Head to head · Open-weight models you run yourself · October 2026 research run
Gemma 4 E4B IT vs Ripple-65B Preview
Gemma 4 E4B IT scores 63.2 (B) on agent readiness against Ripple-65B Preview's 53.3 (D), 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 3674 open weights comparisons
Which one, for what
Best for Agents that need a cheap, permissively licensed helper on 8 GB of memory, for tool calls, short audio or images, rather than hard reasoning
Ahead on
- Reach, 95 against 45
- Risk, 70 against 55
- Agent interface, 85 against 75
- Documentation and upkeep, 75 against 49
Watch for
No safety evaluation figures published
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.
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
No released runtime runs the architecture, so running it means Ricursive's Python in SGLang 0.5.19 or Transformers' remote code
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 Gemma 4 E4B IT +50
Gemma 4 E4B IT 95
Memory 30, Google's own 4-bit QAT GGUF with its projector is 6.15 GB, which with a fifth on top is 7.4 GB and fits in 16 GB or less. Runtimes 25, released support in Transformers, vLLM, SGLang, llama.cpp, Ollama and LM Studio, six of six. Builds 15, Google publishes a Q4_0 GGUF and a w4a16 build and two mobile builds, read as two kinds as for the 31B. Terms 15, plain Apache 2.0 with nothing added. Download 10, no account or gate. Hosted 0, the Gemini API serves only the two largest sizes and no inference provider lists this one.
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.
Documentation and upkeep Gemma 4 E4B IT +26
Gemma 4 E4B IT 75
Model card 20, all seven items are there. Training data 10, a general description by type with a January 2025 cut-off and the filtering, but no sources, mix or sizes. Last change 20, Google's JAX library fixed the Gemma 4 audio embedding that this size runs on 5 October 2026, 28 days before this check, while the Hugging Face files last changed on 20 July. The line drops to 15 after 4 November unless the files or reference code change. Responsiveness 10, Google replied to 12 of 26 questions and reports, the last on 2026-07-17, with the five opened from 21 September to 1 November unanswered. Version policy 10, earlier Gemma generations keep their own repositories and gemma.ai has a dated releases page. File health 5, the files agree with the card but reports about them wait without a reply. 20 + 10 + 20 + 10 + 10 + 5 = 75.
Ripple-65B Preview 49
Model card 14 of 20, no limitations and no knowledge cut-off. Training data 5, a general description with token counts and no cut-off. Last change 20, released today. Responsiveness 0, no discussions yet, read as none answered. Version policy 5, a first release, with the NVFP4 checkpoint replaced in place on release day under a commit note. File health 5, tokenizer_config.json carries a second, tool-less chat template and a 131,072-token limit against the card's 1M.
Risk Gemma 4 E4B IT +15
Gemma 4 E4B IT 70
File safety 25, safetensors and GGUF only, with nothing from the repository run on load. Provenance 15, the files sit in Google's own organisation and every file has a checksum. Safety evaluation 0, the card says testing showed minimal policy violations but gives no figures. Agent guidance 10, the card names the harm categories its safety training covers and asks for content-safety safeguards, and the tool guide says to validate calls, with no prompt-injection guidance. Licence risk 15, Apache 2.0 with no added policy, no threshold and a working link. Security programme 5, Google's security.txt and reward programme give a route to report a flaw, but no public Gemma advisories were found.
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.
Agent interface Gemma 4 E4B IT +10
Gemma 4 E4B IT 85
Chat template 20, a Jinja file in the repository. Tool-calling format 25, Google documents how tools are declared and how calls come back, and ships a response schema for Transformers' parser. Trained tool calling 20, several calls in one turn. Structured output 0, nothing documents how to ask the weights for JSON or a schema. Reasoning control 10, thinking is off by default, turned on per request, and returned in a thought channel. Context 10, 131,072 tokens native, which meets the 128K line.
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.
Score by category
| Category | Weight this run | Gemma 4 E4B IT | Ripple-65B Preview | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 95 | 45 | Gemma 4 E4B IT +50 |
| Risk | 20%20 | 70 | 55 | Gemma 4 E4B IT +15 |
| Agent interface | 10%10 | 85 | 75 | Gemma 4 E4B IT +10 |
| Documentation and upkeep | 10%10 | 75 | 49 | Gemma 4 E4B IT +26 |
| Negative events | ≤15 | 0 | 0 | |
| Total | B 63.2/100 | D 53.3/100 |
Facts side by side
| Fact | Gemma 4 E4B IT | Ripple-65B Preview |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Google DeepMind | Ricursive Intelligence |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-07-20 | 2026-11-04 |
| Terms last updated | no document linked | not read yet |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 3.5/5 (4) | none |
Verdicts
Gemma 4 E4B IT
A small cousin for light agent work, tool-calling loops, transcription and image reading on a phone or laptop, under Apache 2.0. Google's 4-bit build needs about 7 GB with working space and runs in all the common runtimes. Google's card puts it far below larger Gemma 4 sizes on agent and coding tasks, with no published safety results.
