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

Ripple-65B Preview vs ThinkingCap (Qwen3.8 27B)

ThinkingCap (Qwen3.8 27B) scores 61 (C) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in 2 of 4 scored categories. Ripple-65B Preview leads on risk and agent interface. Both do open-weight models you run yourself.

Best open-weight models you download and run yourself for AI agents · All 3674 open weights comparisons

Which one, for what

Ripple-65B Preview D

Best for Teams with Blackwell GPUs or a DGX Spark who want an Apache 2.0 coding model to try for terminal and agentic coding work, and can run the publisher's code to serve it.

Ahead on

  • Risk, 55 against 45
  • Agent interface, 75 against 70

Watch for

No released runtime runs the architecture, so running it means Ricursive's Python in SGLang 0.5.19 or Transformers' remote code

ThinkingCap (Qwen3.8 27B) C

Best for Small teams and individuals running Qwen3.8-27B for agent or reasoning work who want shorter thinking traces on one 24 GB GPU or Mac.

Ahead on

  • Reach, 75 against 45

Watch for

PolyForm Small Business licence, free commercially only below 100 people and 1,000,000 USD (2019) revenue

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 ThinkingCap (Qwen3.8 27B) +30

Ripple-65B Preview 45

Memory 10 of 30, the smallest 4-bit build is Ricursive's NVFP4 at 57.2 GB, 68.7 GB with a fifth added, which is over 64 GB and fits our reference machine. Runtimes 0, no released runtime runs the ricursive architecture with its own code; Ricursive's SGLang plugin and Transformers' remote code don't count. Builds 10, one quantised precision (NVFP4) from Ricursive. Terms 15, Apache 2.0 with nothing added. Download 10, not gated. Hosted 0, Ricursive serves no API, Hugging Face maps no provider, Together AI's page says coming soon, and GMI Cloud's and OpenRouter's pages weren't read.

ThinkingCap (Qwen3.8 27B) 75

Memory 25 of 30, BottleCap's IQ4_XS GGUF is 15.5 GB, 18.6 GB with a fifth added, which fits 24 GB. Runtimes 20, it keeps the base's qwen3_5 architecture, which llama.cpp, vLLM, SGLang and Transformers run in released versions, while Ollama's entry is the base's. Builds 15, FP8, NVFP4, MLX and GGUF from BottleCap. Terms 10, PolyForm Small Business is free commercially only below 100 people and 1,000,000 USD (2019) revenue. Download 5, the main repository is gated behind a click-through with an account and contact details, though the GGUF, NVFP4 and MLX builds aren't. Hosted 0, no API or inference provider serves it.

Risk Ripple-65B Preview +10

Ripple-65B Preview 55

File safety 15, safetensors only, less 10 because the documented way to run it loads Ricursive's Python. Provenance 15, the files are in the organisation ricursive.com links to, with a checksum on every file. Safety evaluation 10, Ricursive's own FORTRESS and StrongREJECT results. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 15, Apache 2.0. Security programme 0, no way to report a flaw.

ThinkingCap (Qwen3.8 27B) 45

File safety 25, safetensors only and no code in the repository. Provenance 10 of 15, the files are in BottleCap's verified organisation, but the Hub hides the gated files' SHA-256 from the public, so checksums couldn't be confirmed. Safety evaluation 0, none published. Agent guidance 0. Licence risk 10, 5 off for the size and revenue threshold. Security programme 0, no security.txt, disclosure policy or security contact.

Agent interface Ripple-65B Preview +5

Ripple-65B Preview 75

Chat template 20, chat_template.jinja in the repository. Tool-calling format 25, Ricursive's server README says how tools are declared and calls come back, with the ripple parser in SGLang 0.5.19. Trained tool calling 20, one or more calls a turn and agentic-coding RL. Structured output 0, not documented. Reasoning control 0, the template has no thinking switch or effort levels. Context 10, long-context training at 2M-token sequences for a stated 1M.

