Head to head · Open-weight models you run yourself · October 2026 research run
MiniCPM-V 4.7 35B-A3B vs Swift 1.5 Qwen3.8-Flash-Next
MiniCPM-V 4.7 35B-A3B scores 61 (C) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 54.2 (C), and leads in every scored category. 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 that need to read screenshots, documents and video on a local machine with 24 to 32 GB for the model, under a permissive licence
Ahead on
- Reach, 90 against 65
- Risk, 55 against 45
- Agent interface, 70 against 60
- Documentation and upkeep, 68 against 40
Watch for
No limitations section, safety results, agent-use guidance or security contact
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.
Documentation and upkeep MiniCPM-V 4.7 35B-A3B +28
MiniCPM-V 4.7 35B-A3B 68
Model card 8, it covers intended use, architecture and parameters but not limitations, context length, languages or a knowledge cut-off. Training data 0, none described. Last change 20, a commit on 3 November 2026. Responsiveness 20, OpenBMB answered 8 of 12 discussions. Version policy 10, earlier versions keep their repositories and the changes are dated and described. File health 10, the files agree. 8 + 0 + 20 + 20 + 10 + 10 = 68.
Swift 1.5 Qwen3.8-Flash-Next 40
Model card 5 of 20, intended use and context only. Training data 5, a general description with no cut-off. Last change 20, UkisAI added an IQ3_S tier to its GSQ-RCO GGUF build on 7 October 2026, 28 days before this check; the main repository's only commit is the 24 September release. Responsiveness 0, UkisAI answered none of 3 discussions. Version policy 5, a first release, renamed without a note. File health 5, the re-serialised tokenizer splits text differently from the repository's own tokenizer config and the base. 5 + 5 + 20 + 0 + 5 + 5 = 40.
Reach MiniCPM-V 4.7 35B-A3B +25
MiniCPM-V 4.7 35B-A3B 90
Memory 25, since OpenBMB's IQ4_XS GGUF with its vision projector is 19.6 GB and needs about 23.6 GB with the fifth added, which fits 24 GB, while its Q4_K_M GGUF, 4-bit safetensors builds and Ollama entry need 32 GB. Runtimes 25, from vLLM 0.33.0, SGLang 0.5.23, Transformers 5.18.0 and later, llama.cpp's own MiniCPM-V 4.7 support and OpenBMB's entry on Ollama. Builds 15, OpenBMB publishes FP8, three 4-bit safetensors builds and GGUF. Terms 15, Apache 2.0 with nothing added. Download 10, nothing is gated. Hosted 0, no inference provider is mapped. 25 + 25 + 15 + 15 + 10 + 0 = 90.
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 MiniCPM-V 4.7 35B-A3B +10
MiniCPM-V 4.7 35B-A3B 55
File safety 25, safetensors and GGUF only, no Python in the main repository and no remote code needed. Provenance 15, the files come from the openbmb organisation OpenBMB's GitHub README links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published on either, and the card has no limitations section. Licence risk 15, plain Apache 2.0. Security programme 0, no security.txt or SECURITY.md.
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.
Agent interface MiniCPM-V 4.7 35B-A3B +10
MiniCPM-V 4.7 35B-A3B 70
Chat template 20, a Jinja file in the repository. Tool-calling format 10, the card shows the tools array going in but not the call coming back, and names no parser. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented or claimed. Reasoning control 10, enable_thinking per request, off by default, with the trace between markers. Context 10, 262,144 tokens native.
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.
Score by category
| Category | Weight this run | MiniCPM-V 4.7 35B-A3B | Swift 1.5 Qwen3.8-Flash-Next | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 90 | 65 | MiniCPM-V 4.7 35B-A3B +25 |
| Risk | 20%20 | 55 | 45 | MiniCPM-V 4.7 35B-A3B +10 |
| Agent interface | 10%10 | 70 | 60 | MiniCPM-V 4.7 35B-A3B +10 |
| Documentation and upkeep | 10%10 | 68 | 40 | MiniCPM-V 4.7 35B-A3B +28 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 54.2/100 |
Facts side by side
| Fact | MiniCPM-V 4.7 35B-A3B | 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 | 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 | 2026-11-03 | 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
MiniCPM-V 4.7 35B-A3B
No score change since it was listed.
Verdicts
MiniCPM-V 4.7 35B-A3B
A vision-language cousin for images and video that fits a 24 to 32 GB machine from OpenBMB's own GGUF, under plain Apache 2.0, with a publisher that answers. The card says little about limits, training data or how tool calls come back, so test the tool format in your runtime before relying on it.
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
MiniCPM-V 4.7 35B-A3B
- Start it with
llama-server -hf openbmb/MiniCPM-V-4.7-35B-A3B-gguf:Q4_K_M --jinja, orollama run openbmb/minicpm-v4.7:35b-a3b - For image input in llama.cpp add
mmproj-model-f16.gguf, or the 4x projector for more visual detail at a higher cost - The GGUF files at the repository root have thinking off. Use the files in
thinking/when you want the trace - With Transformers pass
enable_thinkingtoapply_chat_template, and passdownsample_modeto bothapply_chat_templateandgenerate - Use temperature 0.7, top_p 0.95 and top_k 100 in instruct mode, as the card recommends
- Check tool calls in your runtime first, since the card names no parser. The template uses the Qwen3.5 function-block format
- Validate JSON output yourself, since structured output isn't documented
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-V 4.7 35B-A3B or Swift 1.5 Qwen3.8-Flash-Next?
MiniCPM-V 4.7 35B-A3B scores 61 (C) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 54.2 (C), and leads in every scored category.
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- OTel-2.0-LLM-31B-IT vs Swift 1.5 Qwen3.8-Flash-Next
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- Qwen3-30B-A3B-Instruct-2507 vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3-4B-Instruct-2507 vs Swift 1.5 Qwen3.8-Flash-Next
- 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
- Qwopus3.8-27B-Flash-V2 vs Swift 1.5 Qwen3.8-Flash-Next
- Recursive Flint vs Swift 1.5 Qwen3.8-Flash-Next
- Ripple-65B Preview vs Swift 1.5 Qwen3.8-Flash-Next
- SenseNova-Loop-9B-Preview vs Swift 1.5 Qwen3.8-Flash-Next
- SmolLM3-3B vs Swift 1.5 Qwen3.8-Flash-Next
- Spark-X2.6-4B vs Swift 1.5 Qwen3.8-Flash-Next
- Step 3.7 Flash 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)
- Swift 1.5 Qwen3.8-Flash-Next vs Baidu Unlimited-OCR-2
- Swift 1.5 Qwen3.8-Flash-Next vs WeVisOne-VL-2B
- Swift 1.5 Qwen3.8-Flash-Next vs WeVisOne-VL-4B
- Swift 1.5 Qwen3.8-Flash-Next vs ZAYA1-8B
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/api/v1/tools/minicpm-v-4-7-35b-a3b.json·/api/v1/tools/swift-1-5-qwen3-8-flash-next.json - From a terminal
anchor compare minicpm-v-4-7-35b-a3b swift-1-5-qwen3-8-flash-next(the CLI) - Over MCP
compare_tools {"a": "minicpm-v-4-7-35b-a3b", "b": "swift-1-5-qwen3-8-flash-next"}at/mcp, no key