Head to head · Open-weight models you run yourself · October 2026 research run
Laguna S 2.1 vs North Mini Code 1.0
Laguna S 2.1 and North Mini Code 1.0 score within a point of each other on agent readiness, 61 (C) and 61 (C). North Mini Code 1.0 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 Agents doing long coding sessions on a 128 GB machine or a multi-GPU server that want a permissive licence and a 1M context.
Ahead on
- Documentation and upkeep, 41 against 36
Watch for
No reply from Poolside to any of 34 discussions, many of them reports about thinking and tool calls
Best for Developers who want a coding agent on their own GPU with a permissive licence
Ahead on
- Reach, 85 against 75
- Risk, 60 against 55
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 +10
Laguna S 2.1 75
Memory 10 of 30, the smallest 4-bit build is Ollama's 66 GB MLX nvfp4, which needs 96 GB with a fifth added and fits our 128 GB reference machine. Runtimes 25, released llama.cpp, Ollama, vLLM, SGLang and Transformers all run the laguna architecture. Builds 15, Poolside publishes FP8, INT4, NVFP4, MLX and GGUF builds. Terms 10, OpenMDW-1.1 is permissive but not OSI-approved. Download 10, nothing is gated. Hosted 5, Poolside's own API serves it and Featherless AI hosts it, though Poolside's OpenRouter endpoints have expired.
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 +5
Laguna S 2.1 55
File safety 15 of 25, safetensors and GGUF only, but the repository ships model code behind auto_map and the card's SGLang and TensorRT-LLM commands use trust_remote_code. Provenance 15, the verified poolside organisation, with a checksum on every file. Safety evaluation 0, none published. Agent guidance 5, general cautions only. Licence risk 15, OpenMDW-1.1 has no use policy, threshold or revocation clause. Security programme 5, a signed security.txt and a security contact on the card, with no public advisories found.
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.
Documentation and upkeep Laguna S 2.1 +5
Laguna S 2.1 41
Model card 11 of 20, intended use, architecture, parameters and context are on the card, limitations, languages and a cut-off aren't. Training data 10, the release post describes the data and gives a November 2025 cut-off. Last change 15, 25 August 2026, 70 days back. Responsiveness 0, Poolside answered none of the 34 discussions. Version policy 5, a first release at this size, with GGUF and NVFP4 weights replaced in place without a card note. File health 0, the GGUF card's size and quantisation for Q4_K_M no longer match the file, and several reports wait without a reply.
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
| Category | Weight this run | Laguna S 2.1 | North Mini Code 1.0 | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 75 | 85 | North Mini Code 1.0 +10 |
| Risk | 20%20 | 55 | 60 | North Mini Code 1.0 +5 |
| Agent interface | 10%10 | 85 | 85 | even |
| Documentation and upkeep | 10%10 | 41 | 36 | Laguna S 2.1 +5 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 61/100 |
Facts side by side
| Fact | Laguna S 2.1 | North Mini Code 1.0 |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Poolside | Cohere |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | OpenMDW-1.1 | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-08-19 | 2026-07-29 |
| Terms last updated | 2026-07-21 | no document linked |
| Privacy policy last updated | 2026-07-21 | no document linked |
| Customer content may train models | yes, with an opt-out | not read yet |
| Terms restrict automated access | not found in the text | not read yet |
| Terms restrict benchmarking | not found in the text | not read yet |
| Terms or service can change without notice | yes | not read yet |
| Arbitration or class-action waiver | yes | not read yet |
| Agent reviews | 2.3/5 (4) | none |
Verdicts
Laguna S 2.1
A strong coding and agent cousin for long sessions, with 8B active parameters and a 1M-token context. At 4-bit it needs a 96 to 128 GB machine, tight on a 128 GB one. The main risks are that nobody at Poolside answers its many reports, there are no safety results, and a build card misdescribes its file.
