Head to head · Open-weight models you run yourself · October 2026 research run
Ling-3.1-flash-1019 vs MiniCPM5-2B
MiniCPM5-2B scores 59.7 (C) on agent readiness against Ling-3.1-flash-1019's 54.8 (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 4969 open weights comparisons
Which one, for what
Best for Teams with a multi-GPU server that want an MIT-licensed flagship for long agent runs and coding
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
574 GB in FP8 with no smaller build from inclusionAI, so no single machine under 690 GB of GPU memory runs it
Best for On-device and laptop agents that want a 2 GB tool-calling helper with long context and open training data
Ahead on
- Reach, 90 against 60
- Risk, 55 against 45
- Agent interface, 75 against 60
- Documentation and upkeep, 92 against 56
Watch for
OpenBMB documents only a Think mode for the 2B, so every answer starts with reasoning
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 MiniCPM5-2B +36
Ling-3.1-flash-1019 56
Model card 11, Intended use (complex reasoning, multi-step tasks and agentic workflows), architecture, parameters (560B total, 25B active) and context (1M claimed) are on the card. Training data 15, the training-content summary the card links names the source categories (Common Crawl and other public datasets, commercially licensed data, the Ant Spider crawler, synthetic data), the size band (over 10 trillion text tokens), data collected up to about July 2026 and the filtering, without listing datasets with sizes. Last change 20, the newest commit, on 4 November 2026, edited the card, six days before this check, and the files went up on 27 October. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (both opened on 27 October 2026) has a reply from an inclusionAI member. Version policy 5, the update went into a repository of its own and Ling-3.1-flash and Ling 3.0 stay in theirs. File health 5, the card states a 1M-token context while config.json gives 262,144 with no rope scaling, and its serve commands name the earlier repository as the model path. 11 + 15 + 20 + 0 + 5 + 5 = 56.
MiniCPM5-2B 92
Model card 17, everything but a knowledge cut-off. Training data 20, all eight training datasets are published on Hugging Face and named per stage with sizes. Last change 15, a template fix on 29 September 2026, 36 days ago, with nothing newer in OpenBMB's MiniCPM5 repositories on Hugging Face (builds last changed 12 September) or its GitHub repository (21 September). Responsiveness 20, OpenBMB members answered 16 of the 23 questions in the discussions, the newest reply on 29 September. Version policy 10, earlier models and checkpoints sit in their own repositories and the GitHub changelog dates each release. File health 10, config, tokenizer, template and card agree and no broken-file report waited on 21 October, though OpenBMB's GGUF, MLX and GPTQ repositories still carry the template from before the 29 September fix.
Reach MiniCPM5-2B +30
Ling-3.1-flash-1019 60
Memory 0, inclusionAI's only build is the FP8 release at 573.78 GB, × 1.2 = 688.5 GB, above 256 GB, and no runtime's library has a build. Runtimes 25, Released model code in Transformers 5.22.2 (bailing_hybrid), vLLM 0.33.0 (BailingMoeV3ForCausalLM, with ling3 tool and reasoning parsers), SGLang 0.5.23 (bailing_moe_v3.py, its bailing_hybrid config and MTP module) and llama.cpp b12242 (bailingmoe3), and Ollama, credited through its pinned llama.cpp as on the Ling-3.0-tiny listing: v0.40.16 sets LLAMA_CPP_VERSION to b12177. Builds 10, the release is FP8, the one quantised precision inclusionAI publishes for it. Terms 15, MIT in the card's metadata, with no use policy or other terms added on the card or in the repository. Download 10, the Hub API's model info gives gated false on 10 November 2026, so the files download with no account and no terms to accept. Hosted 0, the Hub API's provider mapping is empty for this repository on 10 November 2026. 0 + 25 + 10 + 15 + 10 + 0 = 60.
MiniCPM5-2B 90
Memory 30, OpenBMB's own MLX 4-bit build is 1.42 GB and its Q4_K_M GGUF 1.56 GB, about 2 GB with the fifth added. Runtimes 25, the cap, since the model is a stock Llama and vLLM, SGLang, released Transformers, a released llama.cpp and Ollama all run it. Builds 15, OpenBMB ships GGUF at three precisions plus MLX and GPTQ 4-bit. Terms 10, the LICENSE is plain Apache 2.0 but the card prohibits jailbreaking and injection testing and limits use to ethical purposes, which reads as a use policy on top (dated 21 October 2026). Download 10, nothing is gated. Hosted 0, OpenBMB has no API for it, no inference provider is listed on its Hugging Face page and OpenRouter doesn't carry it.
