Head to head · Open-weight models you run yourself · October 2026 research run
Qwen3-VL-8B-Instruct vs Step 3.7 Flash
Step 3.7 Flash scores 59.8 (C) on agent readiness against Qwen3-VL-8B-Instruct's 54.9 (C), and leads in 2 of 4 scored categories. Qwen3-VL-8B-Instruct leads on reach and risk. 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 An agent with 8 GB or more to spare that needs a local model to read images, documents and video
Ahead on
- Reach, 100 against 70
- Risk, 55 against 45
Watch for
No safety evaluation, agent-use guidance or security contact
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 60
- Documentation and upkeep, 39 against 31
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.
Reach Qwen3-VL-8B-Instruct +30
Qwen3-VL-8B-Instruct 100
Alibaba's own Q4_K_M GGUF is 5.03 GB, so with a fifth added it needs about 6 GB and fits in 16 GB (30). Transformers 4.57.0, vLLM, SGLang and llama.cpp run it in released versions, and Ollama v0.40.14 pins llama.cpp b11963, which has the qwen3vl architecture, so Ollama is credited through its pin; five runtimes reach the 25 cap. Alibaba publishes FP8 and GGUF builds at F16, Q8_0 and Q4_K_M (15). Apache 2.0 with nothing added (15). No gate and no account (10). Featherless is live in the Hub's provider mapping on 4 November, and Parasail served it per the 21 October check, not re-read, so it's hosted too (5). Novita's mapping still shows an error and isn't counted. 30+25+15+15+10+5 = 100.
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.
Agent interface Step 3.7 Flash +25
Qwen3-VL-8B-Instruct 60
A Jinja chat template in the repository (20). The tool-call format lives only in the template, Hermes-style JSON in tool_call tags, and Alibaba names no parser (10). Trained for function calling with several calls allowed in a turn (20). No documented structured output (0). No thinking mode in this edition, so reasoning control scores 0 by the rule. 262,144 tokens of native context (10). 20+10+20+0+0+10 = 60.
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-VL-8B-Instruct +10
Qwen3-VL-8B-Instruct 55
Safetensors and GGUF only and no code in the repositories, so the FP8 card's trust_remote_code flag runs nothing (25). Files in the Qwen organisation with a checksum on every file (15). No safety evaluation at all (0). No guidance for agent use, which matters for a model sold as a GUI agent (0). Apache 2.0 carries none of the licence risks (15). No security contact, disclosure policy or public advisories found for Qwen (0), and Alibaba's group-wide security centre wasn't checked. 25+15+0+0+15+0 = 55.
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 +8
Qwen3-VL-8B-Instruct 31
The card misses limitations, languages and a cut-off (11). Training data is described only in the technical report, with no cut-off (5). The last change to code that runs it was the qwen-vl-utils fix merged on 4 January 2026, and the weights haven't changed since November 2025; the Hub commits for all three repositories and the QwenLM/Qwen3-VL head (30 January 2026) were the same on 4 November (5). Of 34 Hugging Face discussions on 4 November, the 19 that aren't pull requests (newest 23 August 2026) have no reply from Qwen, and GitHub issues weren't counted (0). Earlier versions kept in their own repositories without notes (5). Two reports wait without a reply (5). 11+5+5+0+5+5 = 31.
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-VL-8B-Instruct | Step 3.7 Flash | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 100 | 70 | Qwen3-VL-8B-Instruct +30 |
| Risk | 20%20 | 55 | 45 | Qwen3-VL-8B-Instruct +10 |
| Agent interface | 10%10 | 60 | 85 | Step 3.7 Flash +25 |
| Documentation and upkeep | 10%10 | 31 | 39 | Step 3.7 Flash +8 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 54.9/100 | C 59.8/100 |
Facts side by side
| Fact | Qwen3-VL-8B-Instruct | 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 | 2025-11-01 | 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 |
Verdicts
Qwen3-VL-8B-Instruct
Qwen3-VL-8B-Instruct reads screenshots, documents, charts and video, and Alibaba's Q4_K_M GGUF needs about 6 GB. Transformers, vLLM, SGLang, llama.cpp and Ollama run it under plain Apache 2.0. It has no thinking mode, documents tool calls only in the template, and has no published safety work, so add guardrails before it drives a GUI.
