Head to head · Open-weight models you run yourself · October 2026 research run
Cola2 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) on agent readiness against Cola2's 33.9 (F), 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 6359 open weights comparisons
Which one, for what
Cola2 F
Best for Researchers working on latent or diffusion language models who want a checkpoint to study
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
No licence stated for the weights
NVIDIA Nemotron 3.5 Bolt E4B 4GB C
Best for Agents and apps that need a fast local conversational model with tool calls on an 8 to 12 GB GPU, a Mac or a Jetson, such as voice assistants and game characters.
Ahead on
- Reach, 90 against 25
- Risk, 80 against 40
- Agent interface, 85 against 20
- Documentation and upkeep, 65 against 58
Watch for
64K tokens of context against the base's 128K, and Ollama's and LM Studio's pages show 128K anyway
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 NVIDIA Nemotron 3.5 Bolt E4B 4GB +65
Cola2 25
Memory 15, the only build is the F32 safetensors set, 35.07 GB, and 35.07 GB × 1.2 = 42.1 GB, within 64 GB. Loaded as BF16, as the card's example does, it needs about half, but the line reads the file size. Runtimes 0, none of the six runs it in a released version. Builds 0, one set of weights and no quantised build. Terms 0, no licence for the weights. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and nothing names an API that serves it. 15 + 0 + 0 + 0 + 10 + 0 = 25.
NVIDIA Nemotron 3.5 Bolt E4B 4GB 90
Memory earns 30, since NVIDIA's Q4_K_M GGUF is 6.18 GB, 7.4 GB with a fifth added. Runtimes earn the full 25 from released llama.cpp, vLLM, Transformers, SGLang, Ollama and LM Studio. Builds earn 10, as NVIDIA's only quantised build is the Q4_K_M GGUF and the DFlash repositories are drafters. Terms earn 10, because OpenMDW-1.1 is permissive with no threshold or use policy but isn't an OSI licence, and the Gemma 4 base adds plain Apache 2.0. Download earns 10, with no gate. Hosted earns 5, as NVIDIA's own API serves it.
Agent interface NVIDIA Nemotron 3.5 Bolt E4B 4GB +65
Cola2 20
Chat template 20, A chat template in tokenizer_config.json (4,116 characters), which came with the Qwen3 tokenizer. Tool-calling format 0, none. Trained tool calling 0, not trained for tool calling. Structured output 0, Nothing about JSON or schema-constrained output. Reasoning control 0, no reasoning mode. Context 0, no context length is stated. 20 + 0 + 0 + 0 + 0 + 0 = 20.
NVIDIA Nemotron 3.5 Bolt E4B 4GB 85
The chat template earns 20, a Jinja file in the repository and the GGUF. The tool-calling format earns 25, with OpenAI-style tools, the gemma4 parser named and an asynchronous pattern documented. Trained tool calling earns 20, multi-step tool-use training and several calls per turn. Structured output earns 5, trained but with no instructions. Reasoning control earns 10, thinking per request, the reasoning returned apart and a budget. Context earns 5 for 64K, half the base's 128K.
Risk NVIDIA Nemotron 3.5 Bolt E4B 4GB +40
Cola2 40
File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the ByteDance-Seed organisation, which the ByteDance-Seed GitHub repository for the model links to (10). Safety evaluation 0, no safety results. Agent guidance 5, General cautions in the GitHub model card: not instruction-tuned or safety-aligned, not for use as an assistant, for facts or advice, or for high-impact decisions about people. Licence risk 0, With no licence for the weights there is nothing to hold ByteDance to. Security programme 5, the GitHub repository's SECURITY.md sends reports to ByteDance's security centre and says how they're assessed (5). 15 + 15 + 0 + 5 + 0 + 5 = 40.
NVIDIA Nemotron 3.5 Bolt E4B 4GB 80
File safety earns 25, safetensors and GGUF only, and the documented routes run no repository code (an undocumented Inference Endpoints handler.py sits in the BF16 repository). Provenance earns 15, NVIDIA's linked organisation with a hash for every file. Safety evaluation earns 0, since NVIDIA names its content-safety and bias datasets but publishes no results. Agent guidance earns 15, for instruction-hierarchy training NVIDIA documents and a subcard on prompt injection and the safeguards to add. Licence risk keeps all 15 under OpenMDW-1.1 and Apache 2.0. The security programme earns 10 for the card's reporting route and NVIDIA's public bulletins.