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.
Before you call either
Gemma 4 E4B IT
- On the reference machine run
llama-server -hf google/gemma-4-E4B-it-qat-q4_0-gguf --hf-file gemma-4-E4B_q4_0-it.gguf --jinja -c 131072, and add--mmprojwith gemma-4-E4B-it-mmproj.gguf for images and audio - With Ollama run
ollama run gemma4:e4band raise num_ctx or OLLAMA_CONTEXT_LENGTH, since the default context is small and agents want at least 64K - In vLLM or SGLang pass
--tool-call-parser gemma4 --reasoning-parser gemma4(vLLM also--enable-auto-tool-choice), or tool calls come back as text; Google's w4a16 build is the 4-bit route there - Thinking is off by default; pass enable_thinking=True or start the system prompt with <|think|>, and drop earlier turns' thinking from history except on tool-call turns
- Put images before the text and audio after it, keep audio to 30 seconds and video to 60 frames, and validate tool names and arguments before running them
Ripple-65B Preview
- Run Ricursive's launcher from inference/server with
uv run --script serve.pyon two B200 GPUs; it listens on 127.0.0.1:30000 with a 16K context by default - The launcher turns on
--reasoning-parser rippleand--tool-call-parser ripple; without them tool calls come back as raw text inside tool_call tags - For
tool_choice: "required"setstrict: trueandadditionalProperties: falseon each function, which the README says stopped extra argument fields - Leave room in
max_tokensfor reasoning as well as the answer, since thinking is always on - Use chat_template.jinja, not the template inside tokenizer_config.json, which has no tool support
Questions
Which is better for AI agents, Gemma 4 E4B IT or Ripple-65B Preview?
Gemma 4 E4B IT scores 63.2 (B) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in every scored category.
Other comparisons with Gemma 4 E4B IT or Ripple-65B Preview
- Agents-A1-4B vs Gemma 4 E4B IT
- Agents-A1-4B vs Ripple-65B Preview
- Apertus v1.5 8B vs Gemma 4 E4B IT
- Apertus v1.5 8B vs Ripple-65B Preview
- Apodex-1.1-mini vs Gemma 4 E4B IT
- Apodex-1.1-mini vs Ripple-65B Preview
- BigBang-v1 vs Gemma 4 E4B IT
- BigBang-v1 vs Ripple-65B Preview
- Carbon-X vs Gemma 4 E4B IT
- Carbon-X vs Ripple-65B Preview
- Cosmos-Reason2-2B vs Gemma 4 E4B IT
- Cosmos-Reason2-2B vs Ripple-65B Preview
- Darwin-36B-Opus vs Gemma 4 E4B IT
- Darwin-36B-Opus vs Ripple-65B Preview
- DeepSeek-V3.2 vs Gemma 4 E4B IT
- DeepSeek-V3.2 vs Ripple-65B Preview
- DeepSeek V4.1 Flash vs Gemma 4 E4B IT
- DeepSeek V4.1 Flash vs Ripple-65B Preview
- DeepSeek-V4-Flash-0731 vs Gemma 4 E4B IT
- DeepSeek-V4-Flash-0731 vs Ripple-65B Preview
- DiffusionGemma 26B A4B IT vs Gemma 4 E4B IT
- DiffusionGemma 26B A4B IT vs Ripple-65B Preview
- Gemma 4 12B IT vs Gemma 4 E4B IT
- Gemma 4 12B IT vs Ripple-65B Preview
- Gemma 4 26B A4B IT vs Gemma 4 E4B IT
- Gemma 4 26B A4B IT vs Ripple-65B Preview
- Gemma 4 31B IT vs Gemma 4 E4B IT
- Gemma 4 31B IT vs Ripple-65B Preview
- Gemma 4 E2B IT vs Gemma 4 E4B IT