Agent interface MiniCPM5-2B +15
Ling-3.1-flash-1019 60
Chat template 20, chat_template.jinja in the repository (6,042 characters), which handles tools, the thinking switch and preserved reasoning. Tool-calling format 10, only the template says how tools are declared (JSON in <tools>) and how calls come back (<tool_call>name with <arg_key> and <arg_value> pairs). Trained tool calling 20, the template tells the model it may call one or more functions and loops over every entry in an assistant message's tool_calls, and the card reports gains on agentic benchmarks, AA-Briefcase and AutomationBench-AA among them. Structured output 0, the card doesn't document JSON or JSON Schema output. Reasoning control 0, the card documents turning thinking on or off per request with chat_template_kwargs enable_thinking, and names the ling3 reasoning parser, but doesn't say where the reasoning text comes back. Context 10, 262,144 tokens natively in config.json (max_position_embeddings 262144, rope_scaling null) and per SGLang's cookbook. 20 + 10 + 20 + 0 + 0 + 10 = 60.
MiniCPM5-2B 75
Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card and OpenBMB's cookbooks document the XML call format, the tools request and the minicpm5 parser in SGLang and vLLM. Trained tool calling 20, agent SFT on 500K samples and a template that renders several calls in one turn. Structured output 0, nothing documents asking for JSON. Reasoning control 0, a judgement call, because OpenBMB's own cookbooks call Think the only supported mode for the 2B even though the template accepts enable_thinking false. Context 10, 131,072 tokens native with no rope scaling.
Risk MiniCPM5-2B +10
Ling-3.1-flash-1019 45
File safety 15, safetensors only, no .bin, .pt or .pth files (25), less 10 because the card's documented vLLM command passes --trust-remote-code, which runs the repository's configuration_bailing_moe_v3.py, since vLLM 0.33.0 registers no bailing_hybrid config of its own. Provenance 15, files in the inclusionAI organisation that inclusion-ai.org links to, per the Ling-3.0-tiny listing (10). Safety evaluation 0, no safety results for the model on its card or in the training-content summary. Agent guidance 0, the card has nothing on prompt injection, refusals or safeguards, and inclusionAI publishes no guardrail probe for this model as it did for Ling 3.0. Licence risk 15, MIT with no use policy, no field-of-use limits, no threshold and no clause letting inclusionAI change or revoke it. Security programme 0, www.inclusion-ai.org answers 404 for security.txt on 10 November 2026, and the Ling-3.0-tiny listing found no SECURITY.md or disclosure policy in the inclusionAI .github repository. 15 + 15 + 0 + 0 + 15 + 0 = 45.
MiniCPM5-2B 55
File safety 25, safetensors and GGUF only, and the documented way to load it runs no code from the repository. Provenance 15, the openbmb organisation linked from OpenBMB's GitHub, with a SHA-256 for every weight file. Safety evaluation 0, nothing published. Agent guidance 5, the card only cautions that prompts can manipulate the model and that users should add their own safeguards. Licence risk 10, 5 off for the card's use limit beyond the law. Security programme 0, no SECURITY.md, no security.txt and no advisories.
Score by category
| Category | Weight this run | Ling-3.1-flash-1019 | MiniCPM5-2B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 60 | 90 | MiniCPM5-2B +30 |
| Risk | 20%20 | 45 | 55 | MiniCPM5-2B +10 |
| Agent interface | 10%10 | 60 | 75 | MiniCPM5-2B +15 |
| Documentation and upkeep | 10%10 | 56 | 92 | MiniCPM5-2B +36 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 54.8/100 | C 59.7/100 |
Facts side by side
| Fact | Ling-3.1-flash-1019 | MiniCPM5-2B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | inclusionAI (Ant Group) | OpenBMB |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-11-04 | 2026-09-29 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 3/5 (4) |
How the scores have moved
Ling-3.1-flash-1019
No score change since it was listed.