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-VL-8B-Instruct
- Start it with
llama-server -hf Qwen/Qwen3-VL-8B-Instruct-GGUF:Q4_K_M --jinja -c 32768, orollama run qwen3-vl:8b-instruct - In Ollama the plain
qwen3-vl:8btag is the Thinking edition, so name8b-instruct - With the GGUF, load the mmproj vision file too, or images won't be read
- Parse tool calls as JSON inside
<tool_call>tags, or use SGLang's--tool-call-parser qwen - Use the card's sampling,
temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5for vision andtemperature=1.0, top_p=1.0, top_k=40, presence_penalty=2.0for text - Validate JSON output yourself, since structured output isn't documented
- vLLM needs 0.11.0 or later and Transformers 4.57.0 or later
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-VL-8B-Instruct or Step 3.7 Flash?
Step 3.7 Flash scores 59.8 (C) on agent readiness against Qwen3-VL-8B-Instruct's 54.9 (C), and leads in 2 of 4 scored categories. Qwen3-VL-8B-Instruct leads on reach and risk.
Other comparisons with Qwen3-VL-8B-Instruct or Step 3.7 Flash
- Agents-A1-4B vs Qwen3-VL-8B-Instruct
- Agents-A1-4B vs Step 3.7 Flash
- Apertus v1.5 8B vs Qwen3-VL-8B-Instruct
- Apertus v1.5 8B vs Step 3.7 Flash
- Apodex-1.1-mini vs Qwen3-VL-8B-Instruct
- Apodex-1.1-mini vs Step 3.7 Flash
- Arete-27B vs Qwen3-VL-8B-Instruct
- Arete-27B vs Step 3.7 Flash
- BigBang-v1 vs Qwen3-VL-8B-Instruct
- BigBang-v1 vs Step 3.7 Flash
- Carbon-X vs Qwen3-VL-8B-Instruct
- Carbon-X vs Step 3.7 Flash
- Cola2 vs Qwen3-VL-8B-Instruct
- Cola2 vs Step 3.7 Flash
- Cosmos-Reason2-2B vs Qwen3-VL-8B-Instruct
- Cosmos-Reason2-2B vs Step 3.7 Flash
- Darwin-36B-Opus vs Qwen3-VL-8B-Instruct
- Darwin-36B-Opus vs Step 3.7 Flash
- DeepSeek-V3.2 vs Qwen3-VL-8B-Instruct
- DeepSeek-V3.2 vs Step 3.7 Flash
- DeepSeek V4.1 Flash vs Qwen3-VL-8B-Instruct
- DeepSeek V4.1 Flash vs Step 3.7 Flash
- DeepSeek-V4-Flash-0731 vs Qwen3-VL-8B-Instruct
- DeepSeek-V4-Flash-0731 vs Step 3.7 Flash
- Devstral Small 2 vs Qwen3-VL-8B-Instruct
- Devstral Small 2 vs Step 3.7 Flash
- DiffusionGemma 26B A4B IT vs Qwen3-VL-8B-Instruct
- DiffusionGemma 26B A4B IT vs Step 3.7 Flash
- EXAONE 4.5 33B vs Qwen3-VL-8B-Instruct
- EXAONE 4.5 33B vs Step 3.7 Flash
- Gemma 4 12B IT vs Qwen3-VL-8B-Instruct
- Gemma 4 12B IT vs Step 3.7 Flash
- Gemma 4 26B A4B IT vs Qwen3-VL-8B-Instruct
- Gemma 4 26B A4B IT vs Step 3.7 Flash
- Gemma 4 31B IT vs Qwen3-VL-8B-Instruct
- Gemma 4 31B IT vs Step 3.7 Flash
