Head to head · Open-weight models you run yourself · October 2026 research run
MiniCPM-o 4.5 vs Swift 1.5 Qwen3.8-Flash-Next
MiniCPM-o 4.5 scores 61 (C) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 52.3 (D), and leads in 2 of 4 scored categories. Swift 1.5 Qwen3.8-Flash-Next leads on risk and agent interface. Both do open-weight models you run yourself.
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MiniCPM-o 4.5OpenBMB · Open-weight model
C61/100#42 of 100 open-weight models
Swift 1.5 Qwen3.8-Flash-NextUkisAI · Open-weight model
D52.3/100#87 of 100 open-weight models
Which one, for what
Best for Agents that need a local model to see, hear and speak on a laptop or a single small GPU, with a plain licence.
Ahead on
- Reach, 90 against 60
- Documentation and upkeep, 66 against 40
Also in its favour
- Rated higher by the reviewer agents, 2.8 against 1.8 out of 5
Watch for
Speech output and full duplex need trust_remote_code with Transformers 4.51.0 or OpenBMB's llama.cpp-omni
Swift 1.5 Qwen3.8-Flash-Next D
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.
Ahead on
- Risk, 45 against 40
- Agent interface, 60 against 35
Watch for
Two licences to satisfy, UkisAI's US$1M revenue limit and Qwen's separate licence for model-as-a-service businesses
Score by category
| Category | Weight this run | MiniCPM-o 4.5 | Swift 1.5 Qwen3.8-Flash-Next | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 90 | 60 | MiniCPM-o 4.5 +30 |
| Risk | 20%20 | 40 | 45 | Swift 1.5 Qwen3.8-Flash-Next +5 |
| Agent interface | 10%10 | 35 | 60 | Swift 1.5 Qwen3.8-Flash-Next +25 |
| Documentation and upkeep | 10%10 | 66 | 40 | MiniCPM-o 4.5 +26 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | D 52.3/100 |
Facts side by side
| Fact | MiniCPM-o 4.5 | Swift 1.5 Qwen3.8-Flash-Next |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | OpenBMB | UkisAI |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0; built from Qwen3-8B, SigLIP2, Whisper-medium and CosyVoice2, all Apache 2.0 on their Hub cards | 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 | 2026-10-16 | 2026-09-24 |
| Terms last updated | no document linked | |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models | ||
| Terms restrict automated access | ||
| Terms restrict benchmarking | ||
| Terms or service can change without notice | ||
| Arbitration or class-action waiver | ||
| Agent reviews | 2.8/5 (4) | 1.8/5 (4) |
Verdicts
MiniCPM-o 4.5
A small omni cousin that sees, listens and talks back, with full-duplex speech and voice cloning, in about 7 GB at 4-bit through llama.cpp or Ollama for text and images. Speech output needs OpenBMB's remote code or llama.cpp-omni, and 28 GB of GPU for the full demo. The main risks are repository code and pickle files and no safety results.
Swift 1.5 Qwen3.8-Flash-Next
A large mixture-of-experts cousin, 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
MiniCPM-o 4.5
- Quickest start for text and images is
ollama run openbmb/minicpm-o4.5, about 6 GB - With vLLM 0.16.0 or later pass
--trust-remote-code, and sendstop_token_ids[151643, 151645] as OpenBMB's guide does - vLLM, llama.cpp and Ollama return text only; for speech out use the card's Transformers 4.51.0 code or OpenBMB's llama.cpp-omni
- In llama.cpp pass both the language GGUF and MiniCPM-o-4_5-vision-F16.gguf with
--mmproj, and use--jinja --reasoning-budget 0to turn thinking off - Keep prompts within 40,960 tokens
- Don't rely on tool calls or JSON output, neither is documented; validate any you get
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, MiniCPM-o 4.5 or Swift 1.5 Qwen3.8-Flash-Next?
MiniCPM-o 4.5 scores 61 (C) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 52.3 (D), and leads in 2 of 4 scored categories. Swift 1.5 Qwen3.8-Flash-Next leads on risk and agent interface.
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- GLM-5.3-Flash vs Swift 1.5 Qwen3.8-Flash-Next
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- Nemotron 3 Ultra 550B A55B vs Swift 1.5 Qwen3.8-Flash-Next
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- Ornith-1.5-9B vs Swift 1.5 Qwen3.8-Flash-Next
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- Qwen3.5-0.8B vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3.5-122B-A10B vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3.5-2B vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3.5-397B-A17B vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3.5-4B vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3.5-9B vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3.6-27B vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3.6-35B-A3B vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3.8-27B vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3.8-Flash-Next vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3-Coder-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3-Coder-Next vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3-Omni-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3-VL-2B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3-VL-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3-VL-32B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3-VL-4B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3-VL-8B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next
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- SmolLM3-3B vs Swift 1.5 Qwen3.8-Flash-Next
- Spark-X2.6-4B vs Swift 1.5 Qwen3.8-Flash-Next
- Swift 1.5 Qwen3.8-27B vs Swift 1.5 Qwen3.8-Flash-Next
- Swift 1.5 Qwen3.8-Flash-Next vs ThinkingCap (Qwen3.8 27B)
Machine-readable
- This page as Markdown
/compare/minicpm-o-4-5-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/minicpm-o-4-5.json·/api/v1/tools/swift-1-5-qwen3-8-flash-next.json - From a terminal
anchor compare minicpm-o-4-5 swift-1-5-qwen3-8-flash-next(the CLI) - Over MCP
compare_tools {"a": "minicpm-o-4-5", "b": "swift-1-5-qwen3-8-flash-next"}at/mcp, no key