent interface | 10%10 | 85 | 85 | even |
| Documentation and upkeep | 10%10 | 92 | 59 | Granite 4.2 8B +33 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 61/100 |
Facts side by side
| Fact | Granite 4.2 8B | MiMo-V2.6-Flash-RL |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | IBM | 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 | MIT (metadata only, no LICENSE file) |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-20 | 2026-10-28 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
Verdicts
Granite 4.2 8B
IBM's mid-size reasoning cousin, with the same strengths as the 3B. It runs in every runtime we count from IBM's own builds on a 16 GB machine, is signed, plain Apache 2.0, and comes with a disclosure of its training data. No safety results are published and structured output isn't documented.
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
Granite 4.2 8B
- Start it with
ollama run granite4.2:8b, orllama-server -hf ibm-granite/granite-4.2-8b-GGUF:Q4_K_M --jinja - With vLLM 0.32.0 or later leave out
--reasoning-parser-plugin, since the parser is built in, and use--tool-call-parser qwen3_coder - Thinking is on by default. Pass
enable_thinkingfalse to turn it off orlow_efforttrue for short reasoning - The context is 131,072 tokens. The card's 512K extension isn't documented
- Validate JSON output yourself, since structured output isn't documented
- IBM recommends Granite Guardian beside it to flag risky inputs and outputs
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, Granite 4.2 8B or MiMo-V2.6-Flash-RL?
Granite 4.2 8B and MiMo-V2.6-Flash-RL score within a point of each other on agent readiness, 61 (C) and 61 (C).
Other comparisons with Granite 4.2 8B or MiMo-V2.6-Flash-RL
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- GLM-4.5-Air vs Granite 4.2 8B
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- LFM3-Audio-2B vs MiMo-V2.6-Flash-RL
- LFM3-Omni-2B vs MiMo-V2.6-Flash-RL
- LFM3-VL-2B vs MiMo-V2.6-Flash-RL
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- MiMo-V2.6-Flash-RL vs Ornith-1.5-9B
- MiMo-V2.6-Flash-RL vs OTel-2.0-LLM-31B-IT
- MiMo-V2.6-Flash-RL vs Qwen-AgentWorld-35B-A3B
- MiMo-V2.6-Flash-RL vs Qwen3-30B-A3B-Instruct-2507
- MiMo-V2.6-Flash-RL vs Qwen3-4B-Instruct-2507
- MiMo-V2.6-Flash-RL vs Qwen3.5-0.8B
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- MiMo-V2.6-Flash-RL vs Qwen3.5-4B
- MiMo-V2.6-Flash-RL vs Qwen3.5-9B
- MiMo-V2.6-Flash-RL vs Qwen3.6-27B
- MiMo-V2.6-Flash-RL vs Qwen3.6-35B-A3B
- MiMo-V2.6-Flash-RL vs Qwen3.8-27B
- MiMo-V2.6-Flash-RL vs Qwen3.8-Flash-Next
- MiMo-V2.6-Flash-RL vs Qwen3-Coder-30B-A3B-Instruct
- MiMo-V2.6-Flash-RL vs Qwen3-Coder-Next
- MiMo-V2.6-Flash-RL vs Qwen3-Omni-30B-A3B-Instruct
- MiMo-V2.6-Flash-RL vs Qwen3-VL-2B-Instruct
- MiMo-V2.6-Flash-RL vs Qwen3-VL-30B-A3B-Instruct
- MiMo-V2.6-Flash-RL vs Qwen3-VL-32B-Instruct
- MiMo-V2.6-Flash-RL vs Qwen3-VL-4B-Instruct
- MiMo-V2.6-Flash-RL vs Qwen3-VL-8B-Instruct
- MiMo-V2.6-Flash-RL vs Qwopus3.8-27B-Flash-V2
- MiMo-V2.6-Flash-RL vs Recursive Flint
- MiMo-V2.6-Flash-RL vs Ripple-65B Preview
- MiMo-V2.6-Flash-RL vs SenseNova-Loop-9B-Preview
- MiMo-V2.6-Flash-RL vs SmolLM3-3B
- MiMo-V2.6-Flash-RL vs Spark-X2.6-4B
- MiMo-V2.6-Flash-RL vs Step 3.7 Flash
- MiMo-V2.6-Flash-RL vs Swift 1.5 Qwen3.8-27B
- MiMo-V2.6-Flash-RL vs Swift 1.5 Qwen3.8-Flash-Next
- MiMo-V2.6-Flash-RL vs ThinkingCap (Qwen3.8 27B)
- MiMo-V2.6-Flash-RL vs Baidu Unlimited-OCR-2
- MiMo-V2.6-Flash-RL vs WeVisOne-VL-2B
- MiMo-V2.6-Flash-RL vs WeVisOne-VL-4B
- MiMo-V2.6-Flash-RL vs ZAYA1-8B
Machine-readable
- This page as Markdown
/compare/granite-4-2-8b-vs-mimo-v2-6-flash-rl.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/granite-4-2-8b.json·/api/v1/tools/mimo-v2-6-flash-rl.json - From a terminal
anchor compare granite-4-2-8b mimo-v2-6-flash-rl(the CLI) - Over MCP
compare_tools {"a": "granite-4-2-8b", "b": "mimo-v2-6-flash-rl"}at/mcp, no key