Head to head · Open-weight models you run yourself · October 2026 research run
Qwen3.5-397B-A17B vs Step 3.7 Flash
Qwen3.5-397B-A17B scores 64.1 (B) on agent readiness against Step 3.7 Flash's 59.8 (C), and leads in 1 of 4 scored categories. Step 3.7 Flash leads on agent interface and documentation and upkeep. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 6359 open weights comparisons
Which one, for what
Best for Agents with access to an 8-GPU server, or calling a hosted endpoint, that want the strongest open Qwen3.5 with vision and a plain licence.
Ahead on
- Risk, 55 against 45
Watch for
No build fits a 256 GB machine, and Ollama has no local build
Best for Teams with a GPU server or a provider account that want an open, long-context agent model with vision
Ahead on
- Agent interface, 85 against 70
- Documentation and upkeep, 39 against 34
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 +15
Qwen3.5-397B-A17B 70
The template ships in the repository (20) and renders several tool calls per turn for a model trained for tool use (20). Tool-calling format gets 10, because the card names the qwen3_coder parser but leaves the declaration and call format to the template. Thinking turns off per request and comes back in think tags split by the qwen3 parser (10), and 262,144 tokens is native (10). Structured output scores 0, as nothing documents JSON output for the model.
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.
Risk Qwen3.5-397B-A17B +10
Qwen3.5-397B-A17B 55
Safetensors only with no repository code run on load or to build prompts gives file safety 25, and files in the Qwen organisation the publisher's GitHub links to, each hashed on the Hub, give provenance 15. Plain Apache 2.0 keeps licence risk at 15. Safety evaluation, agent guidance and a security programme score 0. The card has no safety results (its SecCodeBench row is a coding capability), no cautions, and no reporting route was found. The Qwen3.5 blog and Alibaba's security response centre couldn't be read, so both are scored as absent and left open.
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 Step 3.7 Flash +5
Qwen3.5-397B-A17B 34
The card covers five of the seven items and misses limitations and a cut-off (14). Training data is a general description without a cut-off (5). The files last changed on 24 April 2026, 192 days before this check, past the 180-day band and within a year (5), and Qwen's GitHub repository holds only a README and licence, so no reference code of Alibaba's changed later. Alibaba answered none of the 40 most recent questions and reports, recounted on 2 November (0), and GitHub issues couldn't be counted. It's a first release at this size (5), and two template reports and a template fix still wait unanswered (5). 14 + 5 + 5 + 0 + 5 + 5 = 34.
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.
Score by category
| Category | Weight this run | Qwen3.5-397B-A17B | Step 3.7 Flash | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 70 | 70 | even |
| Risk | 20%20 | 55 | 45 | Qwen3.5-397B-A17B +10 |
| Agent interface | 10%10 | 70 | 85 | Step 3.7 Flash +15 |
| Documentation and upkeep | 10%10 | 34 | 39 | Step 3.7 Flash +5 |
| Negative events | ≤15 | 0 | 0 | |
| Total | B 64.1/100 | C 59.8/100 |
Facts side by side
| Fact | Qwen3.5-397B-A17B | Step 3.7 Flash |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Alibaba (Qwen team) | StepFun |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | Apache-2.0 (metadata and card; no LICENSE file) |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-02-16 | 2026-06-03 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 2.8/5 (4) | none |
How the scores have moved
Step 3.7 Flash
No score change since it was listed.
Verdicts
Qwen3.5-397B-A17B
Alibaba's biggest open Qwen3.5, a cousin for hard coding, agent and vision work. Even at 4 bits it needs about 270 GB, so most agents will call it through Alibaba's Model Studio or one of the many providers that host it. The main risk is silence, with no safety results, no agent guidance and template reports without a reply.
