# NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Step 3.7 Flash

> NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) to Step 3.7 Flash's 59.8 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-step-3-7-flash
- Markdown: https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-step-3-7-flash.md (~10,200 tokens)
- Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key)
- DNS: dig +short TXT <slug>.grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.<category>. and <a>.vs.<b>. under the zone, and the same under grade.letme.dev)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-11-10

NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) on agent readiness against Step 3.7 Flash's 59.8 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.

- NVIDIA Nemotron 3.5 Bolt E4B 4GB: grade C, 61/100, rank #49 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-5-bolt-e4b-4gb.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-bolt-e4b-4gb.json
- Step 3.7 Flash: grade C, 59.8/100, rank #93 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/step-3-7-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/step-3-7-flash.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### NVIDIA Nemotron 3.5 Bolt E4B 4GB (C)

Good 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 70
- Risk, 80 against 45
- Documentation and upkeep, 65 against 39

Watch for: 64K tokens of context against the base's 128K, and Ollama's and LM Studio's pages show 128K anyway

### Step 3.7 Flash (C)

Good for: Teams with a GPU server or a provider account that want an open, long-context agent model with vision

Watch for: About 131 GB to run at 4 bits with images, over a 128 GB machine's share


## Why the edge

### Risk: NVIDIA Nemotron 3.5 Bolt E4B 4GB +35

- 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.
- Step 3.7 Flash (45): File safety 15, safetensors and GGUF only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the stepfun-ai organisation, which the card's StepFun platform and model page belong to (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations section and gives no guidance on prompt injection, instruction priority, refusals or safeguards. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 0, no way to report a flaw on the card, and our StepFun API listing found no security.txt, bug bounty or disclosure policy. 15 + 15 + 0 + 0 + 15 + 0 = 45.

### Documentation and upkeep: NVIDIA Nemotron 3.5 Bolt E4B 4GB +26

- 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.
- Step 3.7 Flash (39): Model card 14, Intended use, the architecture, the parameters, the context and the language (in the metadata) are on the card. Training data 0, Nothing on the card about the training data. Last change 10, the last commit, on 3 June 2026, fixed the modelling code, 160 days before this check and within 180. Responsiveness 0, none of the 11 most recent discussions that aren't pull requests (29 May to 24 June 2026) has a reply from a stepfun-ai member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, Step-3.5-Flash stays in its own repositories and 3.7 came out in new ones, with no dated note on the older cards that we read. File health 10, Config, template and card agree on the architecture and the context, and June commits fixed the modelling code. 14 + 0 + 10 + 0 + 5 + 10 = 39.

### Reach: NVIDIA Nemotron 3.5 Bolt E4B 4GB +20

- 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.
- Step 3.7 Flash (70): Memory 5, the smallest build at 4 bits is StepFun's IQ4_XS GGUF, 104.99 GB with the 3.97 GB projector, and 108.96 GB × 1.2 = 130.8 GB, over what the reference machine can give a model and within 256 GB. The IQ3 and Q3 builds are below 4 bits and aren't read. Runtimes 20, Released vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 have the step3p7 model, and llama.cpp b12242 has the architecture, with StepFun's own GGUF. Four at 5 each, and the card documents all four. Builds 15, StepFun publishes GGUF at five quantised precisions, an FP8 build and an NVFP4 build. Terms 15, Apache 2.0 in the card's metadata and its licence section, an OSI licence with nothing added. Download 10, the Hub API reports gated false for all four repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, StepFun's own API serves it, per the card and our StepFun API listing, and Featherless and DeepInfra are live for it on Hugging Face. 5 + 20 + 15 + 15 + 10 + 5 = 70.

## Score by category

| Category | Weight | NVIDIA Nemotron 3.5 Bolt E4B 4GB | Step 3.7 Flash | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 90 | 70 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +20 |
| Risk | 20% (20 this run) | 80 | 45 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +35 |
| Agent interface | 10% (10 this run) | 85 | 85 | even |
| Documentation and upkeep | 10% (10 this run) | 65 | 39 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +26 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **61 · C** | **59.8 · C** | |

## Facts side by side

| Fact | NVIDIA Nemotron 3.5 Bolt E4B 4GB | Step 3.7 Flash |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | NVIDIA | StepFun |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | OpenMDW-1.1 | Apache-2.0 (metadata and card; no LICENSE file) |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-23 | 2026-06-03 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 4/5 (4) | none |

## Verdicts

**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.

**Step 3.7 Flash.** A capable open agent model with a clean licence, every kind of build and four released runtimes. Two things hold it back: it's a little too big for a 128 GB machine at 4 bits, whatever the card says, and StepFun has gone quiet on the repository since June. No safety results, limitations or data description.

## Before you call either

### NVIDIA Nemotron 3.5 Bolt E4B 4GB

1. Quickest start is `ollama run nemotron-3.5-bolt:e4b`, about 8 GB of files, and set `num_ctx` to 65536 or less, since NVIDIA supports 64K
2. 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
3. With vLLM 0.31.0 or later pass `--enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4` and the repository's chat_template.jinja
4. Pass `enable_thinking` explicitly in `chat_template_kwargs`, since the template defaults to off whatever the card says, and set `skip_special_tokens` false as the card's examples do
5. Use `temperature=1.0` and `top_p=0.95` in both modes, as the card says
6. Set `conversational_mode` true for spoken replies, which appends a brevity instruction to the system prompt
7. Answer a slow tool at once with a pending `tool` message, then send the result later as a user message starting TOOL RESULT, as the card's asynchronous pattern says
8. 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

### Step 3.7 Flash

1. On a 128 GB machine the 4-bit GGUF won't fit with a working context. Use StepFun's API or a provider, or a server
2. On vLLM or SGLang pass `--tool-call-parser step3p5 --reasoning-parser step3p5`
3. The card's commands pass trust-remote-code. Released Transformers 5.22.2 has the model built in if you'd rather not run the repository's code
4. For llama.cpp load the mmproj file from StepFun's GGUF repository for image input
5. StepFun's API has two hosts. Use the one where your key was issued, `api.stepfun.ai` or `api.stepfun.com`
6. The same model is on our StepFun API listing, with prices and terms

## Questions

### Which is better for AI agents, NVIDIA Nemotron 3.5 Bolt E4B 4GB or Step 3.7 Flash?

NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) on agent readiness against Step 3.7 Flash's 59.8 (C), and leads in 3 of 4 scored categories.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-step-3-7-flash.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-step-3-7-flash.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "nemotron-3-5-bolt-e4b-4gb", "b": "step-3-7-flash"}`. From a terminal: `anchor compare nemotron-3-5-bolt-e4b-4gb step-3-7-flash`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-bolt-e4b-4gb.json and https://www.anchorterminal.com/api/v1/tools/step-3-7-flash.json

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