# Cola2 vs MiniMax-M3.1-Flash

> MiniMax-M3.1-Flash scores 54.3 (C) to Cola2's 33.9 (F) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/cola2-vs-minimax-m3-1-flash
- Markdown: https://www.anchorterminal.com/compare/cola2-vs-minimax-m3-1-flash.md (~9,350 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 <slug>.grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.<category>. and <a>.vs.<b>. 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

MiniMax-M3.1-Flash scores 54.3 (C) on agent readiness against Cola2's 33.9 (F), and leads in 2 of 4 scored categories. Cola2 leads on risk and documentation and upkeep. Both do open-weight models you run yourself.

- Cola2: grade F, 33.9/100, rank #160 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/cola2.md · JSON https://www.anchorterminal.com/api/v1/tools/cola2.json
- MiniMax-M3.1-Flash: grade C, 54.3/100, rank #131 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/minimax-m3-1-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/minimax-m3-1-flash.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

### Cola2 (F)

Good for: Researchers working on latent or diffusion language models who want a checkpoint to study

Ahead on:
- Risk, 40 against 35
- Documentation and upkeep, 58 against 41

Watch for: No licence stated for the weights

### MiniMax-M3.1-Flash (C)

Good for: Teams with a 4 to 8 GPU server who want an open multimodal agent and coding model with 1M tokens of context and can meet the licence's attribution and notice terms.

Ahead on:
- Reach, 55 against 25
- Agent interface, 70 against 20

Watch for: SGLang's cookbook still points to a pull request or a dev image on 4 November 2026, and vLLM's recipe still names a nightly image, though both runtimes' releases carry the model code


## Why the edge

### Agent interface: MiniMax-M3.1-Flash +50

- Cola2 (20): Chat template 20, A chat template in tokenizer_config.json (4,116 characters), which came with the Qwen3 tokenizer. Tool-calling format 0, none. Trained tool calling 0, not trained for tool calling. Structured output 0, Nothing about JSON or schema-constrained output. Reasoning control 0, no reasoning mode. Context 0, no context length is stated. 20 + 0 + 0 + 0 + 0 + 0 = 20.
- MiniMax-M3.1-Flash (70): The chat template earns 20 for chat_template.jinja in the repository. The tool-calling format earns 10, since only the template shows the format and the parsers come from vLLM's and SGLang's own docs. Trained tool calling earns 20, with several invoke blocks in one call. Structured output earns nothing, as it isn't documented. Reasoning control earns 10, with reasoning_effort per request on the card and the reasoning in mm:think tags. Context earns 10 for 1,048,576 tokens with no position scaling.

### Reach: MiniMax-M3.1-Flash +30

- Cola2 (25): Memory 15, the only build is the F32 safetensors set, 35.07 GB, and 35.07 GB × 1.2 = 42.1 GB, within 64 GB. Loaded as BF16, as the card's example does, it needs about half, but the line reads the file size. Runtimes 0, none of the six runs it in a released version. Builds 0, one set of weights and no quantised build. Terms 0, no licence for the weights. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and nothing names an API that serves it. 15 + 0 + 0 + 0 + 10 + 0 = 25.
- MiniMax-M3.1-Flash (55): Memory earns nothing. MiniMax's only build is its 250 GB NVFP4 and MXFP8 release, about 300 GB with the fifth added, above 256 GB, and no runtime library has a local build. Runtimes earn 20. llama.cpp from b11861 runs it, Ollama v0.40.14 pins llama.cpp b11963 with the minimax-m3 architecture, vLLM v0.33.0 (tagged 1 November 2026) registers the architecture and meets its recipe's minimum version, and SGLang v0.5.23 (tagged 29 October) registers it with M3.1 processor code and tests, though its cookbook still points to a pull request. Transformers support is in progress per the card and LM Studio has no entry. Builds earn 10 for one publisher precision. Terms earn 10, since commercial use is free with attribution and a notice below $20M of yearly revenue. Download earns 10, with no gate. Hosted earns 5, since MiniMax's API and Novita serve it. 0 + 20 + 10 + 10 + 10 + 5 = 55.

### Documentation and upkeep: Cola2 +17

- Cola2 (58): Model card 8, Intended use and limitations (the out-of-scope list, and the unconverged latent model) and the architecture are on the card and the GitHub model card it links. Training data 15, the GitHub model card names the source (OLMo 2 pretraining text, retokenised) and the mix by stage with token counts, 346.03B in all. Last change 20, All six commits are from 6 November 2026, four days before this check, the last one updating a checksum in the README. Responsiveness 0, no discussions on the repository, so nobody has asked anything yet and the line reads as none answered, as settled in calibration. Version policy 5, A first release. File health 10, Config, files and card agree: F32 weights, a float32 config and a card that says to load them as bfloat16, with SHA256SUMS to check the download. 8 + 15 + 20 + 0 + 5 + 10 = 58.
- MiniMax-M3.1-Flash (41): The model card earns 11, missing limitations, languages and a knowledge cut-off. Training data earns nothing, as it isn't described. The last change earns 20, an evaluation-results commit of 30 October 2026 after the release commit of 23 October. Responsiveness earns nothing, with no MiniMax reply in nine discussions opened from 23 to 31 October. Version policy earns 5 for a first release in its own repository. File health earns 5, for the tokenizer's 40,960,000-token model_max_length against the 1M context, the Transformers library tag against the card, and an unanswered vLLM streaming report. 11 + 0 + 20 + 0 + 5 + 5 = 41.

