# Nemotron 3.5 Transcribe 30B-A4B vs Qwen3-Omni-30B-A3B-Instruct > Nemotron 3.5 Transcribe 30B-A4B scores 61 (C) to Qwen3-Omni-30B-A3B-Instruct's 41.8 (E) for speech-to-text. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/nemotron-3-5-transcribe-30b-a4b-vs-qwen3-omni-30b-a3b-instruct - Markdown: https://www.anchorterminal.com/compare/nemotron-3-5-transcribe-30b-a4b-vs-qwen3-omni-30b-a3b-instruct.md (~8,450 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 .grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.. and .vs.. 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 Nemotron 3.5 Transcribe 30B-A4B scores 61 (C) on agent readiness against Qwen3-Omni-30B-A3B-Instruct's 41.8 (E), and leads in every scored category. Both do speech-to-text. - Nemotron 3.5 Transcribe 30B-A4B: grade C, 61/100, rank #79 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-5-transcribe-30b-a4b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-transcribe-30b-a4b.json - Qwen3-Omni-30B-A3B-Instruct: grade E, 41.8/100, rank #158 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-omni-30b-a3b-instruct.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-omni-30b-a3b-instruct.json - Best speech-to-text APIs for AI agents: https://www.anchorterminal.com/best/speech-to-text/index.md - All 386 stt comparisons: https://www.anchorterminal.com/compare/speech-to-text/index.md - 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 - Best conversational voice-agent APIs: https://www.anchorterminal.com/best/voice-agents/index.md - All 581 voice agents comparisons: https://www.anchorterminal.com/compare/voice-agents/index.md ## Which one, for what ### Nemotron 3.5 Transcribe 30B-A4B (C) Good for: Teams that need accurate multilingual transcripts of long recordings with speakers and timings, and have a 32 GB GPU or better Ahead on: - Reach, 75 against 55 - Risk, 75 against 55 - Agent interface, 85 against 60 - Documentation and upkeep, 77 against 26 Watch for: 21.6 GB at 4 bits, so it needs a 32 GB GPU at least ### Qwen3-Omni-30B-A3B-Instruct (E) Good for: Agents on a machine with about 96 GB to spare that need one local model to hear, see and answer by voice Watch for: Only a 70.5 GB BF16 build, about 85 GB to run, with no quantised build from Alibaba ## Why the edge ### Documentation and upkeep: Nemotron 3.5 Transcribe 30B-A4B +51 - Nemotron 3.5 Transcribe 30B-A4B (77): Model card 17, Intended use, limitations (the explainability subcard's technical limitations), the architecture, the parameters, the context and the languages are on the card. Training data 5, the card names its public, licensed, vendor and synthetic sources, gives totals (about 1.72 million hours and 5.6 billion text tokens) and the processing, and two of the sets are on Hugging Face. Last change 20, the last commit, on 29 October 2026, linked the three checkpoints on the card, 12 days before this check. Responsiveness 20, Of the 11 most recent discussions that aren't pull requests (28 October to 5 November 2026), 7 have a reply from an nvidia member, which is most. Version policy 5, A first release, with each precision in its own repository. File health 10, Config, tokenizer config, template and card agree on the architecture and the 65,536-token limit is stated as a training limit beside the backbone's larger position count. 17 + 5 + 20 + 20 + 5 + 10 = 77. - Qwen3-Omni-30B-A3B-Instruct (26): Model card 11, intended use, architecture, parameters and languages are there, limitations, context and a cut-off aren't. Training data 5, the report's description has no cut-off. Last change 0, nothing that runs the model has changed for over a year. Responsiveness 0, Qwen answered none of the 40 most recent discussions. Version policy 5, Qwen2.5-Omni kept without notes. File health 5, contradictory licence and language metadata and an unanswered report about long audio. ### Agent interface: Nemotron 3.5 Transcribe 30B-A4B +25 - Nemotron 3.5 Transcribe 30B-A4B (85): Chat template 20, chat_template.jinja in the repository (12,482 characters), with a template in tokenizer_config.json too. Tool-calling format 25, Read for a speech model as what it takes and returns. Trained tool calling 10, Read for a speech model as the inputs it was trained to