- Gemma 4 E2B IT vs Ripple-65B Preview
- Gemma 4 E4B IT vs GLM-4.5-Air
- Gemma 4 E4B IT vs GLM-4.7-Flash
- Gemma 4 E4B IT vs GLM-5.3
- Gemma 4 E4B IT vs GLM-5.3-Flash
- Gemma 4 E4B IT vs gpt-oss-120b
- Gemma 4 E4B IT vs gpt-oss-20b
- Gemma 4 E4B IT vs Granite 4.2 3B
- Gemma 4 E4B IT vs Hy3
- Gemma 4 E4B IT vs Inkling
- Gemma 4 E4B IT vs Inkling-Small
- Gemma 4 E4B IT vs InternVL3.5-GPT-OSS-20B-A4B-Preview
- Gemma 4 E4B IT vs Kimi K3
- Gemma 4 E4B IT vs Kolibri-1
- Gemma 4 E4B IT vs Kronos
- Gemma 4 E4B IT vs Laguna M.1
- Gemma 4 E4B IT vs Laguna S 2.1
- Gemma 4 E4B IT vs Laguna XS 2.1
- Gemma 4 E4B IT vs LBS-27B
- Gemma 4 E4B IT vs LFM2.5-2.6B
- Gemma 4 E4B IT vs Ling-3.0-tiny
- Gemma 4 E4B IT vs MiMo-V2.6 Distill Qwen 9B
- Gemma 4 E4B IT vs MiniCPM-o 4.5
- Gemma 4 E4B IT vs MiniCPM5-2B
- Gemma 4 E4B IT vs MiniMax-M2.5
- Gemma 4 E4B IT vs MiniMax-M2.7
- Gemma 4 E4B IT vs MiniMax-M3.1-Flash
- Gemma 4 E4B IT vs Mistral Large 4
- Gemma 4 E4B IT vs Muse Glimmer 30B
- Gemma 4 E4B IT vs Nanbeige4.3-33B
- Gemma 4 E4B IT vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- Gemma 4 E4B IT vs Nemotron 3.5 Lightning 30B A3B
- Gemma 4 E4B IT vs Nemotron 3 Nano 30B A3B
- Gemma 4 E4B IT vs Nemotron 3 Nano 4B
- Gemma 4 E4B IT vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Gemma 4 E4B IT vs Nemotron 3 Super 120B A12B
- Gemma 4 E4B IT vs Nemotron 3 Ultra 550B A55B
- Gemma 4 E4B IT vs occamy-1.0
- Gemma 4 E4B IT vs Olmo 3 7B Think
- Gemma 4 E4B IT vs Ornith-1.5-35B-A3B
- Gemma 4 E4B IT vs Ornith-1.5-397B
- Gemma 4 E4B IT vs Ornith-1.5-9B
- Gemma 4 E4B IT vs Qwen3-30B-A3B-Instruct-2507
- Gemma 4 E4B IT vs Qwen3-4B-Instruct-2507
- Gemma 4 E4B IT vs Qwen3.5-0.8B
- Gemma 4 E4B IT vs Qwen3.5-122B-A10B
- Gemma 4 E4B IT vs Qwen3.5-2B
- Gemma 4 E4B IT vs Qwen3.5-397B-A17B
- Gemma 4 E4B IT vs Qwen3.5-4B
- Gemma 4 E4B IT vs Qwen3.5-9B
- Gemma 4 E4B IT vs Qwen3.6-27B
- Gemma 4 E4B IT vs Qwen3.6-35B-A3B
- Gemma 4 E4B IT vs Qwen3.8-27B
- Gemma 4 E4B IT vs Qwen3.8-Flash-Next
- Gemma 4 E4B IT vs Qwen3-Coder-30B-A3B-Instruct
- Gemma 4 E4B IT vs Qwen3-Coder-Next
- Gemma 4 E4B IT vs Qwen3-Omni-30B-A3B-Instruct
- Gemma 4 E4B IT vs Qwen3-VL-2B-Instruct
- Gemma 4 E4B IT vs Qwen3-VL-30B-A3B-Instruct
- Gemma 4 E4B IT vs Qwen3-VL-32B-Instruct
- Gemma 4 E4B IT vs Qwen3-VL-4B-Instruct
- Gemma 4 E4B IT vs Qwen3-VL-8B-Instruct
- Gemma 4 E4B IT vs Qwopus3.8-27B-Flash-V2
- Gemma 4 E4B IT vs Recursive Flint
- Gemma 4 E4B IT vs SmolLM3-3B
- Gemma 4 E4B IT vs Spark-X2.6-4B
- Gemma 4 E4B IT vs Swift 1.5 Qwen3.8-27B
- Gemma 4 E4B IT vs Swift 1.5 Qwen3.8-Flash-Next
- Gemma 4 E4B IT vs ThinkingCap (Qwen3.8 27B)
- Gemma 4 E4B IT vs Baidu Unlimited-OCR-2
- GLM-4.5-Air vs Ripple-65B Preview
- GLM-4.7-Flash vs Ripple-65B Preview
- GLM-5.3-Flash vs Ripple-65B Preview
- GLM-5.3 vs Ripple-65B Preview
- gpt-oss-120b vs Ripple-65B Preview
- gpt-oss-20b vs Ripple-65B Preview
- Granite 4.2 3B vs Ripple-65B Preview
- Hy3 vs Ripple-65B Preview