- Gemma 4 E2B IT vs Qwen3-VL-8B-Instruct
- Gemma 4 E2B IT vs Step 3.7 Flash
- Gemma 4 E4B IT vs Qwen3-VL-8B-Instruct
- Gemma 4 E4B IT vs Step 3.7 Flash
- GLM-4.5-Air vs Qwen3-VL-8B-Instruct
- GLM-4.5-Air vs Step 3.7 Flash
- GLM-4.7-Flash vs Qwen3-VL-8B-Instruct
- GLM-4.7-Flash vs Step 3.7 Flash
- GLM-5.3-Flash vs Qwen3-VL-8B-Instruct
- GLM-5.3-Flash vs Step 3.7 Flash
- GLM-5.3 vs Qwen3-VL-8B-Instruct
- GLM-5.3 vs Step 3.7 Flash
- gpt-oss-120b vs Qwen3-VL-8B-Instruct
- gpt-oss-120b vs Step 3.7 Flash
- gpt-oss-20b vs Qwen3-VL-8B-Instruct
- gpt-oss-20b vs Step 3.7 Flash
- Granite 4.2 30B vs Qwen3-VL-8B-Instruct
- Granite 4.2 30B vs Step 3.7 Flash
- Granite 4.2 3B vs Qwen3-VL-8B-Instruct
- Granite 4.2 3B vs Step 3.7 Flash
- Granite 4.2 8B vs Qwen3-VL-8B-Instruct
- Granite 4.2 8B vs Step 3.7 Flash
- Hy-Memory-30B-A3B vs Qwen3-VL-8B-Instruct
- Hy-Memory-30B-A3B vs Step 3.7 Flash
- Hy3 vs Qwen3-VL-8B-Instruct
- Hy3 vs Step 3.7 Flash
- Inkling-Small vs Qwen3-VL-8B-Instruct
- Inkling-Small vs Step 3.7 Flash
- Inkling vs Qwen3-VL-8B-Instruct
- Inkling vs Step 3.7 Flash
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3-VL-8B-Instruct
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Step 3.7 Flash
- Kimi K3.1 Pro vs Qwen3-VL-8B-Instruct
- Kimi K3.1 Pro vs Step 3.7 Flash
- Kimi K3 vs Qwen3-VL-8B-Instruct
- Kimi K3 vs Step 3.7 Flash
- Kolibri-1 vs Qwen3-VL-8B-Instruct
- Kolibri-1 vs Step 3.7 Flash
- Kronos vs Qwen3-VL-8B-Instruct
- Kronos vs Step 3.7 Flash
- Laguna M.1 vs Qwen3-VL-8B-Instruct
- Laguna M.1 vs Step 3.7 Flash
- Laguna S 2.1 vs Qwen3-VL-8B-Instruct
- Laguna S 2.1 vs Step 3.7 Flash
- Laguna XS 2.1 vs Qwen3-VL-8B-Instruct
- Laguna XS 2.1 vs Step 3.7 Flash
- LBS-27B vs Qwen3-VL-8B-Instruct
- LBS-27B vs Step 3.7 Flash
- LFM2.5-2.6B vs Qwen3-VL-8B-Instruct
- LFM2.5-2.6B vs Step 3.7 Flash
- LFM3-1.7B vs Qwen3-VL-8B-Instruct
- LFM3-1.7B vs Step 3.7 Flash
- LFM3-Audio-2B vs Qwen3-VL-8B-Instruct
- LFM3-Audio-2B vs Step 3.7 Flash
- LFM3-Omni-2B vs Qwen3-VL-8B-Instruct
- LFM3-Omni-2B vs Step 3.7 Flash
- LFM3-VL-2B vs Qwen3-VL-8B-Instruct
- LFM3-VL-2B vs Step 3.7 Flash
- Ling-3.0-tiny vs Qwen3-VL-8B-Instruct
- Ling-3.0-tiny vs Step 3.7 Flash
- Ling-3.1-flash-1019 vs Qwen3-VL-8B-Instruct
- Ling-3.1-flash-1019 vs Step 3.7 Flash
- LongCat-2.5 vs Qwen3-VL-8B-Instruct
- LongCat-2.5 vs Step 3.7 Flash
- MiMo-V2.6 Distill Qwen 9B vs Qwen3-VL-8B-Instruct
- MiMo-V2.6 Distill Qwen 9B vs Step 3.7 Flash
- MiMo-V2.6-Flash-RL vs Qwen3-VL-8B-Instruct