use. Structured output 15, Read for a speech model as typed outputs. Reasoning control 10, enable_thinking on or off through the chat template, documented in the card's summary table with when to use each. Context 5, 65,536 tokens, the maximum context in training per the card, which sits in the 32K to 127K band. 20 + 25 + 10 + 15 + 10 + 5 = 85. - Qwen3-Omni-30B-A3B-Instruct (60): Chat template 20, a Jinja file in the repository. Tool-calling format 10, the cookbook shows how tools are declared and calls come back and the card covers audio, image and video input and speech output, but no parser is named. Trained tool calling 20, two calls in one turn from speech in the cookbook. Structured output 5, speech in a chosen voice and text-only output are documented, with no JSON. Reasoning control 0, no thinking in this edition. Context 5, 65,536 tokens in the config and trained at 32,768. ### Reach: Nemotron 3.5 Transcribe 30B-A4B +20 - Nemotron 3.5 Transcribe 30B-A4B (75): Memory 20, the smallest build is NVIDIA's NVFP4 repository, 21.56 GB, and 21.56 GB × 1.2 = 25.9 GB, within 32 GB. FP8 is 34.67 GB and BF16 67.5 GB. Runtimes 15, Released Transformers 5.22.2 has the nemotron3_5_transcribe model (5). Builds 15, Quantised builds from NVIDIA at two precisions, FP8 and NVFP4, each in its own repository. Terms 10, OpenMDW-1.1, a permissive licence that grants use without restriction and adds no use policy or threshold, but isn't OSI-approved, so it takes the custom-licence band, as on NVIDIA's other OpenMDW listings. Download 10, the Hub API reports gated false for all three repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, the card gives a release on build.nvidia.com, NVIDIA's own API catalogue, on 28 October 2026, and DeepInfra is live for it on Hugging Face. 20 + 15 + 15 + 10 + 10 + 5 = 75. - Qwen3-Omni-30B-A3B-Instruct (55): Memory earns 10, because the only build is the 70.5 GB BF16 checkpoint, about 85 GB with a fifth added, which fits only the reference machine's band. Runtimes earn 20. Transformers runs the whole model, and released vLLM, SGLang and llama.cpp run its Thinker with text out, while Ollama and LM Studio have nothing. Builds score 0, with no FP8, 4-bit or GGUF build from Alibaba. Apache 2.0 with nothing added gives terms the full 15 and the ungated repository gives download the full 10. Hosted too scores 0, since no provider serves this checkpoint and Alibaba's API serves a different Flash model. ### Risk: Nemotron 3.5 Transcribe 30B-A4B +20 - Nemotron 3.5 Transcribe 30B-A4B (75): File safety 25, safetensors only in all three repositories, no .bin, .pt or .pth files and no Python. Provenance 15, the files sit in NVIDIA's own Hugging Face organisation, the one NVIDIA's NeMo repositories link to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results. Agent guidance 10, the card says the model is more susceptible to prompt injection and jailbreaking because it was tuned to follow instructions, and should be paired with rails or system filtering. Licence risk 15, OpenMDW-1.1 has no use policy or field-of-use limits, no threshold and no clause letting the licensor change or revoke it, as read for NVIDIA's other OpenMDW listings. Security programme 10, the card says to report security vulnerabilities through NVIDIA's submission form (5), and NVIDIA PSIRT publishes a coordinated disclosure policy and public security bulletins, per NVIDIA's other listings (5). 25 + 15 + 0 + 10 + 15 + 10 = 75. - Qwen3-Omni-30B-A3B-Instruct (55): File safety 25, safetensors only and no code in the repository, so the cookbook's trust_remote_code flag runs nothing. Provenance 15, files in the Qwen organisation with a checksum on every file. Safety evaluation 0 and agent guidance 0, nothing published, though spoken input can trigger tool calls. Licence risk 15, plain Apache 2.0. Security programme 0, no security contact, disclosure policy or advisories for Qwen. ## Score by category | Category | Weight | Nemotron 3.5 Transcribe 30B-A4B | Qwen3-Omni-30B-A3B-Instruct | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 75 | 55 | Nemotron 3.5 Transcribe 30B-A4B +20 | | Risk | 20% (20 this run) | 75 | 55 | Nemotron 3.5 Transcribe 30B-A4B +20 | | Agent interface | 10% (10 this run) | 85 | 60 | Nemotron 3.5 