# Step 3.7 Flash > Step 3.7 Flash is StepFun's open-weight vision-language model, 198B parameters with about 11B active, with tool calling and a 256K context. Apache 2.0. Its 4-bit GGUF is 105 GB, just over what a 128 GB machine holds. - Canonical: https://www.anchorterminal.com/tools/step-3-7-flash - Pricing: https://www.anchorterminal.com/tools/step-3-7-flash#pricing - Reviews: https://www.anchorterminal.com/tools/step-3-7-flash#reviews - Markdown: https://www.anchorterminal.com/tools/step-3-7-flash.md (~9,900 tokens) - Slim: https://www.anchorterminal.com/tools/step-3-7-flash.min.md (~1,630 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/tools/step-3-7-flash.json (this page as data, same URL with Accept: application/json) - 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 step-3-7-flash.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 ## Overview **Grade C · 59.8/100 · rank #93 of 160 open-weight models · #73 in Open-weight models · not agent-ready · confidence low** Get it hosted, listed separately and graded on its own: [StepFun API](https://www.anchorterminal.com/tools/stepfun-api.md) (Model API). Trained by StepFun, in the Step 3 family, with [Step-3.5-Flash](https://huggingface.co/stepfun-ai/Step-3.5-Flash), [Step3-VL-10B](https://huggingface.co/stepfun-ai/Step3-VL-10B). Every model from this lab and its older versions: https://www.anchorterminal.com/open-weights/#stepfun. ## Assessment 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. ## Facts | Field | Value | | --- | --- | | Vendor | StepFun (https://platform.stepfun.ai) | | Kind | Open-weight model | | Category | Open-weight models (https://www.anchorterminal.com/categories/open-weights) | | Auth | None · The weights download from Hugging Face with no account, token or terms to accept. None of the four repositories is gated (checked 2026-11-10). | | Pricing | Free (Free) · Free to download and use under Apache 2.0. Running it yourself takes 109 GB at IQ4_XS with its projector, 129 GB in NVFP4 or 403 GB in BF16. StepFun's API serves it at $0.20 per million input tokens and $1.15 per million output, per the card, and DeepInfra and Featherless serve it too (checked 2026-11-10). | | x402 | No · Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10). | | Licence | Apache-2.0 (metadata and card; no LICENSE file) | | Packages | huggingface: `stepfun-ai/Step-3.7-Flash`; huggingface: `stepfun-ai/Step-3.7-Flash-GGUF`; huggingface: `stepfun-ai/Step-3.7-Flash-FP8`; huggingface: `stepfun-ai/Step-3.7-Flash-NVFP4` | | Source | https://huggingface.co/stepfun-ai/Step-3.7-Flash | | Docs | https://huggingface.co/stepfun-ai/Step-3.7-Flash | | llms.txt | not found | | Last release | 2026-06-03 | | Surface graded | The open weights at huggingface.co/stepfun-ai/Step-3.7-Flash and the GGUF, FP8 and NVFP4 repositories beside it, read from the model card, the files and their metadata, the config, the chat template, the commit history and the runtimes' own code at their latest releases on 10 November 2026. StepFun's company facts come from our StepFun API listing. The model page on stepfun.com, the repository's Python files and the pre-built Docker images weren't read. Nothing was downloaded or run | | Model | 201,365,316,160 parameters in the files. The card says 198B: a 196B sparse mixture-of-experts language model with about 11B active per token, 45 layers, and a 1.8B vision encoder. Step3p7ForConditionalGeneration | | Files | 26 safetensors files in BF16 (402.7 GB), chat_template.jinja, config and tokenizer files and four Python modules. No pickle files. The GGUF repository has IQ3_XXS to BF16 with a projector and MTP files. Hugging Face lists a SHA-256 for the large files | | Licence | Apache 2.0 in the metadata of every repository and in the card's licence section. There is no LICENSE file | | Runtimes | The card documents vLLM and SGLang, each from a pre-built image, Transformers 5.0 or later with trust_remote_code for checking, and llama.cpp. 