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
Qwen3.5-397B-A17B
- The card's server command is
vllm serve Qwen/Qwen3.5-397B-A17B --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder, and the FP8 build halves the memory - It doesn't fit our 128 GB reference machine at any precision, so call a hosted endpoint instead (Model Studio's Qwen3.5-Plus, or a provider serving Qwen/Qwen3.5-397B-A17B)
- On Model Studio pass
"enable_thinking": falsedirectly, while vLLM and SGLang take"chat_template_kwargs": {"enable_thinking": false} - Sampling per the card is temperature 0.6, top_p 0.95 and top_k 20 when thinking, and temperature 0.7, top_p 0.8, top_k 20 and presence_penalty 1.5 without
- For multi-token prediction in vLLM add
--speculative-config '{"method":"qwen3_next_mtp","num_speculative_tokens":2}' - Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens
- Validate any JSON it returns, since structured output isn't documented
Step 3.7 Flash
- 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
- On vLLM or SGLang pass
--tool-call-parser step3p5 --reasoning-parser step3p5 - 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
- For llama.cpp load the mmproj file from StepFun's GGUF repository for image input
- StepFun's API has two hosts. Use the one where your key was issued,
api.stepfun.aiorapi.stepfun.com - The same model is on our StepFun API listing, with prices and terms
Questions
Which is better for AI agents, Qwen3.5-397B-A17B or Step 3.7 Flash?
Qwen3.5-397B-A17B scores 64.1 (B) on agent readiness against Step 3.7 Flash's 59.8 (C), and leads in 1 of 4 scored categories. Step 3.7 Flash leads on agent interface and documentation and upkeep.
Other comparisons with Qwen3.5-397B-A17B or Step 3.7 Flash
- Agents-A1-4B vs Qwen3.5-397B-A17B
- Agents-A1-4B vs Step 3.7 Flash
- Apertus v1.5 8B vs Qwen3.5-397B-A17B
- Apertus v1.5 8B vs Step 3.7 Flash
- Apodex-1.1-mini vs Qwen3.5-397B-A17B
- Apodex-1.1-mini vs Step 3.7 Flash
- Arete-27B vs Qwen3.5-397B-A17B
- Arete-27B vs Step 3.7 Flash
- BigBang-v1 vs Qwen3.5-397B-A17B
- BigBang-v1 vs Step 3.7 Flash
- Carbon-X vs Qwen3.5-397B-A17B
- Carbon-X vs Step 3.7 Flash
- Cola2 vs Qwen3.5-397B-A17B
- Cola2 vs Step 3.7 Flash
- Cosmos-Reason2-2B vs Qwen3.5-397B-A17B
- Cosmos-Reason2-2B vs Step 3.7 Flash
- Darwin-36B-Opus vs Qwen3.5-397B-A17B
- Darwin-36B-Opus vs Step 3.7 Flash
- DeepSeek-V3.2 vs Qwen3.5-397B-A17B
- DeepSeek-V3.2 vs Step 3.7 Flash
- DeepSeek V4.1 Flash vs Qwen3.5-397B-A17B
- DeepSeek V4.1 Flash vs Step 3.7 Flash
- DeepSeek-V4-Flash-0731 vs Qwen3.5-397B-A17B
- DeepSeek-V4-Flash-0731 vs Step 3.7 Flash
- Devstral Small 2 vs Qwen3.5-397B-A17B
- Devstral Small 2 vs Step 3.7 Flash