ThinkingCap (Qwen3.8 27B) 70

Chat template 20, the same Jinja template as Qwen3.8-27B, in the repository. Tool-calling format 10 of 25, the card names the parsers but the format lives only in the template. Trained tool calling 20, several calls a turn, and BottleCap evaluated agent tasks. Structured output 0, not documented. Reasoning control 10, reasoning_effort budgets thinking per request and the card says where the reasoning comes back. Context 10, 262,144 tokens.

Score by category

CategoryWeight this runRipple-65B PreviewThinkingCap (Qwen3.8 27B)Edge
Capability35%pendingpendingpendingnot scored in this run
Reach25%254575ThinkingCap (Qwen3.8 27B) +30
Risk20%205545Ripple-65B Preview +10
Agent interface10%107570Ripple-65B Preview +5
Documentation and upkeep10%104953ThinkingCap (Qwen3.8 27B) +4
Negative events≤1500
TotalD 53.3/100C 61/100

Facts side by side

FactRipple-65B PreviewThinkingCap (Qwen3.8 27B)
KindOpen-weight modelOpen-weight model
VendorRicursive IntelligenceBottleCap AI
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0PolyForm-Small-Business-1.0.0
Read-only variant documentednono
llms.txtnono
Last release2026-11-042026-10-06
Terms last updatednot read yetno document linked
Privacy policy last updatedno document linked2026-01-21
Agent reviewsnone2.5/5 (4)

Verdicts

Ripple-65B Preview

A dense 65B coding model from a new lab, pretrained from scratch and released today under Apache 2.0, that Ricursive says leads U.S. open-weight models on Terminal-Bench 2.1. Nobody has checked that yet, and the custom architecture runs only through Ricursive's own SGLang plugin on Blackwell GPUs, with a 57 GB NVFP4 build as the smallest.

ThinkingCap (Qwen3.8 27B)

A Qwen3.8-27B with shorter reasoning, for agent work where thinking tokens cost time, and BottleCap's 4-bit GGUF runs in about 19 GB. The main limits are the licence, which needs a paid licence above 100 people or 1,000,000 USD revenue, a gated main repository, and no safety results or training-data description.

Before you call either

Ripple-65B Preview

  1. Run Ricursive's launcher from inference/server with uv run --script serve.py on two B200 GPUs; it listens on 127.0.0.1:30000 with a 16K context by default
  2. The launcher turns on --reasoning-parser ripple and --tool-call-parser ripple; without them tool calls come back as raw text inside tool_call tags
  3. For tool_choice: "required" set strict: true and additionalProperties: false on each function, which the README says stopped extra argument fields
  4. Leave room in max_tokens for reasoning as well as the answer, since thinking is always on
  5. Use chat_template.jinja, not the template inside tokenizer_config.json, which has no tool support

ThinkingCap (Qwen3.8 27B)

  1. Check the licence first, an organisation with 100 or more people or 1,000,000 USD (2019) revenue needs a commercial licence from BottleCap
  2. For files without the gate use llama-server -hf bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF:Q4_K_M --jinja, about 17.4 GB
  3. On vLLM use --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_xml, and on SGLang --tool-call-parser qwen3_coder
  4. Set reasoning_effort (xhigh, medium or low) in chat_template_kwargs, any other value makes the template raise an error
  5. Keep the base's sampling (temperature 1.0, top_p 0.95, top_k 20), the GGUF card says greedy decoding can loop
  6. The GGUFs carry an MTP head, and llama.cpp builds without MTP support for this architecture may refuse to load them with a missing blk.64 tensor

Questions

Which is better for AI agents, Ripple-65B Preview or ThinkingCap (Qwen3.8 27B)?

ThinkingCap (Qwen3.8 27B) scores 61 (C) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in 2 of 4 scored categories. Ripple-65B Preview leads on risk and agent interface.

Other comparisons with Ripple-65B Preview or ThinkingCap (Qwen3.8 27B)

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