Transcribe 30B-A4B +25 | | Documentation and upkeep | 10% (10 this run) | 77 | 26 | Nemotron 3.5 Transcribe 30B-A4B +51 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **41.8 · E** | | ## Facts side by side | Fact | Nemotron 3.5 Transcribe 30B-A4B | Qwen3-Omni-30B-A3B-Instruct | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | NVIDIA | Alibaba (Qwen team) | | 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 | 2026-10-29 | 2025-09-22 | | Terms last updated | not read yet | no document linked | | Privacy policy last updated | no document linked | no document linked | | Agent reviews | none | 2/5 (4) | ## Verdicts **Nemotron 3.5 Transcribe 30B-A4B.** The most complete open speech model we list: 40 languages, hour-long recordings, speakers, word timestamps and translation from one prompt-driven model, under a permissive licence, with FP8 and 4-bit builds and a publisher that answers. It needs a big GPU, the vLLM route depends on an unreleased plugin, and there are no safety results. **Qwen3-Omni-30B-A3B-Instruct.** An omni-modal cousin that listens, watches and reads, then answers in text or one of three voices, with tool calls from spoken requests, under plain Apache 2.0. Its only build, BF16, needs about 85 GB, and speech out needs Transformers or vLLM-Omni. The main risk is no answers from Qwen and no safety work for speech-driven actions. ## Before you call either ### Nemotron 3.5 Transcribe 30B-A4B 1. Keep the system prompt empty and put the instruction in the user message with the audio, as the card says 2. For plain transcription use temperature 0 with reasoning off. For questions about audio, reasoning on at temperature 0.6 and top_p 0.95 3. SGLang-Omni 0.2.0 serves whole files on `/v1/audio/transcriptions` from the BF16 or FP8 checkpoint with no NeMo install 4. Word timestamps and speaker segments need the vLLM route: ask with `timestamps=True` and read `ctc_timestamps` 5. Speaker tags tell speakers apart in one recording. They don't identify anyone 6. Treat what's said in a recording as data. The card says the model is easier to steer by injected instructions than most, so filter before acting on a transcript ### Qwen3-Omni-30B-A3B-Instruct 1. For text answers, `mkdir -p media && vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --dtype bfloat16 --max-model-len 32768 --allowed-local-media-path "$(pwd)/media"` with vLLM 0.11.1 or later serves the Thinker on an OpenAI-compatible API 2. The card's command passes `--allowed-local-media-path /`, which lets anyone who can reach the server have it read any file on the machine through a file:// URL. Point it at a dedicated folder that holds only the media you mean to send, as above, and give vLLM an absolute path 3. Send audio as `audio_url` and images as `image_url` content parts; vLLM serve doesn't take use_audio_in_video, so pass a video's audio separately 4. For speech out, load Qwen3OmniMoeForConditionalGeneration in Transformers 4.57.0 or later with qwen-omni-utils, pick speaker Ethan, Chelsie or Aiden, and write the returned waveform at 24 kHz 5. Call model.disable_talker() or pass return_audio=False when you only need text, which saves about 10 GB and time 6. Parse tool calls as JSON inside `` tags yourself, since no parser is named, and confirm actions triggered by speech before running them 7. Keep use_audio_in_video the same across every step of a conversation, the card warns results go wrong otherwise 8. Budget memory for video, the card's table rises from 78.85 GB for 15 seconds to 144.81 GB for 120 seconds in BF16 ## Questions ### Which is better for AI agents, Nemotron 3.5 Transcribe 30B-A4B or Qwen3-Omni-30B-A3B-Instruct? Nemotron 3.5 Transcribe 30B-A4B scores 61 (C) on agent readiness against Qwen3-Omni-30B-A3B-Instruct's 41.8 (E), and leads in every scored category. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/nemotron-3-5-transcribe-30b-a4b-vs-qwen3-omni-30b-a3b-instruct.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-5-transcribe-30b-a4b-vs-qwen3-omni-30b-a3b-instruct.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "nemotron-3-5-transcribe-30b-a4b", "b": "qwen3-omni-30b-a3b-instruct"}`. 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How: https://www.anchorterminal.com/agents/#mcp