- DiffusionGemma 26B A4B IT vs Qwen3.5-397B-A17B
- DiffusionGemma 26B A4B IT vs Step 3.7 Flash
- EXAONE 4.5 33B vs Qwen3.5-397B-A17B
- EXAONE 4.5 33B vs Step 3.7 Flash
- Gemma 4 12B IT vs Qwen3.5-397B-A17B
- Gemma 4 12B IT vs Step 3.7 Flash
- Gemma 4 26B A4B IT vs Qwen3.5-397B-A17B
- Gemma 4 26B A4B IT vs Step 3.7 Flash
- Gemma 4 31B IT vs Qwen3.5-397B-A17B
- Gemma 4 31B IT vs Step 3.7 Flash
- Gemma 4 E2B IT vs Qwen3.5-397B-A17B
- Gemma 4 E2B IT vs Step 3.7 Flash
- Gemma 4 E4B IT vs Qwen3.5-397B-A17B
- Gemma 4 E4B IT vs Step 3.7 Flash
- GLM-4.5-Air vs Qwen3.5-397B-A17B
- GLM-4.5-Air vs Step 3.7 Flash
- GLM-4.7-Flash vs Qwen3.5-397B-A17B
- GLM-4.7-Flash vs Step 3.7 Flash
- GLM-5.3-Flash vs Qwen3.5-397B-A17B
- GLM-5.3-Flash vs Step 3.7 Flash
- GLM-5.3 vs Qwen3.5-397B-A17B
- GLM-5.3 vs Step 3.7 Flash
- gpt-oss-120b vs Qwen3.5-397B-A17B
- gpt-oss-120b vs Step 3.7 Flash
- gpt-oss-20b vs Qwen3.5-397B-A17B
- gpt-oss-20b vs Step 3.7 Flash
- Granite 4.2 30B vs Qwen3.5-397B-A17B
- Granite 4.2 30B vs Step 3.7 Flash
- Granite 4.2 3B vs Qwen3.5-397B-A17B
- Granite 4.2 3B vs Step 3.7 Flash
- Granite 4.2 8B vs Qwen3.5-397B-A17B
- Granite 4.2 8B vs Step 3.7 Flash
- Hy-Memory-30B-A3B vs Qwen3.5-397B-A17B
- Hy-Memory-30B-A3B vs Step 3.7 Flash
- Hy3 vs Qwen3.5-397B-A17B
- Hy3 vs Step 3.7 Flash
- Inkling-Small vs Qwen3.5-397B-A17B
- Inkling-Small vs Step 3.7 Flash
- Inkling vs Qwen3.5-397B-A17B
- Inkling vs Step 3.7 Flash
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3.5-397B-A17B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Step 3.7 Flash
- Kimi K3.1 Pro vs Qwen3.5-397B-A17B
- Kimi K3.1 Pro vs Step 3.7 Flash
- Kimi K3 vs Qwen3.5-397B-A17B
- Kimi K3 vs Step 3.7 Flash
- Kolibri-1 vs Qwen3.5-397B-A17B
- Kolibri-1 vs Step 3.7 Flash
- Kronos vs Qwen3.5-397B-A17B
- Kronos vs Step 3.7 Flash
- Laguna M.1 vs Qwen3.5-397B-A17B
- Laguna M.1 vs Step 3.7 Flash
- Laguna S 2.1 vs Qwen3.5-397B-A17B
- Laguna S 2.1 vs Step 3.7 Flash
- Laguna XS 2.1 vs Qwen3.5-397B-A17B
- Laguna XS 2.1 vs Step 3.7 Flash
- LBS-27B vs Qwen3.5-397B-A17B
- LBS-27B vs Step 3.7 Flash
- LFM2.5-2.6B vs Qwen3.5-397B-A17B
- LFM2.5-2.6B vs Step 3.7 Flash
- LFM3-1.7B vs Qwen3.5-397B-A17B
- LFM3-1.7B vs Step 3.7 Flash
- LFM3-Audio-2B vs Qwen3.5-397B-A17B
- LFM3-Audio-2B vs Step 3.7 Flash
- LFM3-Omni-2B vs Qwen3.5-397B-A17B
- LFM3-Omni-2B vs Step 3.7 Flash
- LFM3-VL-2B vs Qwen3.5-397B-A17B
- LFM3-VL-2B vs Step 3.7 Flash
- Ling-3.0-tiny vs Qwen3.5-397B-A17B
- Ling-3.0-tiny vs Step 3.7 Flash
- Ling-3.1-flash-1019 vs Qwen3.5-397B-A17B
- Ling-3.1-flash-1019 vs Step 3.7 Flash
- LongCat-2.5 vs Qwen3.5-397B-A17B
- LongCat-2.5 vs Step 3.7 Flash
- MiMo-V2.6 Distill Qwen 9B vs Qwen3.5-397B-A17B