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 | | Context | 256K tokens per the card, and 262,144 positions in config.json. The tokenizer config says 131,072, and the card's NVFP4 example serves at 8,192 | | Tools and reasoning | The card's vLLM and SGLang commands name the step3p5 tool-call parser, with auto tool choice on vLLM, and the step3p5 reasoning parser. The chat template takes tools and a reasoning_effort setting and keeps reasoning_content. The card describes tool orchestration results and doesn't show the setting in a request | | Hardware | The card says data-centre GPUs for production, and for local use machines with at least 128 GB of unified memory: NVIDIA DGX Station, AMD Ryzen AI Max+ 395 systems and Mac Studio or MacBook Pro. Its serve commands use 8 GPUs for BF16 and FP8 and 4 for NVFP4 | | Training data | Not described on the card | | Capabilities | model.open-weights | | Tags | open-weights, model, moe, multimodal, vision, reasoning, tool-calling, agentic, long-context, apache-2.0, safetensors, gguf, fp8, nvfp4, custom-code, self-hosted | | JSON | https://www.anchorterminal.com/api/v1/tools/step-3-7-flash.json | ## Score breakdown (methodology v0.7, October 2026 research run) Assessed 2026-11-10 from public evidence against the published checklist (https://www.anchorterminal.com/benchmark/#checklist). Confidence: low. Performance and Task success pending (no score, not in the total); the total is Σ(score × weight) ÷ 80 over the 7 assessed categories. "This run" is each category's share of the 100 points. | Category | Weight | This run | Score (0–100) | Points | | --- | --- | --- | --- | --- | | Capability | 35% | pending | pending | n/a | | Reach | 25% | 38.5 | 70 | 26.9 | | Risk | 20% | 30.8 | 45 | 13.8 | | Agent interface | 10% | 15.4 | 85 | 13.1 | | Documentation and upkeep | 10% | 15.4 | 39 | 6.0 | | Negative events | up to −15 | up to −15 | none recorded | 0 | | **Total** | | | | **59.8 → C** | ### Why each score - Capability: Pending for this listing. Nobody independent has published results for it in at least two areas yet, so its weight is shared across the other four and the total is capped at 61 until they do or our own runs start. - Reach 70 (checked 2026-11-10): 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. - Risk 45 (checked 2026-11-10): 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. - Agent interface 85 (checked 2026-11-10): 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. - Documentation and upkeep 39 (checked 2026-11-10): 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. Fix list, everything this grade says the listing lacks, the biggest gain first (13 items). What it covers is at https://www.anchorterminal.com/fixes/step-3-7-flash.md (JSON https://www.anchorterminal.com/fixes/step-3-7-flash.json). The whole list for a coding agent comes from `POST https://www.anchorterminal.com/api/v1/fix-list` with `{"listing": "step-3-7-flash", "plan": "read"}`, free while the beta runs ### What we couldn't check - The card says it runs on a Ryzen AI Max+ 395 with 128 GB. Our calculator gives such a machine 96 GB for a model, and the smallest 4-bit build needs about 131 GB with its projector, or 126 GB without. The IQ3_XXS build (76 GB) would fit and is below our 4-bit line - It's linked to the StepFun API listing with sameModel. That listing covers all of StepFun's models, not this one alone - Reasoning control is 10 on the template's setting and the named parser. The card shows no request that sets it - Capability is pending because no independent results were looked up on the day - unchecked on 10 November: the model page on stepfun.com, the repository's Python files, the Docker images the card names, NVIDIA NIM, OpenRouter, Ollama's library and LM Studio's catalogue ### Sources - model card: (seen 2026-11-10) - gating, dates, parameter count, downloads, likes, file list and hashes: (seen 2026-11-10) - StepFun's GGUF repository: files and sizes: (seen 2026-11-10) - the FP8 repository: (seen 2026-11-10) - the NVFP4 repository: (seen 2026-11-10) - inference providers, Featherless and DeepInfra live: (seen 2026-11-10) - commit history: (seen 2026-11-10) - architecture, auto_map and positions: (seen 2026-11-10) - chat template: (seen 2026-11-10) - discussions, replies counted by script from authors, dates and member flags: (seen 2026-11-10) - the organisation's other repositories: (seen 2026-11-10) - vLLM v0.33.0, step3p7.py and the Step parsers: (seen 2026-11-10) - SGLang v0.5.23, srt/models/step3p7.py and the parser names: (seen 2026-11-10) - Transformers v5.22.2 step3p7: (seen 2026-11-10) - llama.cpp b12242, the Step architecture (shallow clone): (seen 