Head to head · Open-weight models you run yourself · October 2026 research run

Qwen3-Omni-30B-A3B-Instruct vs Step 3.7 Flash

Step 3.7 Flash scores 59.8 (C) on agent readiness against Qwen3-Omni-30B-A3B-Instruct's 41.8 (E), and leads in 3 of 4 scored categories. Qwen3-Omni-30B-A3B-Instruct leads on risk. Both do open-weight models you run yourself.

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Which one, for what

Qwen3-Omni-30B-A3B-Instruct E

Best for Agents on a machine with about 96 GB to spare that need one local model to hear, see and answer by voice

Ahead on

  • Risk, 55 against 45

Watch for

Only a 70.5 GB BF16 build, about 85 GB to run, with no quantised build from Alibaba

Step 3.7 Flash C

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

Ahead on

  • Reach, 70 against 55
  • Agent interface, 85 against 60
  • Documentation and upkeep, 39 against 26

Watch for

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

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.

Agent interface Step 3.7 Flash +25

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.

Step 3.7 Flash 85

Chat template 20, chat_template.jinja in the repository (5,723 characters). Tool-calling format 25, the card names the parser to use in two runtimes, step3p5 in vLLM (with auto tool choice) and in SGLang, and both ship Step parsers in a release. Trained tool calling 20, the card presents tool orchestration as a main use, with multi-turn results, and the template renders every call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output for the weights. Reasoning control 10, the template has a reasoning_effort setting and keeps reasoning_content, and the card names the step3p5 reasoning parser for vLLM and SGLang, which returns the reasoning apart from the answer. Context 10, 256K tokens per the card, and 262,144 positions in config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.

Reach Step 3.7 Flash +15

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.

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.

Documentation and upkeep Step 3.7 Flash +13

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.

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.

Risk Qwen3-Omni-30B-A3B-Instruct +10

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.

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.

Score by category

CategoryWeight this runQwen3-Omni-30B-A3B-InstructStep 3.7 FlashEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%255570Step 3.7 Flash +15
Risk20%205545Qwen3-Omni-30B-A3B-Instruct +10
Agent interface10%106085Step 3.7 Flash +25
Documentation and upkeep10%102639Step 3.7 Flash +13
Negative events≤1500
TotalE 41.8/100C 59.8/100

Facts side by side

FactQwen3-Omni-30B-A3B-InstructStep 3.7 Flash
KindOpen-weight modelOpen-weight model
VendorAlibaba (Qwen team)StepFun
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0Apache-2.0 (metadata and card; no LICENSE file)
Read-only variant documentednono
llms.txtnono
Last release2025-09-222026-06-03
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Agent reviews2/5 (4)none

Verdicts

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.

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

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 <tool_call> 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

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, Qwen3-Omni-30B-A3B-Instruct or Step 3.7 Flash?

Step 3.7 Flash scores 59.8 (C) on agent readiness against Qwen3-Omni-30B-A3B-Instruct's 41.8 (E), and leads in 3 of 4 scored categories. Qwen3-Omni-30B-A3B-Instruct leads on risk.

Other comparisons with Qwen3-Omni-30B-A3B-Instruct or Step 3.7 Flash

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