- MiMo-V2.6-Flash-RL vs Step 3.7 Flash
- MiniCPM-o 4.5 vs Qwen3-VL-8B-Instruct
- MiniCPM-o 4.5 vs Step 3.7 Flash
- MiniCPM-V 4.7 1B vs Qwen3-VL-8B-Instruct
- MiniCPM-V 4.7 1B vs Step 3.7 Flash
- MiniCPM-V 4.7 35B-A3B vs Qwen3-VL-8B-Instruct
- MiniCPM-V 4.7 35B-A3B vs Step 3.7 Flash
- MiniCPM5-2B vs Qwen3-VL-8B-Instruct
- MiniCPM5-2B vs Step 3.7 Flash
- MiniMax-M2.5 vs Qwen3-VL-8B-Instruct
- MiniMax-M2.5 vs Step 3.7 Flash
- MiniMax-M2.7 vs Qwen3-VL-8B-Instruct
- MiniMax-M2.7 vs Step 3.7 Flash
- MiniMax-M3.1-Flash vs Qwen3-VL-8B-Instruct
- MiniMax-M3.1-Flash vs Step 3.7 Flash
- Mistral Large 4 vs Qwen3-VL-8B-Instruct
- Mistral Large 4 vs Step 3.7 Flash
- Muse Glimmer 30B vs Qwen3-VL-8B-Instruct
- Muse Glimmer 30B vs Step 3.7 Flash
- Nanbeige4.2-3B vs Qwen3-VL-8B-Instruct
- Nanbeige4.2-3B vs Step 3.7 Flash
- Nanbeige4.3-33B vs Qwen3-VL-8B-Instruct
- Nanbeige4.3-33B vs Step 3.7 Flash
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-VL-8B-Instruct
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Step 3.7 Flash
- Nemotron 3.5 Lightning 30B A3B vs Qwen3-VL-8B-Instruct
- Nemotron 3.5 Lightning 30B A3B vs Step 3.7 Flash
- Nemotron 3 Nano 30B A3B vs Qwen3-VL-8B-Instruct
- Nemotron 3 Nano 30B A3B vs Step 3.7 Flash
- Nemotron 3 Nano 4B vs Qwen3-VL-8B-Instruct
- Nemotron 3 Nano 4B vs Step 3.7 Flash
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3-VL-8B-Instruct
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Step 3.7 Flash
- Nemotron 3 Super 120B A12B vs Qwen3-VL-8B-Instruct
- Nemotron 3 Super 120B A12B vs Step 3.7 Flash
- Nemotron 3 Ultra 550B A55B vs Qwen3-VL-8B-Instruct
- Nemotron 3 Ultra 550B A55B vs Step 3.7 Flash
- North Mini Code 1.0 vs Qwen3-VL-8B-Instruct
- North Mini Code 1.0 vs Step 3.7 Flash
- occamy-1.0 vs Qwen3-VL-8B-Instruct
- occamy-1.0 vs Step 3.7 Flash
- Olmo 3 7B Think vs Qwen3-VL-8B-Instruct
- Olmo 3 7B Think vs Step 3.7 Flash
- Ornith-1.5-122B vs Qwen3-VL-8B-Instruct
- Ornith-1.5-122B vs Step 3.7 Flash
- Ornith-1.5-35B-A3B vs Qwen3-VL-8B-Instruct
- Ornith-1.5-35B-A3B vs Step 3.7 Flash
- Ornith-1.5-397B vs Qwen3-VL-8B-Instruct
- Ornith-1.5-397B vs Step 3.7 Flash
- Ornith-1.5-9B vs Qwen3-VL-8B-Instruct
- Ornith-1.5-9B vs Step 3.7 Flash
- OTel-2.0-LLM-31B-IT vs Qwen3-VL-8B-Instruct
- OTel-2.0-LLM-31B-IT vs Step 3.7 Flash
- Qwen-AgentWorld-35B-A3B vs Qwen3-VL-8B-Instruct
- Qwen-AgentWorld-35B-A3B vs Step 3.7 Flash
- Qwen3-30B-A3B-Instruct-2507 vs Qwen3-VL-8B-Instruct
- Qwen3-30B-A3B-Instruct-2507 vs Step 3.7 Flash