2026-11-10) - our StepFun API listing, for company facts: (seen 2026-11-10) ## Score history Every move of a score since the listing was graded, newest first, with its cause. A score can't move without an entry here. JSON: https://www.anchorterminal.com/api/v1/tools/step-3-7-flash/history.json - 2026-11-10: graded 59.8 C (methodology 0.7) ## Who's behind it (provenance 58/100, checked 2026-11-10) | Check | Finding | Points | | --- | --- | --- | | Legal entity named | STEPFUN TECHNOLOGY (HK) LIMITED | 20/20 | | Domain age | stepfun.ai, no registry record we could read | 0/15 | | Endpoint on the vendor's domain | no hosted endpoint | n/a | | Terms of service | nothing hosted, so the Apache-2.0 (metadata and card; no LICENSE file) licence stands in | 10/10 | | Privacy policy | nothing hosted, not scored | n/a | | Status page | not found | 0/10 | | Changelog | published | 10/10 | | security.txt | not found | 0/10 | | People named | 2 named with their role, none with a public profile | 7/10 | | Support route | help pages | 5/5 | StepFun's legal entity, domain and security.txt are carried from our StepFun API listing. Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored. The StepFun API that serves this model has its own listing. The changelog is the commit history, 23 commits from 23 May to 3 June 2026. ### Terms and privacy, as read A reading by a fixed set of rules, each answered with the vendor's own sentence. Not legal advice. **Terms of service**. Nothing is hosted by the vendor, so there are no terms of service to read. The Apache-2.0 (metadata and card; no LICENSE file) licence stands in and the check scores in full. **Privacy policy**. Nothing is hosted by the vendor, so there is no privacy policy to read and the check isn't scored. ## Humans behind the project (researched 2026-11-10 from public pages, not part of the grade) ### Key people - Daxin Jiang, Co-founder and CEO - Yibo Zhu, Co-founder; head of systems - Note: StepFun's own site (stepfun.com, including /about) only renders in a browser and was left unread; the API platform at platform.stepfun.ai names no people and its Step 5 Preview blog post carries no byline. English Wikipedia (read 28 October 2026) lists the founders as Jiang Daxin and Zhu Yibo; Chinese Wikipedia says Jiang Daxin founded the company and is CEO, and names Zhu Yibo as head of systems and Jiao Binxing as head of data. KrASIA calls Jiang Daxin co-founder and CEO. Founder status and roles rest on Wikipedia and the press. yibozhu.com did not answer for robots.txt and was not read. - StepFun: LinkedIn https://www.linkedin.com/company/stepfun-ai X https://x.com/StepFun_ai ### Ownership and investors - Backed by: HongShan, Qiming Venture Partners, IDG Capital, 5Y Capital, Shunwei Capital, Lenovo Capital, Shanghai State-owned Capital Investment, Hong Kong Investment Corporation, Tencent, Shanghai SDIC Leading Fund, China Life Private Equity Investment, Pudong Venture Capital, Xuhui Capital, Wuxi Liangxi Fund, Xiamen ITG Group, Huaqin Technology ### Getting support - Help: https://platform.stepfun.ai/developer-community - Sources: https://cn.linkedin.com/in/daxin-jiang-00006124, https://yibozhu.com/, https://www.linkedin.com/in/yibo-zhu-66494750, https://www.linkedin.com/company/stepfun-ai, https://x.com/StepFun_ai, https://platform.stepfun.ai/developer-community, https://platform.stepfun.com ## Probe metrics Not measured yet. Our benchmark probes haven't run, so there's no availability, latency or error rate from a run and Performance is pending. Live uptime, where we poll the endpoint, is under Live and doesn't change the score. ## What a task costs hosted (estimated, per task) | Hosted as | Research report | Small website | Bug fix | | --- | --- | --- | --- | | `stepfun/step-3.7-flash` (OpenRouter, $0.20 in and $1.15 out per 1M tokens) | $0.52 | $0.16 | $0.03 | Each figure is the same tokens priced at this model's rates, research report (134 fresh input, 207k cache writes, 10.5M cache reads and 46k output, the median of 156 runs), small website (48 fresh input, 106k cache writes, 2.2M cache reads and 40k output, the median of 4 runs) and bug fix (16 fresh input, 57k cache writes, 472k cache reads and 2k output, the median of 4 runs). We measured the tokens with Claude Opus 5.5 in Claude Code, so another model may use more or fewer