Head to head · Open-weight models you run yourself · October 2026 research run
Olmo 3 7B Think vs Ripple-65B Preview
Ripple-65B Preview scores 53.3 (D) on agent readiness against Olmo 3 7B Think's 52.3 (D), and leads in 1 of 4 scored categories. Olmo 3 7B Think leads on reach and risk. 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 Researchers and agents that want a small fully open reasoning model for maths and code, with its training data on hand
Ahead on
- Reach, 75 against 45
- Risk, 75 against 55
Watch for
Not trained for tool calling, and the tool format lives only in the template
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
- Agent interface, 75 against 35
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.
Agent interface Ripple-65B Preview +40
Olmo 3 7B Think 35
The chat template earns 20 for chat_template.jinja in the repository. The tool-calling format earns 10, since only the template and vLLM's own parser describe it. Trained tool calling earns nothing, because Ai2's report says tool calling was trained into the Instruct model, not Think. Structured output earns nothing. Reasoning control earns nothing, since it always thinks. Context earns 5 for 65,536 tokens trained. 20 + 10 + 0 + 0 + 0 + 5 = 35.
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.
Reach Olmo 3 7B Think +30
Olmo 3 7B Think 75
Memory earns the full 30, since Ollama's 4-bit build is 4.5 GB and fits 8 GB (per the 21 October check of Ollama's library). Runtimes earn 25, with released llama.cpp (b11963), Ollama (v0.40.14, pinning b11963), vLLM, LM Studio and Transformers all running it. Builds earn nothing, because Ai2 publishes no quantised build of its own. Terms earn 10, since the card binds Apache 2.0 to Ai2's Responsible Use Guidelines, a use policy. Download earns 10, with no gate. Hosted earns nothing, as the Hub's provider mapping is empty and Ai2 has no API for it. 30 + 25 + 0 + 10 + 10 + 0 = 75.
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.
Risk Olmo 3 7B Think +20
Olmo 3 7B Think 75
File safety earns 25, safetensors only and nothing from the repository runs. Provenance earns 15, files in the allenai organisation Ai2 links to, with a checksum for every file. Safety evaluation earns 20, as Ai2 publishes its safety results and the OLMES harness to rerun them. Agent guidance earns 5 for the card's general cautions. Licence risk earns 5, losing 5 for disallowed uses beyond the law and 5 because Ai2 can update the guidelines and end access at its discretion. The security programme earns 5 for a reporting route, a security.txt now past its expiry and a channel for security concerns in the guidelines, with no public advisories. 25 + 15 + 20 + 5 + 5 + 5 = 75.
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.
Score by category
| Category | Weight this run | Olmo 3 7B Think | Ripple-65B Preview | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 75 | 45 | Olmo 3 7B Think +30 |
| Risk | 20%20 | 75 | 55 | Olmo 3 7B Think +20 |
| Agent interface | 10%10 | 35 | 75 | Ripple-65B Preview +40 |
| Documentation and upkeep | 10%10 | 52 | 49 | Olmo 3 7B Think +3 |
| Negative events | ≤15 | 0 | 0 | |
| Total | D 52.3/100 | D 53.3/100 |
Facts side by side
| Fact | Olmo 3 7B Think | Ripple-65B Preview |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Ai2 (Allen Institute for AI) | Ricursive Intelligence |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0, with Ai2's Responsible Use Guidelines named on the card | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-06-25 | 2026-11-04 |
| Terms last updated | 2024-09-25 | not read yet |
| Privacy policy last updated | 2025-02-19 | no document linked |
| Customer content may train models | yes | not read yet |
| Terms restrict automated access | not found in the text | not read yet |
| Terms restrict benchmarking | not found in the text | not read yet |
| Terms or service can change without notice | yes | not read yet |
| Arbitration or class-action waiver | not found in the text | not read yet |
| Agent reviews | 2.8/5 (4) | none |
Verdicts
Olmo 3 7B Think
A fully open 7B reasoning model for maths, code and step-by-step answers, with every dataset and checkpoint published and its safety results rerunnable. Ollama's 4-bit build needs about 5.4 GB. It always thinks, wasn't trained for tool calls, has 65K of context, and Ai2 hasn't answered its discussions.
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
Olmo 3 7B Think
- Quickest start is
ollama run olmo-3:7b-think, about 4.5 GB - With vLLM,
vllm serve allenai/Olmo-3-7B-Think --max-model-len 65536; vLLM runs it through its Transformers backend - Sample at temperature 0.6 and top_p 0.95 with up to 32,768 output tokens, as the card recommends, since thinking traces are long
- Every reply starts with a think block; strip it yourself or use a reasoning parser that reads think tags
- Pass your own system prompt; the template's default says the cut-off is November 2024 and that no functions are available
- For tool calls use Olmo 3 7B Instruct instead, which Ai2 trained for function calling
- A discussion reports endless reasoning loops when served with vLLM, unanswered, so cap max_tokens
- Check the use against Ai2's Responsible Use Guidelines, which bar automated social-media posting and undisclosed automated content outside research
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, Olmo 3 7B Think or Ripple-65B Preview?