- Qwen3-4B-Instruct-2507 vs Qwen3-VL-8B-Instruct
- Qwen3-4B-Instruct-2507 vs Step 3.7 Flash
- Qwen3.5-0.8B vs Qwen3-VL-8B-Instruct
- Qwen3.5-0.8B vs Step 3.7 Flash
- Qwen3.5-122B-A10B vs Qwen3-VL-8B-Instruct
- Qwen3.5-122B-A10B vs Step 3.7 Flash
- Qwen3.5-2B vs Qwen3-VL-8B-Instruct
- Qwen3.5-2B vs Step 3.7 Flash
- Qwen3.5-397B-A17B vs Qwen3-VL-8B-Instruct
- Qwen3.5-397B-A17B vs Step 3.7 Flash
- Qwen3.5-4B vs Qwen3-VL-8B-Instruct
- Qwen3.5-4B vs Step 3.7 Flash
- Qwen3.5-9B vs Qwen3-VL-8B-Instruct
- Qwen3.5-9B vs Step 3.7 Flash
- Qwen3.6-27B vs Qwen3-VL-8B-Instruct
- Qwen3.6-27B vs Step 3.7 Flash
- Qwen3.6-35B-A3B vs Qwen3-VL-8B-Instruct
- Qwen3.6-35B-A3B vs Step 3.7 Flash
- Qwen3.8-27B vs Qwen3-VL-8B-Instruct
- Qwen3.8-27B vs Step 3.7 Flash
- Qwen3.8-Flash-Next vs Qwen3-VL-8B-Instruct
- Qwen3.8-Flash-Next vs Step 3.7 Flash
- Qwen3-Coder-30B-A3B-Instruct vs Qwen3-VL-8B-Instruct
- Qwen3-Coder-30B-A3B-Instruct vs Step 3.7 Flash
- Qwen3-Coder-Next vs Qwen3-VL-8B-Instruct
- Qwen3-Coder-Next vs Step 3.7 Flash
- Qwen3-Omni-30B-A3B-Instruct vs Qwen3-VL-8B-Instruct
- Qwen3-Omni-30B-A3B-Instruct vs Step 3.7 Flash
- Qwen3-VL-2B-Instruct vs Qwen3-VL-8B-Instruct
- Qwen3-VL-2B-Instruct vs Step 3.7 Flash
- Qwen3-VL-30B-A3B-Instruct vs Qwen3-VL-8B-Instruct
- Qwen3-VL-30B-A3B-Instruct vs Step 3.7 Flash
- Qwen3-VL-32B-Instruct vs Qwen3-VL-8B-Instruct
- Qwen3-VL-32B-Instruct vs Step 3.7 Flash
- Qwen3-VL-4B-Instruct vs Qwen3-VL-8B-Instruct
- Qwen3-VL-4B-Instruct vs Step 3.7 Flash
- Qwen3-VL-8B-Instruct vs Qwopus3.8-27B-Flash-V2
- Qwen3-VL-8B-Instruct vs Recursive Flint
- Qwen3-VL-8B-Instruct vs Ripple-65B Preview
- Qwen3-VL-8B-Instruct vs SenseNova-Loop-9B-Preview
- Qwen3-VL-8B-Instruct vs SmolLM3-3B
- Qwen3-VL-8B-Instruct vs Spark-X2.6-4B
- Qwen3-VL-8B-Instruct vs Swift 1.5 Qwen3.8-27B
- Qwen3-VL-8B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3-VL-8B-Instruct vs ThinkingCap (Qwen3.8 27B)
- Qwen3-VL-8B-Instruct vs Baidu Unlimited-OCR-2
- Qwen3-VL-8B-Instruct vs WeVisOne-VL-2B
- Qwen3-VL-8B-Instruct vs WeVisOne-VL-4B
- Qwen3-VL-8B-Instruct vs ZAYA1-8B
- 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-vl-8b-instruct-vs-step-3-7-flash.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/qwen3-vl-8b-instruct.json·/api/v1/tools/step-3-7-flash.json - From a terminal
anchor compare qwen3-vl-8b-instruct step-3-7-flash(the CLI) - Over MCP
compare_tools {"a": "qwen3-vl-8b-instruct", "b": "step-3-7-flash"}at/mcp, no key