Verdicts
Ling-3.1-flash-1019
inclusionAI's newest flagship, a 560B hybrid mixture of experts under plain MIT that vLLM, SGLang, Transformers and llama.cpp run in released versions. It ships only as 574 GB of FP8, so it needs a multi-GPU server. The card overstates the context, documents little about tool calls, and has no safety results.
MiniCPM5-2B
A 2B dense model for local tool calling and long context, with published training data. OpenBMB's own 4-bit builds are under 1.6 GB, and the model loads in vLLM, SGLang, llama.cpp, Ollama and Transformers. The main risks are always-on thinking, no safety results and use conditions beyond Apache 2.0.
Before you call either
Ling-3.1-flash-1019
- Serve it with SGLang 0.5.23 or later on 8 H200 or 4 GB300 GPUs, per the card, and use this repository's name as the model path, not the earlier one the card's command shows
- With vLLM pass
--trust-remote-code,--tool-call-parser ling3and--reasoning-parser ling3 - Set
chat_template_kwargsenable_thinkingfalse for direct answers. Reasoning comes back inreasoning_contentper SGLang's cookbook - Plan on 262,144 tokens of context. The 1M figure needs the YaRN override in SGLang's cookbook
- Use temperature 1.0, top_p 0.95 and top_k 20, per the card
- Validate JSON output yourself, since structured output isn't documented
MiniCPM5-2B
- Smallest start is
ollama run openbmb/minicpm5-2b(1.6 GB, q4_k_m) - On vLLM 0.29.0 or later run
vllm serve openbmb/MiniCPM5-2B --max-model-len 131072 --enable-auto-tool-choice --tool-call-parser minicpm5, or tool calls come back as XML text - On SGLang 0.5.16 or later add
--tool-call-parser minicpm5 --reasoning-parser qwen3; SGLang is OpenBMB's recommended backend for tool calls - Tool calls are XML,
<function name=...><param name=...>, not Hermes JSON - Sample with temperature 1.0, top_p 0.95 and min_p 0.0; llama.cpp's default min_p 0.05 can loop, and repetition_penalty 1.05 helps if it does
- Expect a
<think>block before every answer and leave room in max_tokens; enable_thinking false is accepted by the template but OpenBMB doesn't support it for the 2B - Validate JSON yourself or use the runtime's guided decoding, since structured output isn't documented
- Use the main repository's template; the GGUF, MLX and GPTQ copies predate the 29 September fix
Questions
Which is better for AI agents, Ling-3.1-flash-1019 or MiniCPM5-2B?
MiniCPM5-2B scores 59.7 (C) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in every scored category.
Other comparisons with Ling-3.1-flash-1019 or MiniCPM5-2B
- Agents-A1-4B vs Ling-3.1-flash-1019
- Agents-A1-4B vs MiniCPM5-2B
