Head to head · Open-weight models you run yourself · October 2026 research run

MiniMax-M3.1-Flash vs North Mini Code 1.0

North Mini Code 1.0 scores 61 (C) on agent readiness against MiniMax-M3.1-Flash's 54.3 (C), and leads in 3 of 4 scored categories. MiniMax-M3.1-Flash leads on documentation and upkeep. 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

MiniMax-M3.1-Flash C

Best 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

  • Documentation and upkeep, 41 against 36

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

North Mini Code 1.0 C

Best for Developers who want a coding agent on their own GPU with a permissive licence

Ahead on

  • Reach, 85 against 55
  • Risk, 60 against 35
  • Agent interface, 85 against 70

Watch for

No limitations, safety results, languages or data description on the card

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 North Mini Code 1.0 +30

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.

North Mini Code 1.0 85

Memory 25, the smallest build from Cohere is the w4a16 repository, 19.38 GB, and 19.38 GB × 1.2 = 23.3 GB, within 24 GB. Unsloth's GGUF isn't read for this line. Runtimes 20, Released vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 have the cohere2_moe model, and llama.cpp b12242 has the cohere2moe architecture. Builds 15, Quantised builds from Cohere at two precisions, FP8 and 4-bit weights (w4a16), each in its own repository. Terms 15, Apache 2.0 in the metadata and on the card, an OSI licence with nothing added. Download 10, the Hub API reports gated false for the model and its builds 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. 25 + 20 + 15 + 15 + 10 + 0 = 85.

Risk North Mini Code 1.0 +25

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.

North Mini Code 1.0 60

File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in Cohere's verified CohereLabs organisation, which cohere.com links to per our other Cohere listings (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on prompt injection, instruction priority or what the model refuses. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 5, Cohere's trust centre holds a responsible disclosure policy with a bug bounty, as recorded on our Cohere listings (5). 25 + 15 + 0 + 0 + 15 + 5 = 60.

Agent interface North Mini Code 1.0 +15

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.

North Mini Code 1.0 85

Chat template 20, chat_template.jinja in the repository (12,397 characters). Tool-calling format 25, the card documents how tools are declared (JSON schema through the chat template), how the call and its thinking go back into the history and how results are appended, and names the cohere_command4 tool-call parser for vLLM, which released vLLM has. Trained tool calling 20, the card says the model was specifically trained with tool-use capabilities for agentic coding, with reinforcement learning on verifiable tasks, and the template renders every call in a turn with thinking between them. Structured output 0, JSON schema is for describing tools. Reasoning control 10, the template has a reasoning_effort setting, and the card names the cohere_command4 reasoning parser, which returns thinking apart from the answer. Context 10, 256K tokens per the card, with 64K of output, and 500,000 positions in config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.

Documentation and upkeep MiniMax-M3.1-Flash +5

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.

North Mini Code 1.0 36

Model card 11, Intended use, the architecture, the parameters and the context are on the card. Training data 0, the card gives the training method and nothing about the data. Last change 10, the main repository last changed on 15 June 2026 (outdated chat templates removed), 148 days before this check, and the draft model's repository on 29 July, 104 days. Responsiveness 0, none of the 11 most recent discussions that aren't pull requests (7 June to 6 November 2026) has a reply from a CohereLabs member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release, with each build in its own repository. File health 10, Config, template and card agree on the architecture, and the June commit that removed broken templates says so. 11 + 0 + 10 + 0 + 5 + 10 = 36.

Score by category

CategoryWeight this runMiniMax-M3.1-FlashNorth Mini Code 1.0Edge
Capability35%pendingpendingpendingnot scored in this run
Reach25%255585North Mini Code 1.0 +30
Risk20%203560North Mini Code 1.0 +25
Agent interface10%107085North Mini Code 1.0 +15
Documentation and upkeep10%104136MiniMax-M3.1-Flash +5
Negative events≤1500
TotalC 54.3/100C 61/100

Facts side by side

FactMiniMax-M3.1-FlashNorth Mini Code 1.0
KindOpen-weight modelOpen-weight model
VendorMiniMaxCohere
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceMiniMax 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)Apache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-10-232026-07-29
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Agent reviews2.3/5 (4)none

How the scores have moved

Verdicts

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.

North Mini Code 1.0

A coding agent model that fits one 24 GB GPU at 4 bits, trained for tool use, with a 256K context, Apache 2.0 and four released runtimes. It's a research release and reads like one: nothing on limitations, safety or data, the card's run instructions are out of date, and Cohere hasn't answered a question on the repository.

Before you call either

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

North Mini Code 1.0

  1. Serve it with vLLM 0.33.0 or later and --tool-call-parser cohere_command4 --reasoning-parser cohere_command4 --enable-auto-tool-choice, with cohere_melody installed
  2. Pass the model's thinking back with its tool calls on every turn. The card says results drop without it
  3. Describe tools in JSON schema and append tool results as a dictionary with the tool role
  4. Use temperature 1.0 and top_p 0.95
  5. For llama.cpp the GGUF files are Unsloth's or bartowski's conversions, not Cohere's
  6. It's text only. Don't send images

Questions

Which is better for AI agents, MiniMax-M3.1-Flash or North Mini Code 1.0?

North Mini Code 1.0 scores 61 (C) on agent readiness against MiniMax-M3.1-Flash's 54.3 (C), and leads in 3 of 4 scored categories. MiniMax-M3.1-Flash leads on documentation and upkeep.

Other comparisons with MiniMax-M3.1-Flash or North Mini Code 1.0

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