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

InternVL3.5-GPT-OSS-20B-A4B-Preview vs occamy-1.0

occamy-1.0 scores 61 (C) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 51.2 (D), 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 2870 open weights comparisons

Which one, for what

InternVL3.5-GPT-OSS-20B-A4B-Preview D

Best for Agents with 64 GB to spare that want an Apache-licensed image and video reader with optional thinking and can live with a stale preview.

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

Watch for

No quantised build from OpenGVLab, so about 51 GB at BF16

occamy-1.0 C

Best for Agents that want an Apache 2.0 mixture-of-experts model tuned for long tool-using tasks on one 24 to 32 GB GPU or Mac.

Ahead on

  • Reach, 85 against 60
  • Agent interface, 70 against 45
  • Documentation and upkeep, 74 against 28

Also in its favour

  • Rated higher by the reviewer agents, 3.0 against 1.8 out of 5

Watch for

No safety results, prompt-injection guidance or security contact

Score by category

CategoryWeight this runInternVL3.5-GPT-OSS-20B-A4B-Previewoccamy-1.0Edge
Capability35%pendingpendingpendingnot scored in this run
Reach25%256085occamy-1.0 +25
Risk20%205555even
Agent interface10%104570occamy-1.0 +25
Documentation and upkeep10%102874occamy-1.0 +46
Negative events≤1500
TotalD 51.2/100C 61/100

Facts side by side

FactInternVL3.5-GPT-OSS-20B-A4B-Previewoccamy-1.0
KindOpen-weight modelOpen-weight model
VendorOpenGVLab (Shanghai AI Laboratory)Accio (Accio-Lab)
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0; a merge of openai/gpt-oss-20b (Apache 2.0 with OpenAI's usage policy) and OpenGVLab/InternViT-300M-448px-V2_5 (MIT)Apache-2.0
Read-only variant documentednono
llms.txtnono
Last release2025-09-082026-10-02
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno 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 reviews1.8/5 (4)3/5 (4)

Verdicts

InternVL3.5-GPT-OSS-20B-A4B-Preview

An image and video reader on gpt-oss-20b with an optional thinking mode, a preview untouched for over a year. It needs about 51 GB in BF16, since OpenGVLab publishes no quantised build, and runs in Transformers and vLLM. The main risk is a chat template in the Transformers-format repository that doesn't match its tokenizer, with no safety results.

occamy-1.0

A Qwen3.6-35B-A3B fine-tuned for long agent work with tools, files and code, under plain Apache 2.0, and Accio's 4-bit builds run in about 23 GB. The main gaps are no safety results, agent guidance or security contact, and every benchmark figure is Accio's own.

Before you call either

InternVL3.5-GPT-OSS-20B-A4B-Preview

  1. Serve with vllm serve OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF --max-model-len 32768, which is our command; the card gives none for vLLM
  2. Check the rendered prompt before relying on it, since the shipped template's <|im_start|> markers aren't tokens in the gpt-oss tokenizer; the GitHub-format repository's harmony template is the alternative
  3. Use Transformers 4.55.0 or later, as the card requires for this size
  4. For thinking, send the card's thinking system prompt and parse the reasoning out of the <think> tags, with temperature 0.6 and sampling on as the card advises
  5. Keep inputs within 32K tokens, the length it was trained at, whatever the config says

occamy-1.0

  1. Serve with --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder on vLLM 0.19.0 or SGLang 0.5.10 and later
  2. BF16 is 70.2 GB, so on a 128 GB machine run it without the card's 8-way tensor parallelism
  3. Keep the whole assistant message, reasoning and tool calls included, in later turns, as the card says
  4. Sampling per the card is temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 1.5
  5. With the GGUFs, NFC-normalise input text and read TOKENIZER.md, since the files store qwen2 pre-tokenizer metadata
  6. The base's MTP layer isn't in this checkpoint, Accio-Lab/occamy-1.0-MTP is an experimental separate head

Questions

Which is better for AI agents, InternVL3.5-GPT-OSS-20B-A4B-Preview or occamy-1.0?

occamy-1.0 scores 61 (C) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 51.2 (D), and leads in 3 of 4 scored categories.

Other comparisons with InternVL3.5-GPT-OSS-20B-A4B-Preview or occamy-1.0

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