### Risk: Cola2 +5

- Cola2 (40): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the ByteDance-Seed organisation, which the ByteDance-Seed GitHub repository for the model links to (10). Safety evaluation 0, no safety results. Agent guidance 5, General cautions in the GitHub model card: not instruction-tuned or safety-aligned, not for use as an assistant, for facts or advice, or for high-impact decisions about people. Licence risk 0, With no licence for the weights there is nothing to hold ByteDance to. Security programme 5, the GitHub repository's SECURITY.md sends reports to ByteDance's security centre and says how they're assessed (5). 15 + 15 + 0 + 5 + 0 + 5 = 40.
- MiniMax-M3.1-Flash (35): File safety earns 15, safetensors only, less 10 because the SGLang route the card links passes trust_remote_code for the repository's configuration and processor code. Provenance earns the full 15, files in the organisation MiniMax links to with a SHA-256 for each shard. Safety evaluation and agent guidance earn nothing; the launch post's security testing measured offensive capability, and the template's root role isn't documented as a trained hierarchy. Licence risk earns 5, after the military-use ban and the $20M threshold. The security programme earns nothing, with no security.txt, disclosure policy or advisories.

## Score by category

| Category | Weight | Cola2 | MiniMax-M3.1-Flash | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 25 | 55 | MiniMax-M3.1-Flash +30 |
| Risk | 20% (20 this run) | 40 | 35 | Cola2 +5 |
| Agent interface | 10% (10 this run) | 20 | 70 | MiniMax-M3.1-Flash +50 |
| Documentation and upkeep | 10% (10 this run) | 58 | 41 | Cola2 +17 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **33.9 · F** | **54.3 · C** | |

## Facts side by side

| Fact | Cola2 | MiniMax-M3.1-Flash |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | ByteDance (Seed) | MiniMax |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | None stated for the weights (the code is Apache 2.0) | MiniMax Community License (custom; commercial use needs a "Built with MiniMax M3.1-Flash" line and a one-time notice to MiniMax, and MiniMax's written authorisation above $20M yearly revenue) |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-11-06 | 2026-10-23 |
| Terms last updated | no document linked | 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

### Cola2

No score change since it was listed.

### MiniMax-M3.1-Flash

- +3.8 on 4 November 2026 (D to C): product

## Verdicts

**Cola2.** A research release, and an honest one: ByteDance Seed says it's a base model with an unconverged latent stage, scoring below Llama-3-8B on its own table. It's here for people studying continuous-latent generation. Agents can't use it as a model: 32-token completions, no chat or tools, the repository's own code to load it, and no licence on the weights.

**MiniMax-M3.1-Flash.** A 428B mixture-of-experts model, 23B active, for agent work, coding and computer use, with image and video input, about 1M tokens of context and parallel tool calls. It needs a 4 to 8 GPU server for 250 GB of NVFP4 and MXFP8 weights. vLLM 0.33.0 and SGLang 0.5.23 carry its code. Commercial use needs attribution and notice.

## Before you call either

### Cola2

1. Don't plan to use this as your model. It completes short prompts and wasn't tuned to follow instructions
2. Don't use it commercially on the strength of this listing. The weights have no stated licence
3. If you study it, read modeling_cola2.py first: trust_remote_code runs it
4. Pin Transformers below 5, as the card requires
5. Pass a seeded torch.Generator to generate(). Output varies between runs even with greedy decoding
6. Check the download with `sha256sum -c SHA256SUMS`

### MiniMax-M3.1-Flash

1. Check the use case first; commercial products must show "Built with MiniMax M3.1-Flash" and notify MiniMax once, or get written authorisation above $20M yearly revenue
2. On 8 H100 or H200 GPUs use vLLM 0.33.0 or later, or the recipe's image vllm/vllm-openai:minimax-m31-flash, with --tensor-parallel-size 8 --block-size 128 --kv-cache-dtype fp8 --moe-backend marlin --max-model-len 262144
3. Pass --reasoning-parser minimax_m3 --tool-call-parser minimax_m3 --enable-auto-tool-choice in vLLM, or minimax-m3 for both in SGLang with --trust-remote-code
4. --block-size 128 is mandatory in vLLM, since MSA's sparse block size is 128
5. Set effort per request with chat_template_kwargs reasoning_effort (low, medium, high, xhigh, max); sample at temperature 1.0 and top_p 0.95
6. On Blackwell or MI355X, 4 GPUs serve it with the DSpark draft from dspark/; vLLM's Hopper command leaves DSpark off and turns video input off
7. For GGUF builds by others, use llama.cpp b11861 or later; earlier builds mis-scale the NVFP4 experts and produce garbage
8. Without the hardware, call MiniMax-M3.1-Flash on MiniMax's API (minimax-api), whose thinking defaults to max effort

## Questions

### Which is better for AI agents, Cola2 or MiniMax-M3.1-Flash?

MiniMax-M3.1-Flash scores 54.3 (C) on agent readiness against Cola2's 33.9 (F), and leads in 2 of 4 scored categories. Cola2 leads on risk and documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/cola2-vs-minimax-m3-1-flash.json, and with the fewest tokens: https://www.anchorterminal.com/compare/cola2-vs-minimax-m3-1-flash.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "cola2", "b": "minimax-m3-1-flash"}`. From a terminal: `anchor compare cola2 minimax-m3-1-flash`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/cola2.json and https://www.anchorterminal.com/api/v1/tools/minimax-m3-1-flash.json

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---

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