- MiMo-V2.6 Distill Qwen 9B vs Step 3.7 Flash
- MiMo-V2.6-Flash-RL vs Qwen3.5-397B-A17B
- MiMo-V2.6-Flash-RL vs Step 3.7 Flash
- MiniCPM-o 4.5 vs Qwen3.5-397B-A17B
- MiniCPM-o 4.5 vs Step 3.7 Flash
- MiniCPM-V 4.7 1B vs Qwen3.5-397B-A17B
- MiniCPM-V 4.7 1B vs Step 3.7 Flash
- MiniCPM-V 4.7 35B-A3B vs Qwen3.5-397B-A17B
- MiniCPM-V 4.7 35B-A3B vs Step 3.7 Flash
- MiniCPM5-2B vs Qwen3.5-397B-A17B
- MiniCPM5-2B vs Step 3.7 Flash
- MiniMax-M2.5 vs Qwen3.5-397B-A17B
- MiniMax-M2.5 vs Step 3.7 Flash
- MiniMax-M2.7 vs Qwen3.5-397B-A17B
- MiniMax-M2.7 vs Step 3.7 Flash
- MiniMax-M3.1-Flash vs Qwen3.5-397B-A17B
- MiniMax-M3.1-Flash vs Step 3.7 Flash
- Mistral Large 4 vs Qwen3.5-397B-A17B
- Mistral Large 4 vs Step 3.7 Flash
- Muse Glimmer 30B vs Qwen3.5-397B-A17B
- Muse Glimmer 30B vs Step 3.7 Flash
- Nanbeige4.2-3B vs Qwen3.5-397B-A17B
- Nanbeige4.2-3B vs Step 3.7 Flash
- Nanbeige4.3-33B vs Qwen3.5-397B-A17B
- Nanbeige4.3-33B vs Step 3.7 Flash
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.5-397B-A17B
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Step 3.7 Flash
- Nemotron 3.5 Lightning 30B A3B vs Qwen3.5-397B-A17B
- Nemotron 3.5 Lightning 30B A3B vs Step 3.7 Flash
- Nemotron 3 Nano 30B A3B vs Qwen3.5-397B-A17B
- Nemotron 3 Nano 30B A3B vs Step 3.7 Flash
- Nemotron 3 Nano 4B vs Qwen3.5-397B-A17B
- Nemotron 3 Nano 4B vs Step 3.7 Flash
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3.5-397B-A17B
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Step 3.7 Flash
- Nemotron 3 Super 120B A12B vs Qwen3.5-397B-A17B
- Nemotron 3 Super 120B A12B vs Step 3.7 Flash
- Nemotron 3 Ultra 550B A55B vs Qwen3.5-397B-A17B
- Nemotron 3 Ultra 550B A55B vs Step 3.7 Flash
- North Mini Code 1.0 vs Qwen3.5-397B-A17B
- North Mini Code 1.0 vs Step 3.7 Flash
- occamy-1.0 vs Qwen3.5-397B-A17B
- occamy-1.0 vs Step 3.7 Flash
- Olmo 3 7B Think vs Qwen3.5-397B-A17B
- Olmo 3 7B Think vs Step 3.7 Flash
- Ornith-1.5-122B vs Qwen3.5-397B-A17B
- Ornith-1.5-122B vs Step 3.7 Flash
- Ornith-1.5-35B-A3B vs Qwen3.5-397B-A17B
- Ornith-1.5-35B-A3B vs Step 3.7 Flash
- Ornith-1.5-397B vs Qwen3.5-397B-A17B
- Ornith-1.5-397B vs Step 3.7 Flash
- Ornith-1.5-9B vs Qwen3.5-397B-A17B
- Ornith-1.5-9B vs Step 3.7 Flash
- OTel-2.0-LLM-31B-IT vs Qwen3.5-397B-A17B
- OTel-2.0-LLM-31B-IT vs Step 3.7 Flash
- Qwen-AgentWorld-35B-A3B vs Qwen3.5-397B-A17B
- Qwen-AgentWorld-35B-A3B vs Step 3.7 Flash
- Qwen3-30B-A3B-Instruct-2507 vs Qwen3.5-397B-A17B
- Qwen3-30B-A3B-Instruct-2507 vs Step 3.7 Flash
- Qwen3-4B-Instruct-2507 vs Qwen3.5-397B-A17B
- Qwen3-4B-Instruct-2507 vs Step 3.7 Flash
- Qwen3.5-0.8B vs Qwen3.5-397B-A17B
- Qwen3.5-0.8B vs Step 3.7 Flash
- Qwen3.5-122B-A10B vs Qwen3.5-397B-A17B