- Inkling-Small vs Ripple-65B Preview
- Inkling vs Ripple-65B Preview
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Ripple-65B Preview
- Kimi K3 vs Ripple-65B Preview
- Kolibri-1 vs Ripple-65B Preview
- Kronos vs Ripple-65B Preview
- Laguna M.1 vs Ripple-65B Preview
- Laguna S 2.1 vs Ripple-65B Preview
- Laguna XS 2.1 vs Ripple-65B Preview
- LBS-27B vs Ripple-65B Preview
- LFM2.5-2.6B vs Ripple-65B Preview
- Ling-3.0-tiny vs Ripple-65B Preview
- MiMo-V2.6 Distill Qwen 9B vs Ripple-65B Preview
- MiniCPM-o 4.5 vs Ripple-65B Preview
- MiniCPM5-2B vs Ripple-65B Preview
- MiniMax-M2.5 vs Ripple-65B Preview
- MiniMax-M2.7 vs Ripple-65B Preview
- MiniMax-M3.1-Flash vs Ripple-65B Preview
- Mistral Large 4 vs Ripple-65B Preview
- Muse Glimmer 30B vs Ripple-65B Preview
- Nanbeige4.3-33B vs Ripple-65B Preview
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Ripple-65B Preview
- Nemotron 3.5 Lightning 30B A3B vs Ripple-65B Preview
- Nemotron 3 Nano 30B A3B vs Ripple-65B Preview
- Nemotron 3 Nano 4B vs Ripple-65B Preview
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Ripple-65B Preview
- Nemotron 3 Super 120B A12B vs Ripple-65B Preview
- Nemotron 3 Ultra 550B A55B vs Ripple-65B Preview
- occamy-1.0 vs Ripple-65B Preview
- Olmo 3 7B Think vs Ripple-65B Preview
- Ornith-1.5-35B-A3B vs Ripple-65B Preview
- Ornith-1.5-397B vs Ripple-65B Preview
- Ornith-1.5-9B vs Ripple-65B Preview
- Qwen3-30B-A3B-Instruct-2507 vs Ripple-65B Preview
- Qwen3-4B-Instruct-2507 vs Ripple-65B Preview
- Qwen3.5-0.8B vs Ripple-65B Preview
- Qwen3.5-122B-A10B vs Ripple-65B Preview
- Qwen3.5-2B vs Ripple-65B Preview
- Qwen3.5-397B-A17B vs Ripple-65B Preview
- Qwen3.5-4B vs Ripple-65B Preview
- Qwen3.5-9B vs Ripple-65B Preview
- Qwen3.6-27B vs Ripple-65B Preview
- Qwen3.6-35B-A3B vs Ripple-65B Preview
- Qwen3.8-27B vs Ripple-65B Preview
- Qwen3.8-Flash-Next vs Ripple-65B Preview
- Qwen3-Coder-30B-A3B-Instruct vs Ripple-65B Preview
- Qwen3-Coder-Next vs Ripple-65B Preview
- Qwen3-Omni-30B-A3B-Instruct vs Ripple-65B Preview
- Qwen3-VL-2B-Instruct vs Ripple-65B Preview
- Qwen3-VL-30B-A3B-Instruct vs Ripple-65B Preview
- Qwen3-VL-32B-Instruct vs Ripple-65B Preview
- Qwen3-VL-4B-Instruct vs Ripple-65B Preview
- Qwen3-VL-8B-Instruct vs Ripple-65B Preview
- Qwopus3.8-27B-Flash-V2 vs Ripple-65B Preview
- Recursive Flint vs Ripple-65B Preview
- Ripple-65B Preview vs SmolLM3-3B
- Ripple-65B Preview vs Spark-X2.6-4B
- Ripple-65B Preview vs Swift 1.5 Qwen3.8-27B
- Ripple-65B Preview vs Swift 1.5 Qwen3.8-Flash-Next
- Ripple-65B Preview vs ThinkingCap (Qwen3.8 27B)
- Ripple-65B Preview vs Baidu Unlimited-OCR-2
Machine-readable
- This page as Markdown
/compare/gemma-4-e4b-it-vs-ripple-65b-preview.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/gemma-4-e4b-it.json·/api/v1/tools/ripple-65b-preview.json - From a terminal
anchor compare gemma-4-e4b-it ripple-65b-preview(the CLI) - Over MCP
compare_tools {"a": "gemma-4-e4b-it", "b": "ripple-65b-preview"}at/mcp, no key