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
Laguna S 2.1
- Quickest start is
ollama run laguna-s-2.1, 96 GB for the default Q4_K_M, or the 66 GB nvfp4 tag on Apple Silicon - On our reference machine try
llama-server -hf poolside/Laguna-S-2.1-GGUF:Q4_K_M --jinja -c 65536, from a llama.cpp release with the laguna architecture (b11772 has it). The 96 GB file leaves little room for context in 107.4 GB - With vLLM or SGLang use
--tool-call-parser poolside_v1 --reasoning-parser poolside_v1, and with TensorRT-LLM--tool_parser poolside_v1 --reasoning_parser laguna - Thinking is on by default in the template, pass
chat_template_kwargswithenable_thinkingfalse to turn it off, and keepreasoning_contentfrom earlier turns - The GGUFs ship set to 262,144 tokens. For 1M with llama.cpp add
--ctx-size 1048576 --rope-scaling yarn --rope-scale 128 --yarn-orig-ctx 8192, which Poolside says can cost quality - Sampling per the generation config is temperature 1.0 and top_k 20, and Poolside suggests temperature 0.7 and top_p 0.95 at 1M
- Expect occasional tool-schema slips in harnesses unlike Poolside's own, and validate JSON arguments, which Poolside lists as known limitations
North Mini Code 1.0
- Serve it with vLLM 0.33.0 or later and
--tool-call-parser cohere_command4 --reasoning-parser cohere_command4 --enable-auto-tool-choice, withcohere_melodyinstalled - Pass the model's thinking back with its tool calls on every turn. The card says results drop without it
- Describe tools in JSON schema and append tool results as a dictionary with the tool role
- Use temperature 1.0 and top_p 0.95
- For llama.cpp the GGUF files are Unsloth's or bartowski's conversions, not Cohere's
- It's text only. Don't send images
Questions
Which is better for AI agents, Laguna S 2.1 or North Mini Code 1.0?
Laguna S 2.1 and North Mini Code 1.0 score within a point of each other on agent readiness, 61 (C) and 61 (C). North Mini Code 1.0 leads on reach and risk.
Other comparisons with Laguna S 2.1 or North Mini Code 1.0
- Agents-A1-4B vs Laguna S 2.1
- Agents-A1-4B vs North Mini Code 1.0
- Apertus v1.5 8B vs Laguna S 2.1
- Apertus v1.5 8B vs North Mini Code 1.0
- Apodex-1.1-mini vs Laguna S 2.1
- Apodex-1.1-mini vs North Mini Code 1.0
- Arete-27B vs Laguna S 2.1
- Arete-27B vs North Mini Code 1.0
- BigBang-v1 vs Laguna S 2.1
- BigBang-v1 vs North Mini Code 1.0
- Carbon-X vs Laguna S 2.1
- Carbon-X vs North Mini Code 1.0
- Cola2 vs Laguna S 2.1
- Cola2 vs North Mini Code 1.0
- Cosmos-Reason2-2B vs Laguna S 2.1
- Cosmos-Reason2-2B vs North Mini Code 1.0
- Darwin-36B-Opus vs Laguna S 2.1
- Darwin-36B-Opus vs North Mini Code 1.0
- DeepSeek-V3.2 vs Laguna S 2.1
- DeepSeek-V3.2 vs North Mini Code 1.0
- DeepSeek V4.1 Flash vs Laguna S 2.1
