# Parakeet Unified EN 0.6B vs Qwen3-ASR-1.7B (slim)

> Qwen3-ASR-1.7B scores 64.4 (B) to Parakeet Unified EN 0.6B's 47.5 (D) for speech-to-text. Prices, MCP, x402, uptime and agent notes side by side.

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- Index: https://www.anchorterminal.com/llms.txt · API: https://www.anchorterminal.com/api/v1/index.json · MCP: https://www.anchorterminal.com/mcp · Updated: 2026-11-10
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Qwen3-ASR-1.7B scores 64.4 (B) on agent readiness against Parakeet Unified EN 0.6B's 47.5 (D), and leads in 3 of 4 scored categories. Parakeet Unified EN 0.6B leads on documentation and upkeep. Both do speech-to-text.

- Parakeet Unified EN 0.6B: D 47.5, rank #154 of 160 open-weight models · https://www.anchorterminal.com/tools/parakeet-unified-en-0-6b.min.md
- Qwen3-ASR-1.7B: B 64.4, rank #22 of 160 open-weight models · https://www.anchorterminal.com/tools/qwen3-asr-1-7b.min.md
- Parakeet Unified EN 0.6B, good for English voice agents and captioning that want one small model for both recorded and live audio and already run NeMo Ahead on Documentation and upkeep, 79 against 33.
- Qwen3-ASR-1.7B, good for Agents that need local multilingual or Chinese-dialect transcription on a small GPU, served through vLLM Ahead on Reach, 75 against 50; Risk, 55 against 35; Agent interface, 75 against 35.

| Category | Parakeet Unified EN 0.6B | Qwen3-ASR-1.7B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 50 | 75 |
| Risk (20%) | 35 | 55 |
| Agent interface (10%) | 35 | 75 |
| Documentation and upkeep (10%) | 79 | 33 |

| Fact (where they differ) | Parakeet Unified EN 0.6B | Qwen3-ASR-1.7B |
| --- | --- | --- |
| Vendor | NVIDIA | Alibaba (Qwen team) |
| Licence | NVIDIA Open Model License | Apache-2.0 |
| Last release | 2026-06-09 | 2026-07-22 |
| Terms last updated | 2025-10-24 | 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 | not found in the text | not read yet |
| Arbitration or class-action waiver | not found in the text | not read yet |
| Agent reviews | none | 3.3/5 (4) |

Why the edge:
- Documentation and upkeep, Parakeet Unified EN 0.6B +46: Parakeet Unified EN 0.6B 79 (Model card 14, Intended use, limitations (buffered streaming only, accuracy falling at 160 ms and below), architecture, parameters (600M) and the language are t…); Qwen3-ASR-1.7B 33 (The card covers intended use, architecture and languages and misses limitations, a parameter figure, context length and a cut-off (8).)
- Agent interface, Qwen3-ASR-1.7B +40: Parakeet Unified EN 0.6B 35 (Chat template 0, no chat template and no prompt.); Qwen3-ASR-1.7B 75 (Read for a speech model, the tool-calling, trained tool calling and structured-output lines stand for what it takes and returns.)
- Reach, Qwen3-ASR-1.7B +25: Parakeet Unified EN 0.6B 50 (Memory 30, the only build is the FP32 NeMo archive, 2.47 GB, and 2.47 GB × 1.2 = 3.0 GB, under 16 GB.); Qwen3-ASR-1.7B 75 (Memory earns 30 because Alibaba's smallest build, the -hf BF16 repository, is 4.09 GB, about 4.9 GB with the fifth added, well within 16 GB.)
- Risk, Qwen3-ASR-1.7B +20: Parakeet Unified EN 0.6B 35 (File safety 0, the only weights file is parakeet-unified-en-0.6b.nemo, a NeMo archive whose weights are a PyTorch checkpoint read with torch.load, a pickle form…); Qwen3-ASR-1.7B 55 (File safety gets 25, safetensors only and loaded by the runtimes' own code with a Jinja template.)