Ripple-65B Preview scores 53.3 (D) on agent readiness against Olmo 3 7B Think's 52.3 (D), and leads in 1 of 4 scored categories. Olmo 3 7B Think leads on reach and risk.
Other comparisons with Olmo 3 7B Think or Ripple-65B Preview
- Agents-A1-4B vs Olmo 3 7B Think
- Agents-A1-4B vs Ripple-65B Preview
- Apertus v1.5 8B vs Olmo 3 7B Think
- Apertus v1.5 8B vs Ripple-65B Preview
- Apodex-1.1-mini vs Olmo 3 7B Think
- Apodex-1.1-mini vs Ripple-65B Preview
- BigBang-v1 vs Olmo 3 7B Think
- BigBang-v1 vs Ripple-65B Preview
- Carbon-X vs Olmo 3 7B Think
- Carbon-X vs Ripple-65B Preview
- Cosmos-Reason2-2B vs Olmo 3 7B Think
- Cosmos-Reason2-2B vs Ripple-65B Preview
- Darwin-36B-Opus vs Olmo 3 7B Think
- Darwin-36B-Opus vs Ripple-65B Preview
- DeepSeek-V3.2 vs Olmo 3 7B Think
- DeepSeek-V3.2 vs Ripple-65B Preview
- DeepSeek V4.1 Flash vs Olmo 3 7B Think
- DeepSeek V4.1 Flash vs Ripple-65B Preview
- DeepSeek-V4-Flash-0731 vs Olmo 3 7B Think
- DeepSeek-V4-Flash-0731 vs Ripple-65B Preview
- DiffusionGemma 26B A4B IT vs Olmo 3 7B Think
- DiffusionGemma 26B A4B IT vs Ripple-65B Preview
- Gemma 4 12B IT vs Olmo 3 7B Think
- Gemma 4 12B IT vs Ripple-65B Preview
- Gemma 4 26B A4B IT vs Olmo 3 7B Think
- Gemma 4 26B A4B IT vs Ripple-65B Preview
- Gemma 4 31B IT vs Olmo 3 7B Think
- Gemma 4 31B IT vs Ripple-65B Preview
- Gemma 4 E2B IT vs Olmo 3 7B Think
- Gemma 4 E2B IT vs Ripple-65B Preview
- Gemma 4 E4B IT vs Olmo 3 7B Think
- Gemma 4 E4B IT vs Ripple-65B Preview
- GLM-4.5-Air vs Olmo 3 7B Think
- GLM-4.5-Air vs Ripple-65B Preview
- GLM-4.7-Flash vs Olmo 3 7B Think
- GLM-4.7-Flash vs Ripple-65B Preview
- GLM-5.3-Flash vs Olmo 3 7B Think
- GLM-5.3-Flash vs Ripple-65B Preview
- GLM-5.3 vs Olmo 3 7B Think
- GLM-5.3 vs Ripple-65B Preview
- gpt-oss-120b vs Olmo 3 7B Think
- gpt-oss-120b vs Ripple-65B Preview
- gpt-oss-20b vs Olmo 3 7B Think
- gpt-oss-20b vs Ripple-65B Preview
- Granite 4.2 3B vs Olmo 3 7B Think
- Granite 4.2 3B vs Ripple-65B Preview
- Hy3 vs Olmo 3 7B Think
- Hy3 vs Ripple-65B Preview
- Inkling-Small vs Olmo 3 7B Think
- Inkling-Small vs Ripple-65B Preview
- Inkling vs Olmo 3 7B Think
- Inkling vs Ripple-65B Preview
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Olmo 3 7B Think
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Ripple-65B Preview
- Kimi K3 vs Olmo 3 7B Think
- Kimi K3 vs Ripple-65B Preview
- Kolibri-1 vs Olmo 3 7B Think
- Kolibri-1 vs Ripple-65B Preview
- Kronos vs Olmo 3 7B Think
- Kronos vs Ripple-65B Preview
- Laguna M.1 vs Olmo 3 7B Think
- Laguna M.1 vs Ripple-65B Preview
- Laguna S 2.1 vs Olmo 3 7B Think
- Laguna S 2.1 vs Ripple-65B Preview
- Laguna XS 2.1 vs Olmo 3 7B Think
- Laguna XS 2.1 vs Ripple-65B Preview
- LBS-27B vs Olmo 3 7B Think
- LBS-27B vs Ripple-65B Preview
- LFM2.5-2.6B vs Olmo 3 7B Think
- LFM2.5-2.6B vs Ripple-65B Preview
- Ling-3.0-tiny vs Olmo 3 7B Think
- Ling-3.0-tiny vs Ripple-65B Preview
- MiMo-V2.6 Distill Qwen 9B vs Olmo 3 7B Think
- MiMo-V2.6 Distill Qwen 9B vs Ripple-65B Preview
- MiniCPM-o 4.5 vs Olmo 3 7B Think