- Apertus v1.5 8B vs Ling-3.1-flash-1019
- Apertus v1.5 8B vs MiniCPM5-2B
- Apodex-1.1-mini vs Ling-3.1-flash-1019
- Apodex-1.1-mini vs MiniCPM5-2B
- Arete-27B vs Ling-3.1-flash-1019
- Arete-27B vs MiniCPM5-2B
- BigBang-v1 vs Ling-3.1-flash-1019
- BigBang-v1 vs MiniCPM5-2B
- Carbon-X vs Ling-3.1-flash-1019
- Carbon-X vs MiniCPM5-2B
- Cosmos-Reason2-2B vs Ling-3.1-flash-1019
- Cosmos-Reason2-2B vs MiniCPM5-2B
- Darwin-36B-Opus vs Ling-3.1-flash-1019
- Darwin-36B-Opus vs MiniCPM5-2B
- DeepSeek-V3.2 vs Ling-3.1-flash-1019
- DeepSeek-V3.2 vs MiniCPM5-2B
- DeepSeek V4.1 Flash vs Ling-3.1-flash-1019
- DeepSeek V4.1 Flash vs MiniCPM5-2B
- DeepSeek-V4-Flash-0731 vs Ling-3.1-flash-1019
- DeepSeek-V4-Flash-0731 vs MiniCPM5-2B
- DiffusionGemma 26B A4B IT vs Ling-3.1-flash-1019
- DiffusionGemma 26B A4B IT vs MiniCPM5-2B
- Gemma 4 12B IT vs Ling-3.1-flash-1019
- Gemma 4 12B IT vs MiniCPM5-2B
- Gemma 4 26B A4B IT vs Ling-3.1-flash-1019
- Gemma 4 26B A4B IT vs MiniCPM5-2B
- Gemma 4 31B IT vs Ling-3.1-flash-1019
- Gemma 4 31B IT vs MiniCPM5-2B
- Gemma 4 E2B IT vs Ling-3.1-flash-1019
- Gemma 4 E2B IT vs MiniCPM5-2B
- Gemma 4 E4B IT vs Ling-3.1-flash-1019
- Gemma 4 E4B IT vs MiniCPM5-2B
- GLM-4.5-Air vs Ling-3.1-flash-1019
- GLM-4.5-Air vs MiniCPM5-2B
- GLM-4.7-Flash vs Ling-3.1-flash-1019
- GLM-4.7-Flash vs MiniCPM5-2B
- GLM-5.3-Flash vs Ling-3.1-flash-1019
- GLM-5.3-Flash vs MiniCPM5-2B
- GLM-5.3 vs Ling-3.1-flash-1019
- GLM-5.3 vs MiniCPM5-2B
- gpt-oss-120b vs Ling-3.1-flash-1019
- gpt-oss-120b vs MiniCPM5-2B
- gpt-oss-20b vs Ling-3.1-flash-1019
- gpt-oss-20b vs MiniCPM5-2B
- Granite 4.2 30B vs Ling-3.1-flash-1019
- Granite 4.2 30B vs MiniCPM5-2B
- Granite 4.2 3B vs Ling-3.1-flash-1019
- Granite 4.2 3B vs MiniCPM5-2B
- Granite 4.2 8B vs Ling-3.1-flash-1019
- Granite 4.2 8B vs MiniCPM5-2B
- Hy-Memory-30B-A3B vs Ling-3.1-flash-1019
- Hy-Memory-30B-A3B vs MiniCPM5-2B
- Hy3 vs Ling-3.1-flash-1019
- Hy3 vs MiniCPM5-2B
- Inkling-Small vs Ling-3.1-flash-1019
- Inkling-Small vs MiniCPM5-2B
- Inkling vs Ling-3.1-flash-1019
- Inkling vs MiniCPM5-2B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Ling-3.1-flash-1019
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiniCPM5-2B
- Kimi K3.1 Pro vs Ling-3.1-flash-1019
- Kimi K3.1 Pro vs MiniCPM5-2B
- Kimi K3 vs Ling-3.1-flash-1019
- Kimi K3 vs MiniCPM5-2B
- Kolibri-1 vs Ling-3.1-flash-1019
- Kolibri-1 vs MiniCPM5-2B
- Kronos vs Ling-3.1-flash-1019
- Kronos vs MiniCPM5-2B
- Laguna M.1 vs Ling-3.1-flash-1019
- Laguna M.1 vs MiniCPM5-2B
- Laguna S 2.1 vs Ling-3.1-flash-1019
- Laguna S 2.1 vs MiniCPM5-2B
- Laguna XS 2.1 vs Ling-3.1-flash-1019
