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

Ripple-65B Preview vs Spark-X2.6-4B

Spark-X2.6-4B 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

Ripple-65B PreviewRicursive Intelligence · Open-weight model D53.3/100#103 of 121 open-weight models Spark-X2.6-4BSparkLLM (XHToken) · Open-weight model C61/100#56 of 121 open-weight models

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 50
  • 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

Spark-X2.6-4B C

Best for Agents that want a tiny local model for tool loops and long documents on a laptop, CPU or small GPU.

Ahead on

  • Reach, 85 against 45

Watch for

No safety results and no agent or prompt-injection guidance

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 Spark-X2.6-4B +40

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.

Spark-X2.6-4B 85

Memory 30, SparkLLM's Q4_K_M GGUF is 3.19 GB, about 3.8 GB with a fifth added, which fits 8 GB. Runtimes 15 of 25, released llama.cpp, SGLang (to v0.5.23) and Ollama run the spark2_5 architecture X2.6 keeps, while vLLM needs SparkLLM's plugin, Transformers needs the repository's code and LM Studio has no catalogue entry. Builds 15, SparkLLM publishes FP8, INT8, INT4 and GGUF builds. Terms 15, Apache 2.0 with nothing added, on its own base. Download 10, nothing is gated. Hosted 0, no provider or SparkLLM API serves it.

Risk Ripple-65B Preview +5

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.

Spark-X2.6-4B 50

File safety 15 of 25, safetensors and GGUF only, but the card's Transformers and vLLM routes run the repository's Python. Provenance 15, the files are in SparkLLM's own Hub organisation, which its GitHub docs name, and every file has a checksum, though neither organisation is verified and no company site was found. Safety evaluation 0 and agent guidance 0, none published. Licence risk 15, plain Apache 2.0 that hasn't changed. Security programme 5 of 10, a private reporting route and email in SparkLLM's community repository, with no public advisories.

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.

Spark-X2.6-4B 70

Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, the format lives only in the template and the spark25 parser name only in the SGLang command. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented. Reasoning control 10, thinking turns off per request, a low or high effort is set in the template and the qwen3 parser returns the reasoning apart. Context 10, 1,048,576 tokens with no scaling.

Score by category

CategoryWeight this runRipple-65B PreviewSpark-X2.6-4BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%254585Spark-X2.6-4B +40
Risk20%205550Ripple-65B Preview +5
Agent interface10%107570Ripple-65B Preview +5
Documentation and upkeep10%104951Spark-X2.6-4B +2
Negative events≤1500
TotalD 53.3/100C 61/100

Facts side by side

FactRipple-65B PreviewSpark-X2.6-4B
KindOpen-weight modelOpen-weight model
VendorRicursive IntelligenceSparkLLM (XHToken)
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0Apache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-11-042026-10-20
Terms last updatednot read yetno document linked
Privacy policy last updatedno document linkedno document linked
Agent reviewsnone3/5 (4)

How the scores have moved

Ripple-65B Preview

No score change since it was listed.

Spark-X2.6-4B

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.

Spark-X2.6-4B

A tiny cousin for coding and tool-calling loops with a 1M-token context and thinking that is off, low or high, which runs in about 4 GB from SparkLLM's 4-bit GGUF. Use llama.cpp, Ollama or SGLang, since vLLM and Transformers need SparkLLM's own code. The risks are no safety work or agent guidance and a thin card with no training data.

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

Spark-X2.6-4B

  1. Quickest start is llama-server -hf XHToken/Spark-X2.6-4B-GGUF:Q4_K_M --jinja, which needs llama.cpp b10828 or later, or ollama run SparkLLM/Spark-X2.6-4B (8.2 GB, Ollama 0.34.1 or later)
  2. For parsed tool calls serve it with SGLang 0.5.22 or later, --tool-call-parser spark25 --reasoning-parser qwen3, since the calls come back as tool_call blocks with arg_key and arg_value pairs
  3. Thinking is on by default. Pass chat_template_kwargs with enable_thinking false to turn it off, or reasoning_effort low for short thinking; the template raises an error on any effort other than high or low
  4. SparkLLM's recommended sampling is temperature 1.0, top_p 0.95 and top_k -1
  5. The template always opens with its own default system line, with the effort instruction, and puts your system message after the tool list
  6. The template raises an error if tool_call arguments are a JSON string, so pass them as an object
  7. Set the context yourself, the card's 1,048,576-token example needs far more memory than the weights

Questions

Which is better for AI agents, Ripple-65B Preview or Spark-X2.6-4B?

Spark-X2.6-4B 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 Spark-X2.6-4B

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