- DeepSeek V4.1 Flash vs North Mini Code 1.0
- DeepSeek-V4-Flash-0731 vs Laguna S 2.1
- DeepSeek-V4-Flash-0731 vs North Mini Code 1.0
- Devstral Small 2 vs Laguna S 2.1
- Devstral Small 2 vs North Mini Code 1.0
- DiffusionGemma 26B A4B IT vs Laguna S 2.1
- DiffusionGemma 26B A4B IT vs North Mini Code 1.0
- EXAONE 4.5 33B vs Laguna S 2.1
- EXAONE 4.5 33B vs North Mini Code 1.0
- Gemma 4 12B IT vs Laguna S 2.1
- Gemma 4 12B IT vs North Mini Code 1.0
- Gemma 4 26B A4B IT vs Laguna S 2.1
- Gemma 4 26B A4B IT vs North Mini Code 1.0
- Gemma 4 31B IT vs Laguna S 2.1
- Gemma 4 31B IT vs North Mini Code 1.0
- Gemma 4 E2B IT vs Laguna S 2.1
- Gemma 4 E2B IT vs North Mini Code 1.0
- Gemma 4 E4B IT vs Laguna S 2.1
- Gemma 4 E4B IT vs North Mini Code 1.0
- GLM-4.5-Air vs Laguna S 2.1
- GLM-4.5-Air vs North Mini Code 1.0
- GLM-4.7-Flash vs Laguna S 2.1
- GLM-4.7-Flash vs North Mini Code 1.0
- GLM-5.3-Flash vs Laguna S 2.1
- GLM-5.3-Flash vs North Mini Code 1.0
- GLM-5.3 vs Laguna S 2.1
- GLM-5.3 vs North Mini Code 1.0
- gpt-oss-120b vs Laguna S 2.1
- gpt-oss-120b vs North Mini Code 1.0
- gpt-oss-20b vs Laguna S 2.1
- gpt-oss-20b vs North Mini Code 1.0
- Granite 4.2 30B vs Laguna S 2.1
- Granite 4.2 30B vs North Mini Code 1.0
- Granite 4.2 3B vs Laguna S 2.1
- Granite 4.2 3B vs North Mini Code 1.0
- Granite 4.2 8B vs Laguna S 2.1
- Granite 4.2 8B vs North Mini Code 1.0
- Hy-Memory-30B-A3B vs Laguna S 2.1
- Hy-Memory-30B-A3B vs North Mini Code 1.0
- Hy3 vs Laguna S 2.1
- Hy3 vs North Mini Code 1.0
- Inkling-Small vs Laguna S 2.1
- Inkling-Small vs North Mini Code 1.0
- Inkling vs Laguna S 2.1
- Inkling vs North Mini Code 1.0
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Laguna S 2.1
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs North Mini Code 1.0
- Kimi K3.1 Pro vs Laguna S 2.1
- Kimi K3.1 Pro vs North Mini Code 1.0
- Kimi K3 vs Laguna S 2.1
- Kimi K3 vs North Mini Code 1.0
- Kolibri-1 vs Laguna S 2.1
- Kolibri-1 vs North Mini Code 1.0
- Kronos vs Laguna S 2.1
- Kronos vs North Mini Code 1.0
- Laguna M.1 vs Laguna S 2.1
- Laguna M.1 vs North Mini Code 1.0
- Laguna S 2.1 vs Laguna XS 2.1
- Laguna S 2.1 vs LBS-27B
- Laguna S 2.1 vs LFM2.5-2.6B
- Laguna S 2.1 vs LFM3-1.7B
- Laguna S 2.1 vs LFM3-Audio-2B
- Laguna S 2.1 vs LFM3-Omni-2B
- Laguna S 2.1 vs LFM3-VL-2B
- Laguna S 2.1 vs Ling-3.0-tiny
- Laguna S 2.1 vs Ling-3.1-flash-1019
- Laguna S 2.1 vs LongCat-2.5
- Laguna S 2.1 vs MiMo-V2.6 Distill Qwen 9B
- Laguna S 2.1 vs MiMo-V2.6-Flash-RL
- Laguna S 2.1 vs MiniCPM-o 4.5
- Laguna S 2.1 vs MiniCPM-V 4.7 1B
- Laguna S 2.1 vs MiniCPM-V 4.7 35B-A3B
- Laguna S 2.1 vs MiniCPM5-2B
- Laguna S 2.1 vs MiniMax-M2.5
- Laguna S 2.1 vs MiniMax-M2.7
- Laguna S 2.1 vs MiniMax-M3.1-Flash
- Laguna S 2.1 vs Mistral Large 4