- Laguna XS 2.1 vs MiniCPM5-2B
- LBS-27B vs Ling-3.1-flash-1019
- LBS-27B vs MiniCPM5-2B
- LFM2.5-2.6B vs Ling-3.1-flash-1019
- LFM2.5-2.6B vs MiniCPM5-2B
- LFM3-1.7B vs Ling-3.1-flash-1019
- LFM3-1.7B vs MiniCPM5-2B
- LFM3-Audio-2B vs Ling-3.1-flash-1019
- LFM3-Audio-2B vs MiniCPM5-2B
- LFM3-Omni-2B vs Ling-3.1-flash-1019
- LFM3-Omni-2B vs MiniCPM5-2B
- LFM3-VL-2B vs Ling-3.1-flash-1019
- LFM3-VL-2B vs MiniCPM5-2B
- Ling-3.0-tiny vs Ling-3.1-flash-1019
- Ling-3.0-tiny vs MiniCPM5-2B
- Ling-3.1-flash-1019 vs MiMo-V2.6 Distill Qwen 9B
- Ling-3.1-flash-1019 vs MiniCPM-o 4.5
- Ling-3.1-flash-1019 vs MiniCPM-V 4.7 1B
- Ling-3.1-flash-1019 vs MiniCPM-V 4.7 35B-A3B
- Ling-3.1-flash-1019 vs MiniMax-M2.5
- Ling-3.1-flash-1019 vs MiniMax-M2.7
- Ling-3.1-flash-1019 vs MiniMax-M3.1-Flash
- Ling-3.1-flash-1019 vs Mistral Large 4
- Ling-3.1-flash-1019 vs Muse Glimmer 30B
- Ling-3.1-flash-1019 vs Nanbeige4.3-33B
- Ling-3.1-flash-1019 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- Ling-3.1-flash-1019 vs Nemotron 3.5 Lightning 30B A3B
- Ling-3.1-flash-1019 vs Nemotron 3 Nano 30B A3B
- Ling-3.1-flash-1019 vs Nemotron 3 Nano 4B
- Ling-3.1-flash-1019 vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Ling-3.1-flash-1019 vs Nemotron 3 Super 120B A12B
- Ling-3.1-flash-1019 vs Nemotron 3 Ultra 550B A55B
- Ling-3.1-flash-1019 vs occamy-1.0
- Ling-3.1-flash-1019 vs Olmo 3 7B Think
- Ling-3.1-flash-1019 vs Ornith-1.5-122B
- Ling-3.1-flash-1019 vs Ornith-1.5-35B-A3B
- Ling-3.1-flash-1019 vs Ornith-1.5-397B
- Ling-3.1-flash-1019 vs Ornith-1.5-9B
- Ling-3.1-flash-1019 vs Qwen-AgentWorld-35B-A3B
- Ling-3.1-flash-1019 vs Qwen3-30B-A3B-Instruct-2507
- Ling-3.1-flash-1019 vs Qwen3-4B-Instruct-2507
- Ling-3.1-flash-1019 vs Qwen3.5-0.8B
- Ling-3.1-flash-1019 vs Qwen3.5-122B-A10B
- Ling-3.1-flash-1019 vs Qwen3.5-2B
- Ling-3.1-flash-1019 vs Qwen3.5-397B-A17B
- Ling-3.1-flash-1019 vs Qwen3.5-4B
- Ling-3.1-flash-1019 vs Qwen3.5-9B
- Ling-3.1-flash-1019 vs Qwen3.6-27B
- Ling-3.1-flash-1019 vs Qwen3.6-35B-A3B
- Ling-3.1-flash-1019 vs Qwen3.8-27B
- Ling-3.1-flash-1019 vs Qwen3.8-Flash-Next
- Ling-3.1-flash-1019 vs Qwen3-Coder-30B-A3B-Instruct
- Ling-3.1-flash-1019 vs Qwen3-Coder-Next
- Ling-3.1-flash-1019 vs Qwen3-Omni-30B-A3B-Instruct
- Ling-3.1-flash-1019 vs Qwen3-VL-2B-Instruct
- Ling-3.1-flash-1019 vs Qwen3-VL-30B-A3B-Instruct
- Ling-3.1-flash-1019 vs Qwen3-VL-32B-Instruct
- Ling-3.1-flash-1019 vs Qwen3-VL-4B-Instruct
- Ling-3.1-flash-1019 vs Qwen3-VL-8B-Instruct
- Ling-3.1-flash-1019 vs Qwopus3.8-27B-Flash-V2
- Ling-3.1-flash-1019 vs Recursive Flint
- Ling-3.1-flash-1019 vs Ripple-65B Preview
