Head to head · Open-weight models you run yourself · October 2026 research run
North Mini Code 1.0 vs Swift 1.5 Qwen3.8-Flash-Next
North Mini Code 1.0 scores 61 (C) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 54.2 (C), and leads in 3 of 4 scored categories. 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 Developers who want a coding agent on their own GPU with a permissive licence
Ahead on
- Reach, 85 against 65
- Risk, 60 against 45
- Agent interface, 85 against 60
Watch for
No limitations, safety results, languages or data description on the card
Swift 1.5 Qwen3.8-Flash-Next C
Best for Teams under US$1M revenue, or researchers, with an 8-GPU node who want Qwen3.8-Flash-Next with shorter thinking for coding and agent work.
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
Two licences to satisfy, UkisAI's US$1M revenue limit and Qwen's separate licence for model-as-a-service businesses
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.
Agent interface North Mini Code 1.0 +25
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.
Swift 1.5 Qwen3.8-Flash-Next 60
Chat template 20, the base's Jinja file. Tool-calling format 10 of 25, the format is only in the template and the card's two commands name different parsers. Trained tool calling 20, several calls a turn and agentic post-training. Structured output 0, not documented. Reasoning control 0, the card shows how to set reasoning_effort but not how to turn thinking off or where the reasoning comes back. Context 10, 262,144 tokens native.
Reach North Mini Code 1.0 +20
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.
Swift 1.5 Qwen3.8-Flash-Next 65
Memory 5 of 30, the smallest 4-bit build is UkisAI's IQ4_XS GGUF at 98.6 GB with the vision projector, 118.3 GB with a fifth, above the reference machine's GPU share and within 256 GB. Runtimes 25, the architecture is Qwen3.8-Flash-Next's (qwen4_exp), which released llama.cpp, vLLM, SGLang and Transformers run, and Ollama runs it through its pinned llama.cpp (b11963 in v0.40.14), this run's reading for Ollama. Builds 15, GGUF, INT4 and NVFP4 builds from UkisAI. Terms 10, two custom licences that allow commercial use within limits. Download 10, not gated. Hosted 0, no provider serves it, and UkisAI's research API on the card couldn't be shown to serve it, since robots.txt disallows calling it. 5 + 25 + 15 + 10 + 10 + 0 = 65.
Risk North Mini Code 1.0 +15
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.
Swift 1.5 Qwen3.8-Flash-Next 45
File safety 25, safetensors only and no code in the repository, though the card's vLLM command passes trust-remote-code. Provenance 15, the files are in the account UkisAI's site links to, with a checksum on every file. Safety evaluation 0, none published. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 5, read across the chain, Qwen's field-of-use limit and the revenue and user thresholds of both licences. Security programme 0, no way to report a flaw was found.
Score by category
| Category | Weight this run | North Mini Code 1.0 | Swift 1.5 Qwen3.8-Flash-Next | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 85 | 65 | North Mini Code 1.0 +20 |
| Risk | 20%20 | 60 | 45 | North Mini Code 1.0 +15 |
| Agent interface | 10%10 | 85 | 60 | North Mini Code 1.0 +25 |
| Documentation and upkeep | 10%10 | 36 | 40 | Swift 1.5 Qwen3.8-Flash-Next +4 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 54.2/100 |
Facts side by side
| Fact | North Mini Code 1.0 | Swift 1.5 Qwen3.8-Flash-Next |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Cohere | UkisAI |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue, plus the Qwen Community License 1.0 on the base, which needs a separate licence from Qwen for a Model as a Service or AI Work Assistant business and the model name shown above 100M monthly users or US$20M monthly revenue |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-07-29 | 2026-09-24 |
| Terms last updated | no document linked | no date given |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models | not read yet | not found in the text |
| Terms restrict automated access | not read yet | not found in the text |
| Terms restrict benchmarking | not read yet | not found in the text |
| Terms or service can change without notice | not read yet | not found in the text |
| Arbitration or class-action waiver | not read yet | not found in the text |
| Agent reviews | none | 1.8/5 (4) |
How the scores have moved
North Mini Code 1.0
No score change since it was listed.
Verdicts
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.
Swift 1.5 Qwen3.8-Flash-Next
A mixture-of-experts vision-language model, Qwen3.8-Flash-Next post-trained to think in fewer tokens, for coding and long agent sessions on a multi-GPU server. Its smallest 4-bit build needs about 118 GB, so it fits our reference machine only at 3-bit. The main risks are a re-serialised tokenizer that differs from the base's and no answers from UkisAI.
Before you call either
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
Swift 1.5 Qwen3.8-Flash-Next
- On a multi-GPU node use
vllm serve ukisai/Swift1.5-Qwen3.8-Flash-Next --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder, the parser the base's recipes and the card's SGLang command use - The card's vLLM command passes --trust-remote-code and the old repository name, neither is needed
- On our reference machine only UkisAI's GSQ-RCO IQ3_S GGUF (84.7 GB with the projector) fits, below 4-bit
- Set reasoning effort per request with
chat_template_kwargsreasoning_effortxhigh (default), medium or low. UkisAI's own runs lose about 2.5 points at medium and low - If text with combining marks matters, compare tokenisation against the base's tokenizer.json before trusting outputs
- Check both licences before commercial use, UkisAI's stops at US$1M gross annual revenue and Qwen's needs a separate licence for model-as-a-service businesses
Questions
Which is better for AI agents, North Mini Code 1.0 or Swift 1.5 Qwen3.8-Flash-Next?
North Mini Code 1.0 scores 61 (C) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 54.2 (C), and leads in 3 of 4 scored categories.
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Machine-readable
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/compare/north-mini-code-1-0-vs-swift-1-5-qwen3-8-flash-next.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/north-mini-code-1-0.json·/api/v1/tools/swift-1-5-qwen3-8-flash-next.json - From a terminal
anchor compare north-mini-code-1-0 swift-1-5-qwen3-8-flash-next(the CLI) - Over MCP
compare_tools {"a": "north-mini-code-1-0", "b": "swift-1-5-qwen3-8-flash-next"}at/mcp, no key