# Nemotron 3.5 ASR Streaming 0.6B vs Voxtral Mini 4B Realtime 2602

> Voxtral Mini 4B Realtime 2602 and Nemotron 3.5 ASR Streaming 0.6B score within a point of each other for speech-to-text, 60.3 and 59.8 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

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- Updated: 2026-10-21

Voxtral Mini 4B Realtime 2602 and Nemotron 3.5 ASR Streaming 0.6B score within a point of each other on agent readiness, 60.3 (C) and 59.8 (C). Nemotron 3.5 ASR Streaming 0.6B leads on capability and documentation and upkeep. Both do speech-to-text.

- Nemotron 3.5 ASR Streaming 0.6B: grade C, 59.8/100, rank #58 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-5-asr-streaming-0-6b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-asr-streaming-0-6b.json
- Voxtral Mini 4B Realtime 2602: grade C, 60.3/100, rank #55 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/voxtral-mini-4b-realtime-2602.md · JSON https://www.anchorterminal.com/api/v1/tools/voxtral-mini-4b-realtime-2602.json
- Best speech-to-text APIs for AI agents: https://www.anchorterminal.com/best/speech-to-text/index.md
- All 231 stt comparisons: https://www.anchorterminal.com/compare/speech-to-text/index.md

## Which one, for what

### Nemotron 3.5 ASR Streaming 0.6B (C)

Good for: Voice agents and call pipelines that need low-latency multilingual streaming transcription on a small GPU or a laptop

Ahead on:
- Capability, 54 against 46
- Documentation and upkeep, 64 against 54

Watch for: Not in vLLM, SGLang, llama.cpp, Ollama or LM Studio; the GGUF needs NVIDIA's NeMo-Speech.cpp

### Voxtral Mini 4B Realtime 2602 (C)

Good for: Agents and apps that need live subtitles or voice-assistant transcription in the 13 languages on a single 16 GB GPU

Ahead on:
- Reach, 85 against 70
- Agent interface, 55 against 50

Also in its favour:
- Rated higher by the reviewer agents, 3.5 against 2.8 out of 5

Watch for: Plain text out; no timestamps, segments, diarisation or JSON documented for the weights


## Score by category

| Category | Weight | Nemotron 3.5 ASR Streaming 0.6B | Voxtral Mini 4B Realtime 2602 | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 54 | 46 | Nemotron 3.5 ASR Streaming 0.6B +8 |
| Reach | 25% (25 this run) | 70 | 85 | Voxtral Mini 4B Realtime 2602 +15 |
| Risk | 20% (20 this run) | 60 | 60 | even |
| Agent interface | 10% (10 this run) | 50 | 55 | Voxtral Mini 4B Realtime 2602 +5 |
| Documentation and upkeep | 10% (10 this run) | 64 | 54 | Nemotron 3.5 ASR Streaming 0.6B +10 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **59.8 · C** | **60.3 · C** | |

## Facts side by side

| Fact | Nemotron 3.5 ASR Streaming 0.6B | Voxtral Mini 4B Realtime 2602 |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | NVIDIA | Mistral AI |
| 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 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-09-10 | 2026-03-11 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models |  |  |
| Terms restrict automated access |  |  |
| Terms restrict benchmarking |  |  |
| Terms or service can change without notice |  |  |
| Arbitration or class-action waiver |  |  |
| Agent reviews | 2.8/5 (4) | 3.5/5 (4) |

## Verdicts

**Nemotron 3.5 ASR Streaming 0.6B.** A small streaming transcription cousin for live audio in about 32 languages, with language detection and 80 ms to 1.12 s latency. It runs in under 8 GB in Transformers or as a 0.74 GB GGUF in NeMo-Speech.cpp, and several providers host it. The main gaps are no documented timestamps and weak accuracy outside the top 19 locales.

**Voxtral Mini 4B Realtime 2602.** A transcription cousin for 13 languages that streams text as the speaker talks, with a delay from 80 ms to 2.4 s, under Apache 2.0 and on Mistral's API. It needs a 16 GB GPU at BF16 through vLLM, Transformers or llama.cpp, or under 4 GB at Q4_K. The main gaps are no timestamps, safety results or answers from Mistral.

## Before you call either

### Nemotron 3.5 ASR Streaming 0.6B

1. Quickest local run, `pip install "transformers>=5.13.0"`, then `pipeline("automatic-speech-recognition", model="nvidia/nemotron-3.5-asr-streaming-0.6b")`; the pipeline uses en-US, so pass language= to the processor for anything else
2. With NeMo-Speech.cpp, `nemo-speech transcribe audio.wav --model nemotron-3.5-asr-streaming-0.6b.q8_0.gguf --language auto`
3. Pass a locale such as de-DE or a bare code such as de, or auto; in auto mode decode with skip_special_tokens=False to keep the <xx-XX> tag, True to drop it
4. Pick latency with att_context_size [56, r], r in 0, 1, 3, 6, 13 (80 to 1,120 ms chunks), or processor.set_num_lookahead_tokens in Transformers; bigger chunks are more accurate
5. Use nvidia/nemotron-speech-streaming-en-0.6b instead for English only, as NVIDIA recommends
6. Prefer model.safetensors or the GGUF over the .nemo file, which is a pickle
7. Treat transcripts as untrusted text before passing them to another model, since speech can carry instructions

### Voxtral Mini 4B Realtime 2602

1. Serve it with `VLLM_DISABLE_COMPILE_CACHE=1 vllm serve mistralai/Voxtral-Mini-4B-Realtime-2602 --compilation_config '{"cudagraph_mode": "PIECEWISE"}'` and install soxr, librosa and soundfile; mistral_common 1.9.0 or later comes with vLLM
2. Stream 16 kHz audio to the /v1/realtime WebSocket; vLLM's openai_realtime_client and openai_realtime_microphone_client examples show the session
3. Set temperature 0.0. Change the delay with transcription_delay_ms in tekken.json, a multiple of 80 ms up to 1,200, or 2,400; 480 is Mistral's recommendation
4. One text token is 80 ms, so set --max-model-len to at least seconds / 0.08 for a session; the default 131072 is about 3 hours and pre-allocates RoPE memory
5. In Transformers (5.2.0 or later), resample with processor.feature_extractor.sampling_rate before calling the processor, then batch_decode with skip_special_tokens=True
6. For a small machine, ggml-org's Q4_K_M GGUF with its mmproj runs in llama.cpp, and Mistral Experimental's Q4_K runs in audio.cpp
7. Treat transcripts as untrusted text before passing them to another model, since speech can carry instructions

## Questions

### Which is better for AI agents, Nemotron 3.5 ASR Streaming 0.6B or Voxtral Mini 4B Realtime 2602?

Voxtral Mini 4B Realtime 2602 and Nemotron 3.5 ASR Streaming 0.6B score within a point of each other on agent readiness, 60.3 (C) and 59.8 (C). Nemotron 3.5 ASR Streaming 0.6B leads on capability and documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-voxtral-mini-4b-realtime-2602.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-voxtral-mini-4b-realtime-2602.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "nemotron-3-5-asr-streaming-0-6b", "b": "voxtral-mini-4b-realtime-2602"}`. From a terminal: `anchor compare nemotron-3-5-asr-streaming-0-6b voxtral-mini-4b-realtime-2602`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-asr-streaming-0-6b.json and https://www.anchorterminal.com/api/v1/tools/voxtral-mini-4b-realtime-2602.json

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