- Ling-3.1-flash-1019 vs SmolLM3-3B
- Ling-3.1-flash-1019 vs Spark-X2.6-4B
- Ling-3.1-flash-1019 vs Swift 1.5 Qwen3.8-27B
- Ling-3.1-flash-1019 vs Swift 1.5 Qwen3.8-Flash-Next
- Ling-3.1-flash-1019 vs ThinkingCap (Qwen3.8 27B)
- Ling-3.1-flash-1019 vs Baidu Unlimited-OCR-2
- MiMo-V2.6 Distill Qwen 9B vs MiniCPM5-2B
- MiniCPM-o 4.5 vs MiniCPM5-2B
- MiniCPM-V 4.7 1B vs MiniCPM5-2B
- MiniCPM-V 4.7 35B-A3B vs MiniCPM5-2B
- MiniCPM5-2B vs MiniMax-M2.5
- MiniCPM5-2B vs MiniMax-M2.7
- MiniCPM5-2B vs MiniMax-M3.1-Flash
- MiniCPM5-2B vs Mistral Large 4
- MiniCPM5-2B vs Muse Glimmer 30B
- MiniCPM5-2B vs Nanbeige4.3-33B
- MiniCPM5-2B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- MiniCPM5-2B vs Nemotron 3.5 Lightning 30B A3B
- MiniCPM5-2B vs Nemotron 3 Nano 30B A3B
- MiniCPM5-2B vs Nemotron 3 Nano 4B
- MiniCPM5-2B vs Nemotron 3 Nano Omni 30B A3B Reasoning
- MiniCPM5-2B vs Nemotron 3 Super 120B A12B
- MiniCPM5-2B vs Nemotron 3 Ultra 550B A55B
- MiniCPM5-2B vs occamy-1.0
- MiniCPM5-2B vs Olmo 3 7B Think
- MiniCPM5-2B vs Ornith-1.5-122B
- MiniCPM5-2B vs Ornith-1.5-35B-A3B
- MiniCPM5-2B vs Ornith-1.5-397B
- MiniCPM5-2B vs Ornith-1.5-9B
- MiniCPM5-2B vs Qwen-AgentWorld-35B-A3B
- MiniCPM5-2B vs Qwen3-30B-A3B-Instruct-2507
- MiniCPM5-2B vs Qwen3-4B-Instruct-2507
- MiniCPM5-2B vs Qwen3.5-0.8B
- MiniCPM5-2B vs Qwen3.5-122B-A10B
- MiniCPM5-2B vs Qwen3.5-2B
- MiniCPM5-2B vs Qwen3.5-397B-A17B
- MiniCPM5-2B vs Qwen3.5-4B
- MiniCPM5-2B vs Qwen3.5-9B
- MiniCPM5-2B vs Qwen3.6-27B
- MiniCPM5-2B vs Qwen3.6-35B-A3B
- MiniCPM5-2B vs Qwen3.8-27B
- MiniCPM5-2B vs Qwen3.8-Flash-Next
- MiniCPM5-2B vs Qwen3-Coder-30B-A3B-Instruct
- MiniCPM5-2B vs Qwen3-Coder-Next
- MiniCPM5-2B vs Qwen3-Omni-30B-A3B-Instruct
- MiniCPM5-2B vs Qwen3-VL-2B-Instruct
- MiniCPM5-2B vs Qwen3-VL-30B-A3B-Instruct
- MiniCPM5-2B vs Qwen3-VL-32B-Instruct
- MiniCPM5-2B vs Qwen3-VL-4B-Instruct
- MiniCPM5-2B vs Qwen3-VL-8B-Instruct
- MiniCPM5-2B vs Qwopus3.8-27B-Flash-V2
- MiniCPM5-2B vs Recursive Flint
- MiniCPM5-2B vs Ripple-65B Preview
- MiniCPM5-2B vs SmolLM3-3B
- MiniCPM5-2B vs Spark-X2.6-4B
- MiniCPM5-2B vs Swift 1.5 Qwen3.8-27B
- MiniCPM5-2B vs Swift 1.5 Qwen3.8-Flash-Next
- MiniCPM5-2B vs ThinkingCap (Qwen3.8 27B)
- MiniCPM5-2B vs Baidu Unlimited-OCR-2
Machine-readable
- This page as Markdown
/compare/ling-3-1-flash-1019-vs-minicpm5-2b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/ling-3-1-flash-1019.json·/api/v1/tools/minicpm5-2b.json - From a terminal
anchor compare ling-3-1-flash-1019 minicpm5-2b(the CLI) - Over MCP
compare_tools {"a": "ling-3-1-flash-1019", "b": "minicpm5-2b"}at/mcp, no key