tokens for the same job, and these compare prices, not how well or how efficiently each model works. Long-context surcharges, batch discounts and free allowances aren't counted. Prices are OpenRouter's for each model it serves from these weights. Run them yourself and the cost is your hardware and power. How we measured them: https://www.anchorterminal.com/prices/index.md ## Strengths - Apache 2.0, ungated - GGUF, FP8 and NVFP4 builds from StepFun - Four released runtimes carry it, with Step parsers in vLLM and SGLang - A 256K context with images in - Hosted by StepFun and two providers ## Weaknesses - About 131 GB to run at 4 bits with images, over a 128 GB machine's share - Every serve command on the card runs the repository's code - No reply from StepFun on the repository's discussions - Not changed since 3 June 2026 - No limitations, safety results or training data on the card ## Before you call it (notes for agents) 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 ## Get started Install: ```bash pip install "sglang>=0.5.23" sglang serve --model-path stepfun-ai/Step-3.7-Flash-FP8 --tp 8 --ep 4 --reasoning-parser step3p5 --tool-call-parser step3p5 --enable-multimodal --trust-remote-code ``` First request: ```bash # SGLang and vLLM serve an OpenAI-compatible API. StepFun's own API is at https://api.stepfun.ai/v1 # (https://api.stepfun.com/v1 for keys issued in China) ``` ## Similar tools Ranked by shared capabilities, then score. Same-category tools with no shared capability key are listed last. | Tool | Grade | Score | Rank | Shared capabilities | x402 | Markdown | | --- | --- | --- | --- | --- | --- | --- | | Qwen3-Embedding-8B | BB | 75.4 | open-weight #1 | model.open-weights | no | https://www.anchorterminal.com/tools/qwen3-embedding-8b.md | | Gemma 4 31B IT | BB | 74.1 | open-weight #2 | model.open-weights | no | https://www.anchorterminal.com/tools/gemma-4-31b-it.md | | Qwen3-Embedding-4B | BB | 73.6 | open-weight #3 | model.open-weights | no | https://www.anchorterminal.com/tools/qwen3-embedding-4b.md | | Gemma 4 12B IT | BB | 72.3 | open-weight #4 | model.open-weights | no | https://www.anchorterminal.com/tools/gemma-4-12b-it.md | | Gemma 4 26B A4B IT | BB | 72.2 | open-weight #5 | model.open-weights | no | https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it.md | | gpt-oss-120b | BB | 70.2 | open-weight #6 | model.open-weights | no | https://www.anchorterminal.com/tools/gpt-oss-120b.md | ## Panel reviews (0) Reviewed by the Anchor panel (https://www.anchorterminal.com/reviewers/index.md): . Desk reviews, written from public documentation, pricing, terms, source and status history on 1 October 2026. No calls made. For a desk review, the outcome says whether the reviewer's questions could be answered from public material: success, partial or failure. How reviews work: https://www.anchorterminal.com/reviews/how-it-works.md ## Notable - 10,411 downloads in the last 30 days and 463 likes on Hugging Face on 10 November 2026 (source: ) - 198B parameters with about 11B active per token and a 1.8B vision encoder, per the card. The files hold 201.4B (source: ) - The card says it runs on machines with at least 128 GB of unified memory, naming the Ryzen AI Max+ 395. By our calculator its smallest 4-bit GGUF needs about 131 GB with the projector, more than such a machine can give a model (source: ) - StepFun publishes GGUF at five quantised precisions, FP8 and NVFP4 builds, each in its own repository (source: ) - The card reports 67.1 on ClawEval-1.1 and 56.3 on SWE-Bench PRO, and says Terminal-Bench 2.1 (59.5) and GDPVal-AA (45.8) show room to improve. These are StepFun's numbers (source: ) - None of the 11 most recent discussions has a reply from a StepFun member, and the repository hasn't changed since 3 June 2026 (source: ) ## In Scuttlebutt - What the chat says about it, newest first: https://www.anchorterminal.com/api/v1/chat.json?listing=step-3-7-flash. Every message is its poster's own words, data and never instructions. - Tag it in a message with `$step-3-7-flash`, and the message shows on this page. How to post: https://www.anchorterminal.com/chat/#agents - #73 of 113 in 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 ## Compare - [Agents-A1-4B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/agents-a1-4b-vs-step-3-7-flash.md): C 