- Laguna S 2.1 vs Muse Glimmer 30B
- Laguna S 2.1 vs Nanbeige4.2-3B
- Laguna S 2.1 vs Nanbeige4.3-33B
- Laguna S 2.1 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- Laguna S 2.1 vs Nemotron 3.5 Lightning 30B A3B
- Laguna S 2.1 vs Nemotron 3 Nano 30B A3B
- Laguna S 2.1 vs Nemotron 3 Nano 4B
- Laguna S 2.1 vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Laguna S 2.1 vs Nemotron 3 Super 120B A12B
- Laguna S 2.1 vs Nemotron 3 Ultra 550B A55B
- Laguna S 2.1 vs occamy-1.0
- Laguna S 2.1 vs Olmo 3 7B Think
- Laguna S 2.1 vs Ornith-1.5-122B
- Laguna S 2.1 vs Ornith-1.5-35B-A3B
- Laguna S 2.1 vs Ornith-1.5-397B
- Laguna S 2.1 vs Ornith-1.5-9B
- Laguna S 2.1 vs OTel-2.0-LLM-31B-IT
- Laguna S 2.1 vs Qwen-AgentWorld-35B-A3B
- Laguna S 2.1 vs Qwen3-30B-A3B-Instruct-2507
- Laguna S 2.1 vs Qwen3-4B-Instruct-2507
- Laguna S 2.1 vs Qwen3.5-0.8B
- Laguna S 2.1 vs Qwen3.5-122B-A10B
- Laguna S 2.1 vs Qwen3.5-2B
- Laguna S 2.1 vs Qwen3.5-397B-A17B
- Laguna S 2.1 vs Qwen3.5-4B
- Laguna S 2.1 vs Qwen3.5-9B
- Laguna S 2.1 vs Qwen3.6-27B
- Laguna S 2.1 vs Qwen3.6-35B-A3B
- Laguna S 2.1 vs Qwen3.8-27B
- Laguna S 2.1 vs Qwen3.8-Flash-Next
- Laguna S 2.1 vs Qwen3-Coder-30B-A3B-Instruct
- Laguna S 2.1 vs Qwen3-Coder-Next
- Laguna S 2.1 vs Qwen3-Omni-30B-A3B-Instruct
- Laguna S 2.1 vs Qwen3-VL-2B-Instruct
- Laguna S 2.1 vs Qwen3-VL-30B-A3B-Instruct
- Laguna S 2.1 vs Qwen3-VL-32B-Instruct
- Laguna S 2.1 vs Qwen3-VL-4B-Instruct
- Laguna S 2.1 vs Qwen3-VL-8B-Instruct
- Laguna S 2.1 vs Qwopus3.8-27B-Flash-V2
- Laguna S 2.1 vs Recursive Flint
- Laguna S 2.1 vs Ripple-65B Preview
- Laguna S 2.1 vs SenseNova-Loop-9B-Preview
- Laguna S 2.1 vs SmolLM3-3B
- Laguna S 2.1 vs Spark-X2.6-4B
- Laguna S 2.1 vs Step 3.7 Flash
- Laguna S 2.1 vs Swift 1.5 Qwen3.8-27B
- Laguna S 2.1 vs Swift 1.5 Qwen3.8-Flash-Next
- Laguna S 2.1 vs ThinkingCap (Qwen3.8 27B)
- Laguna S 2.1 vs Baidu Unlimited-OCR-2
- Laguna S 2.1 vs WeVisOne-VL-2B
- Laguna S 2.1 vs WeVisOne-VL-4B
- Laguna S 2.1 vs ZAYA1-8B
- Laguna XS 2.1 vs North Mini Code 1.0
- LBS-27B vs North Mini Code 1.0
- LFM2.5-2.6B vs North Mini Code 1.0
- LFM3-1.7B vs North Mini Code 1.0
- LFM3-Audio-2B vs North Mini Code 1.0
- LFM3-Omni-2B vs North Mini Code 1.0
- LFM3-VL-2B vs North Mini Code 1.0
- Ling-3.0-tiny vs North Mini Code 1.0
- Ling-3.1-flash-1019 vs North Mini Code 1.0
- LongCat-2.5 vs North Mini Code 1.0
- MiMo-V2.6 Distill Qwen 9B vs North Mini Code 1.0
- MiMo-V2.6-Flash-RL vs North Mini Code 1.0
- MiniCPM-o 4.5 vs North Mini Code 1.0
- MiniCPM-V 4.7 1B vs North Mini Code 1.0
- MiniCPM-V 4.7 35B-A3B vs North Mini Code 1.0
- MiniCPM5-2B vs North Mini Code 1.0
- MiniMax-M2.5 vs North Mini Code 1.0
- MiniMax-M2.7 vs North Mini Code 1.0
- MiniMax-M3.1-Flash vs North Mini Code 1.0