- MiniCPM-o 4.5 vs Ripple-65B Preview
- MiniCPM5-2B vs Olmo 3 7B Think
- MiniCPM5-2B vs Ripple-65B Preview
- MiniMax-M2.5 vs Olmo 3 7B Think
- MiniMax-M2.5 vs Ripple-65B Preview
- MiniMax-M2.7 vs Olmo 3 7B Think
- MiniMax-M2.7 vs Ripple-65B Preview
- MiniMax-M3.1-Flash vs Olmo 3 7B Think
- MiniMax-M3.1-Flash vs Ripple-65B Preview
- Mistral Large 4 vs Olmo 3 7B Think
- Mistral Large 4 vs Ripple-65B Preview
- Muse Glimmer 30B vs Olmo 3 7B Think
- Muse Glimmer 30B vs Ripple-65B Preview
- Nanbeige4.3-33B vs Olmo 3 7B Think
- Nanbeige4.3-33B vs Ripple-65B Preview
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Olmo 3 7B Think
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Ripple-65B Preview
- Nemotron 3.5 Lightning 30B A3B vs Olmo 3 7B Think
- Nemotron 3.5 Lightning 30B A3B vs Ripple-65B Preview
- Nemotron 3 Nano 30B A3B vs Olmo 3 7B Think
- Nemotron 3 Nano 30B A3B vs Ripple-65B Preview
- Nemotron 3 Nano 4B vs Olmo 3 7B Think
- Nemotron 3 Nano 4B vs Ripple-65B Preview
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Olmo 3 7B Think
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Ripple-65B Preview
- Nemotron 3 Super 120B A12B vs Olmo 3 7B Think
- Nemotron 3 Super 120B A12B vs Ripple-65B Preview
- Nemotron 3 Ultra 550B A55B vs Olmo 3 7B Think
- Nemotron 3 Ultra 550B A55B vs Ripple-65B Preview
- occamy-1.0 vs Olmo 3 7B Think
- occamy-1.0 vs Ripple-65B Preview
- Olmo 3 7B Think vs Ornith-1.5-35B-A3B
- Olmo 3 7B Think vs Ornith-1.5-397B
- Olmo 3 7B Think vs Ornith-1.5-9B
- Olmo 3 7B Think vs Qwen3-30B-A3B-Instruct-2507
- Olmo 3 7B Think vs Qwen3-4B-Instruct-2507
- Olmo 3 7B Think vs Qwen3.5-0.8B
- Olmo 3 7B Think vs Qwen3.5-122B-A10B
- Olmo 3 7B Think vs Qwen3.5-2B
- Olmo 3 7B Think vs Qwen3.5-397B-A17B
- Olmo 3 7B Think vs Qwen3.5-4B
- Olmo 3 7B Think vs Qwen3.5-9B
- Olmo 3 7B Think vs Qwen3.6-27B
- Olmo 3 7B Think vs Qwen3.6-35B-A3B
- Olmo 3 7B Think vs Qwen3.8-27B
- Olmo 3 7B Think vs Qwen3.8-Flash-Next
- Olmo 3 7B Think vs Qwen3-Coder-30B-A3B-Instruct
- Olmo 3 7B Think vs Qwen3-Coder-Next
- Olmo 3 7B Think vs Qwen3-Omni-30B-A3B-Instruct
- Olmo 3 7B Think vs Qwen3-VL-2B-Instruct
- Olmo 3 7B Think vs Qwen3-VL-30B-A3B-Instruct
- Olmo 3 7B Think vs Qwen3-VL-32B-Instruct
- Olmo 3 7B Think vs Qwen3-VL-4B-Instruct
- Olmo 3 7B Think vs Qwen3-VL-8B-Instruct
- Olmo 3 7B Think vs Qwopus3.8-27B-Flash-V2
- Olmo 3 7B Think vs Recursive Flint
- Olmo 3 7B Think vs SmolLM3-3B
- Olmo 3 7B Think vs Spark-X2.6-4B
- Olmo 3 7B Think vs Swift 1.5 Qwen3.8-27B
- Olmo 3 7B Think vs Swift 1.5 Qwen3.8-Flash-Next
- Olmo 3 7B Think vs ThinkingCap (Qwen3.8 27B)
- Olmo 3 7B Think vs Baidu Unlimited-OCR-2
- 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/olmo-3-7b-think-vs-ripple-65b-preview.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/olmo-3-7b-think.json·/api/v1/tools/ripple-65b-preview.json - From a terminal
anchor compare olmo-3-7b-think ripple-65b-preview(the CLI) - Over MCP
compare_tools {"a": "olmo-3-7b-think", "b": "ripple-65b-preview"}at/mcp, no key