Documentation and upkeep NVIDIA Nemotron 3.5 Bolt E4B 4GB +7
Cola2 58
Model card 8, Intended use and limitations (the out-of-scope list, and the unconverged latent model) and the architecture are on the card and the GitHub model card it links. Training data 15, the GitHub model card names the source (OLMo 2 pretraining text, retokenised) and the mix by stage with token counts, 346.03B in all. Last change 20, All six commits are from 6 November 2026, four days before this check, the last one updating a checksum in the README. Responsiveness 0, no discussions on the repository, so nobody has asked anything yet and the line reads as none answered, as settled in calibration. Version policy 5, A first release. File health 10, Config, files and card agree: F32 weights, a float32 config and a card that says to load them as bfloat16, with SHA256SUMS to check the download. 8 + 15 + 20 + 0 + 5 + 10 = 58.
NVIDIA Nemotron 3.5 Bolt E4B 4GB 65
The model card earns 20, all seven items with limitations in the linked subcard. Training data earns 15, the RL datasets published and a cut-off given, but the card's data section describes a larger SFT corpus the report says wasn't used, and the base's pre-training is described only in general. Last change earns 20 for 23 October 2026. Responsiveness earns 0, as NVIDIA hasn't answered either of the two discussions. Version policy earns 5 as a first release whose GGUF was replaced on release day with only a commit title. File health earns 5, since the card and the template disagree on the thinking default and both cards carry leftover lines.
Score by category
| Category | Weight this run | Cola2 | NVIDIA Nemotron 3.5 Bolt E4B 4GB | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 25 | 90 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +65 |
| Risk | 20%20 | 40 | 80 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +40 |
| Agent interface | 10%10 | 20 | 85 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +65 |
| Documentation and upkeep | 10%10 | 58 | 65 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +7 |
| Negative events | ≤15 | 0 | 0 | |
| Total | F 33.9/100 | C 61/100 |
Facts side by side
| Fact | Cola2 | NVIDIA Nemotron 3.5 Bolt E4B 4GB |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | ByteDance (Seed) | NVIDIA |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | None stated for the weights (the code is Apache 2.0) | OpenMDW-1.1 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-11-06 | 2026-10-23 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 4/5 (4) |
Verdicts
Cola2
A research release, and an honest one: ByteDance Seed says it's a base model with an unconverged latent stage, scoring below Llama-3-8B on its own table. It's here for people studying continuous-latent generation. Agents can't use it as a model: 32-token completions, no chat or tools, the repository's own code to load it, and no licence on the weights.
NVIDIA Nemotron 3.5 Bolt E4B 4GB
A small cousin for voice and device assistants, game characters and robot interfaces, trained for multi-turn tool calls, instruction hierarchy and short spoken replies, with thinking switchable per request. NVIDIA's Q4_K_M GGUF needs about 8 GB, and Ollama and LM Studio carry it. Context is 64K, half the Gemma 4 base, and NVIDIA publishes no safety results.
Before you call either
Cola2
- Don't plan to use this as your model. It completes short prompts and wasn't tuned to follow instructions
- Don't use it commercially on the strength of this listing. The weights have no stated licence
- If you study it, read modeling_cola2.py first: trust_remote_code runs it
- Pin Transformers below 5, as the card requires
- Pass a seeded torch.Generator to generate(). Output varies between runs even with greedy decoding
- Check the download with
sha256sum -c SHA256SUMS
NVIDIA Nemotron 3.5 Bolt E4B 4GB
- Quickest start is
ollama run nemotron-3.5-bolt:e4b, about 8 GB of files, and setnum_ctxto 65536 or less, since NVIDIA supports 64K - With NVIDIA's GGUF use
llama-server -m Nemotron-3.5-Bolt-E4B-Q4_K_M.gguf -c 65536 -ngl 999 --jinja, and add--reasoning on --reasoning-budget <tokens>to cap thinking - With vLLM 0.31.0 or later pass
--enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4and the repository's chat_template.jinja - Pass
enable_thinkingexplicitly inchat_template_kwargs, since the template defaults to off whatever the card says, and setskip_special_tokensfalse as the card's examples do - Use
temperature=1.0andtop_p=0.95in both modes, as the card says - Set
conversational_modetrue for spoken replies, which appends a brevity instruction to the system prompt - Answer a slow tool at once with a pending
toolmessage, then send the result later as a user message starting TOOL RESULT, as the card's asynchronous pattern says - Keep persistent rules in the system prompt and send state updates as separate user or system messages in order, per the card's prompting guidelines
Questions
Which is better for AI agents, Cola2 or NVIDIA Nemotron 3.5 Bolt E4B 4GB?
NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) on agent readiness against Cola2's 33.9 (F), and leads in every scored category.
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Machine-readable
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/compare/cola2-vs-nemotron-3-5-bolt-e4b-4gb.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/cola2.json·/api/v1/tools/nemotron-3-5-bolt-e4b-4gb.json - From a terminal
anchor compare cola2 nemotron-3-5-bolt-e4b-4gb(the CLI) - Over MCP
compare_tools {"a": "cola2", "b": "nemotron-3-5-bolt-e4b-4gb"}at/mcp, no key