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Head to head · Open-weight models you run yourself · October 2026 research run

Kronos vs MiniCPM-V 4.7 35B-A3B

MiniCPM-V 4.7 35B-A3B scores 61 (C) on agent readiness against Kronos's 55.2 (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 4969 open weights comparisons

KronosYu Shi (shiyu-coder, NeoQuasar), Tsinghua University · Open-weight model C55.2/100#104 of 135 open-weight models MiniCPM-V 4.7 35B-A3BOpenBMB · Open-weight model C61/100#69 of 135 open-weight models

Which one, for what

Kronos C

Best for Agents that need a quick local forecast or synthetic sample of a candlestick series on modest hardware, for research or as one input among others.

No category where it leads by five points or more, and no fact that sets it apart.

Watch for

Runs only through the publisher's own PyTorch code from GitHub, in no named runtime and with no quantised build

MiniCPM-V 4.7 35B-A3B C

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 55
  • Risk, 55 against 50
  • Agent interface, 70 against 60
  • Documentation and upkeep, 68 against 61

Watch for

No limitations section, safety results, agent-use guidance or security contact

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 MiniCPM-V 4.7 35B-A3B +35

Kronos 55

Memory 30, the publisher's Kronos-mini with its tokeniser is 0.03 GB and even Kronos-base is 0.43 GB in FP32. Runtimes 0, it runs only through the publisher's own PyTorch package from GitHub, and none of the six named runtimes supports it. Builds 0, FP32 safetensors only. Terms 15, MIT with nothing added. Download 10, nothing is gated. Hosted 0, no inference provider or publisher API serves it (provider mapping still empty on 4 November).

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.

Agent interface MiniCPM-V 4.7 35B-A3B +10

Kronos 60

Read for a forecasting model, the tool-calling, trained tool calling and structured-output lines stand for what it takes and returns. The card documents the DataFrame and timestamps it takes and the DataFrame it returns, and the publisher's KronosPredictor builds and parses them (25). It was trained on six K-line fields with calendar fields, one series per forecast (10). Output is a typed DataFrame indexed by the requested timestamps, the mean of the sampled paths (15). The input format is given only as code (10). Reasoning control and context score 0, with no reasoning and a 512-bar context (2,048 for Kronos-mini).

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.

Documentation and upkeep MiniCPM-V 4.7 35B-A3B +7

Kronos 61

Model card 11, intended use, architecture, parameters and context are there, limitations, languages and a cut-off aren't. Training data 20, the paper lists every exchange with asset and observation counts, the filtering and a June 2024 end. Last change 5, the reference code on 13 April 2026 (still the newest commit on 4 November, 205 days) and the Hub repositories on 9 September 2025. Responsiveness 20, the publisher answered 10 of 18 Hugging Face discussions (recounted by script on 4 November), though none since 24 November 2025, and the seven opened since April 2026 have no reply from it. Version policy 5, a first release. File health 0, the cards point to example data the repository has deleted and to licence and requirements links on a branch that doesn't exist.

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.

Risk MiniCPM-V 4.7 35B-A3B +5

Kronos 50

File safety 15, safetensors only, but the only documented loading path is the publisher's Python package in place of a runtime. Provenance 15, the files are in the NeoQuasar account the publisher's README links, with SHA-256 hashes. Safety evaluation 0, none published. Agent guidance 5, the README's general cautions that the fine-tuning pipeline isn't a production trading system. Licence risk 15, plain MIT; the broken licence link is charged to file health. Security programme 0, no reporting route found.

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.

Score by category

CategoryWeight this runKronosMiniCPM-V 4.7 35B-A3BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%255590MiniCPM-V 4.7 35B-A3B +35
Risk20%205055MiniCPM-V 4.7 35B-A3B +5
Agent interface10%106070MiniCPM-V 4.7 35B-A3B +10
Documentation and upkeep10%106168MiniCPM-V 4.7 35B-A3B +7
Negative events≤1500
TotalC 55.2/100C 61/100

Facts side by side

FactKronosMiniCPM-V 4.7 35B-A3B
KindOpen-weight modelOpen-weight model
VendorYu Shi (shiyu-coder, NeoQuasar), Tsinghua UniversityOpenBMB
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceMITApache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-04-132026-11-03
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Popularity43k starsnone

Verdicts

Kronos

Forecasts the next price bars from a history of up to 512 candlestick bars. Every size fits in under 0.5 GB and runs on a CPU, but only through the publisher's own Python package. Nobody has benchmarked it independently, its quickstart data was deleted from the repository, and the publisher hasn't replied since November 2025.

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.

Before you call either

Kronos

  1. Clone https://github.com/shiyu-coder/Kronos and pip install -r requirements.txt, then load Kronos.from_pretrained("NeoQuasar/Kronos-small") and KronosTokenizer.from_pretrained("NeoQuasar/Kronos-Tokenizer-base") and wrap them in KronosPredictor
  2. Pair Kronos-mini with Kronos-Tokenizer-2k and Kronos-small or Kronos-base with Kronos-Tokenizer-base, the cards' pairing
  3. The examples read examples/data/XSHG_5min_600977.csv, which no longer exists; point them at tests/data/regression_input.csv, 2,500 five-minute bars
  4. Keep the lookback at or under max_context, 512 bars, and pass the future timestamps you want as y_timestamp, since the calendar fields come from them
  5. Raise sample_count for a steadier forecast, but expect only the mean back; call the lower-level auto_regressive_inference yourself if you need the paths
  6. Treat forecasts as model output to check, not signals; the README says its own backtest pipeline isn't a production trading system

MiniCPM-V 4.7 35B-A3B

  1. Start it with llama-server -hf openbmb/MiniCPM-V-4.7-35B-A3B-gguf:Q4_K_M --jinja, or ollama run openbmb/minicpm-v4.7:35b-a3b
  2. For image input in llama.cpp add mmproj-model-f16.gguf, or the 4x projector for more visual detail at a higher cost
  3. The GGUF files at the repository root have thinking off. Use the files in thinking/ when you want the trace
  4. With Transformers pass enable_thinking to apply_chat_template, and pass downsample_mode to both apply_chat_template and generate
  5. Use temperature 0.7, top_p 0.95 and top_k 100 in instruct mode, as the card recommends
  6. Check tool calls in your runtime first, since the card names no parser. The template uses the Qwen3.5 function-block format
  7. Validate JSON output yourself, since structured output isn't documented

Questions

Which is better for AI agents, Kronos or MiniCPM-V 4.7 35B-A3B?

MiniCPM-V 4.7 35B-A3B scores 61 (C) on agent readiness against Kronos's 55.2 (C), and leads in every scored category.

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