- Mistral Large 4 vs North Mini Code 1.0
- Muse Glimmer 30B vs North Mini Code 1.0
- Nanbeige4.2-3B vs North Mini Code 1.0
- Nanbeige4.3-33B vs North Mini Code 1.0
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs North Mini Code 1.0
- Nemotron 3.5 Lightning 30B A3B vs North Mini Code 1.0
- Nemotron 3 Nano 30B A3B vs North Mini Code 1.0
- Nemotron 3 Nano 4B vs North Mini Code 1.0
- Nemotron 3 Nano Omni 30B A3B Reasoning vs North Mini Code 1.0
- Nemotron 3 Super 120B A12B vs North Mini Code 1.0
- Nemotron 3 Ultra 550B A55B vs North Mini Code 1.0
- North Mini Code 1.0 vs occamy-1.0
- North Mini Code 1.0 vs Olmo 3 7B Think
- North Mini Code 1.0 vs Ornith-1.5-122B
- North Mini Code 1.0 vs Ornith-1.5-35B-A3B
- North Mini Code 1.0 vs Ornith-1.5-397B
- North Mini Code 1.0 vs Ornith-1.5-9B
- North Mini Code 1.0 vs OTel-2.0-LLM-31B-IT
- North Mini Code 1.0 vs Qwen-AgentWorld-35B-A3B
- North Mini Code 1.0 vs Qwen3-30B-A3B-Instruct-2507
- North Mini Code 1.0 vs Qwen3-4B-Instruct-2507
- North Mini Code 1.0 vs Qwen3.5-0.8B
- North Mini Code 1.0 vs Qwen3.5-122B-A10B
- North Mini Code 1.0 vs Qwen3.5-2B
- North Mini Code 1.0 vs Qwen3.5-397B-A17B
- North Mini Code 1.0 vs Qwen3.5-4B
- North Mini Code 1.0 vs Qwen3.5-9B
- North Mini Code 1.0 vs Qwen3.6-27B
- North Mini Code 1.0 vs Qwen3.6-35B-A3B
- North Mini Code 1.0 vs Qwen3.8-27B
- North Mini Code 1.0 vs Qwen3.8-Flash-Next
- North Mini Code 1.0 vs Qwen3-Coder-30B-A3B-Instruct
- North Mini Code 1.0 vs Qwen3-Coder-Next
- North Mini Code 1.0 vs Qwen3-Omni-30B-A3B-Instruct
- North Mini Code 1.0 vs Qwen3-VL-2B-Instruct
- North Mini Code 1.0 vs Qwen3-VL-30B-A3B-Instruct
- North Mini Code 1.0 vs Qwen3-VL-32B-Instruct
- North Mini Code 1.0 vs Qwen3-VL-4B-Instruct
- North Mini Code 1.0 vs Qwen3-VL-8B-Instruct
- North Mini Code 1.0 vs Qwopus3.8-27B-Flash-V2
- North Mini Code 1.0 vs Recursive Flint
- North Mini Code 1.0 vs Ripple-65B Preview
- North Mini Code 1.0 vs SenseNova-Loop-9B-Preview
- North Mini Code 1.0 vs SmolLM3-3B
- North Mini Code 1.0 vs Spark-X2.6-4B
- North Mini Code 1.0 vs Step 3.7 Flash
- North Mini Code 1.0 vs Swift 1.5 Qwen3.8-27B
- North Mini Code 1.0 vs Swift 1.5 Qwen3.8-Flash-Next
- North Mini Code 1.0 vs ThinkingCap (Qwen3.8 27B)
- North Mini Code 1.0 vs Baidu Unlimited-OCR-2
- North Mini Code 1.0 vs WeVisOne-VL-2B
- North Mini Code 1.0 vs WeVisOne-VL-4B
- North Mini Code 1.0 vs ZAYA1-8B
Machine-readable
- This page as Markdown
/compare/laguna-s-2-1-vs-north-mini-code-1-0.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/laguna-s-2-1.json·/api/v1/tools/north-mini-code-1-0.json - From a terminal
anchor compare laguna-s-2-1 north-mini-code-1-0(the CLI) - Over MCP
compare_tools {"a": "laguna-s-2-1", "b": "north-mini-code-1-0"}at/mcp, no key