- Qwen3.5-122B-A10B vs Step 3.7 Flash
- Qwen3.5-2B vs Qwen3.5-397B-A17B
- Qwen3.5-2B vs Step 3.7 Flash
- Qwen3.5-397B-A17B vs Qwen3.5-4B
- Qwen3.5-397B-A17B vs Qwen3.5-9B
- Qwen3.5-397B-A17B vs Qwen3.6-27B
- Qwen3.5-397B-A17B vs Qwen3.6-35B-A3B
- Qwen3.5-397B-A17B vs Qwen3.8-27B
- Qwen3.5-397B-A17B vs Qwen3.8-Flash-Next
- Qwen3.5-397B-A17B vs Qwen3-Coder-30B-A3B-Instruct
- Qwen3.5-397B-A17B vs Qwen3-Coder-Next
- Qwen3.5-397B-A17B vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3.5-397B-A17B vs Qwen3-VL-2B-Instruct
- Qwen3.5-397B-A17B vs Qwen3-VL-30B-A3B-Instruct
- Qwen3.5-397B-A17B vs Qwen3-VL-32B-Instruct
- Qwen3.5-397B-A17B vs Qwen3-VL-4B-Instruct
- Qwen3.5-397B-A17B vs Qwen3-VL-8B-Instruct
- Qwen3.5-397B-A17B vs Qwopus3.8-27B-Flash-V2
- Qwen3.5-397B-A17B vs Recursive Flint
- Qwen3.5-397B-A17B vs Ripple-65B Preview
- Qwen3.5-397B-A17B vs SenseNova-Loop-9B-Preview
- Qwen3.5-397B-A17B vs SmolLM3-3B
- Qwen3.5-397B-A17B vs Spark-X2.6-4B
- Qwen3.5-397B-A17B vs Swift 1.5 Qwen3.8-27B
- Qwen3.5-397B-A17B vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3.5-397B-A17B vs ThinkingCap (Qwen3.8 27B)
- Qwen3.5-397B-A17B vs Baidu Unlimited-OCR-2
- Qwen3.5-397B-A17B vs WeVisOne-VL-2B
- Qwen3.5-397B-A17B vs WeVisOne-VL-4B
- Qwen3.5-397B-A17B vs ZAYA1-8B
- Qwen3.5-4B vs Step 3.7 Flash
- Qwen3.5-9B vs Step 3.7 Flash
- Qwen3.6-27B vs Step 3.7 Flash
- Qwen3.6-35B-A3B vs Step 3.7 Flash
- Qwen3.8-27B vs Step 3.7 Flash
- Qwen3.8-Flash-Next vs Step 3.7 Flash
- Qwen3-Coder-30B-A3B-Instruct vs Step 3.7 Flash
- Qwen3-Coder-Next vs Step 3.7 Flash
- Qwen3-Omni-30B-A3B-Instruct vs Step 3.7 Flash
- Qwen3-VL-2B-Instruct vs Step 3.7 Flash
- Qwen3-VL-30B-A3B-Instruct vs Step 3.7 Flash
- Qwen3-VL-32B-Instruct vs Step 3.7 Flash
- Qwen3-VL-4B-Instruct vs Step 3.7 Flash
- Qwen3-VL-8B-Instruct vs Step 3.7 Flash
- Qwopus3.8-27B-Flash-V2 vs Step 3.7 Flash
- Recursive Flint vs Step 3.7 Flash
- Ripple-65B Preview vs Step 3.7 Flash
- SenseNova-Loop-9B-Preview vs Step 3.7 Flash
- SmolLM3-3B vs Step 3.7 Flash
- Spark-X2.6-4B vs Step 3.7 Flash
- Step 3.7 Flash vs Swift 1.5 Qwen3.8-27B
- Step 3.7 Flash vs Swift 1.5 Qwen3.8-Flash-Next
- Step 3.7 Flash vs ThinkingCap (Qwen3.8 27B)
- Step 3.7 Flash vs Baidu Unlimited-OCR-2
- Step 3.7 Flash vs WeVisOne-VL-2B
- Step 3.7 Flash vs WeVisOne-VL-4B
- Step 3.7 Flash vs ZAYA1-8B
Machine-readable
- This page as Markdown
/compare/qwen3-5-397b-a17b-vs-step-3-7-flash.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/qwen3-5-397b-a17b.json·/api/v1/tools/step-3-7-flash.json - From a terminal
anchor compare qwen3-5-397b-a17b step-3-7-flash(the CLI) - Over MCP
compare_tools {"a": "qwen3-5-397b-a17b", "b": "step-3-7-flash"}at/mcp, no key