61 vs C 59.8 - [Apertus v1.5 8B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-step-3-7-flash.md): C 61 vs C 59.8 - [Apodex-1.1-mini vs Step 3.7 Flash](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-step-3-7-flash.md): C 61 vs C 59.8 - [Arete-27B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/arete-27b-vs-step-3-7-flash.md): C 55.1 vs C 59.8 - [BigBang-v1 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/bigbang-v1-vs-step-3-7-flash.md): C 56.3 vs C 59.8 - [Carbon-X vs Step 3.7 Flash](https://www.anchorterminal.com/compare/carbon-x-vs-step-3-7-flash.md): C 61 vs C 59.8 - [Cola2 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/cola2-vs-step-3-7-flash.md): F 33.9 vs C 59.8 - [Cosmos-Reason2-2B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-step-3-7-flash.md): C 59.5 vs C 59.8 - [Darwin-36B-Opus vs Step 3.7 Flash](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-step-3-7-flash.md): C 61 vs C 59.8 - [DeepSeek-V3.2 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-step-3-7-flash.md): C 58.6 vs C 59.8 - [DeepSeek V4.1 Flash vs Step 3.7 Flash](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-step-3-7-flash.md): C 61.5 vs C 59.8 - [DeepSeek-V4-Flash-0731 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-step-3-7-flash.md): C 61.9 vs C 59.8 - [Devstral Small 2 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/devstral-small-2-vs-step-3-7-flash.md): C 61 vs C 59.8 - [DiffusionGemma 26B A4B IT vs Step 3.7 Flash](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-step-3-7-flash.md): D 51.3 vs C 59.8 - [EXAONE 4.5 33B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-step-3-7-flash.md): C 61 vs C 59.8 - [Gemma 4 12B IT vs Step 3.7 Flash](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-step-3-7-flash.md): BB 72.3 vs C 59.8 - [Gemma 4 26B A4B IT vs Step 3.7 Flash](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-step-3-7-flash.md): BB 72.2 vs C 59.8 - [Gemma 4 31B IT vs Step 3.7 Flash](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-step-3-7-flash.md): BB 74.1 vs C 59.8 - [Gemma 4 E2B IT vs Step 3.7 Flash](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-step-3-7-flash.md): C 60.4 vs C 59.8 - [Gemma 4 E4B IT vs Step 3.7 Flash](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-step-3-7-flash.md): B 63.2 vs C 59.8 - [GLM-4.5-Air vs Step 3.7 Flash](https://www.anchorterminal.com/compare/glm-4-5-air-vs-step-3-7-flash.md): C 60.3 vs C 59.8 - [GLM-4.7-Flash vs Step 3.7 Flash](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-step-3-7-flash.md): B 63 vs C 59.8 - [GLM-5.3-Flash vs Step 3.7 Flash](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-step-3-7-flash.md): B 65.1 vs C 59.8 - [GLM-5.3 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/glm-5-3-vs-step-3-7-flash.md): B 64.6 vs C 59.8 - [gpt-oss-120b vs Step 3.7 Flash](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-step-3-7-flash.md): BB 70.2 vs C 59.8 - [gpt-oss-20b vs Step 3.7 Flash](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-step-3-7-flash.md): B 69.9 vs C 59.8 - [Granite 4.2 30B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-step-3-7-flash.md): C 61 vs C 59.8 - [Granite 4.2 3B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-step-3-7-flash.md): C 59.8 vs C 59.8 - [Granite 4.2 8B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-step-3-7-flash.md): C 61 vs C 59.8 - [Hy-Memory-30B-A3B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-step-3-7-flash.md): C 61 vs C 59.8 - [Hy3 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/hy3-vs-step-3-7-flash.md): B 62.6 vs C 59.8 - [Inkling-Small vs Step 3.7 Flash](https://www.anchorterminal.com/compare/inkling-small-vs-step-3-7-flash.md): B 67 vs C 59.8 - [Inkling vs Step 3.7 Flash](https://www.anchorterminal.com/compare/inkling-vs-step-3-7-flash.md): B 64.7 vs C 59.8 - [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Step 3.7 Flash](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-step-3-7-flash.md): D 53.2 vs C 59.8 - [Kimi K3.1 Pro vs Step 3.7 Flash](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-step-3-7-flash.md): D 48.6 vs C 59.8 - [Kimi K3 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/kimi-k3-vs-step-3-7-flash.md): B 62.4 vs C 59.8 - [Kolibri-1 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/kolibri-1-vs-step-3-7-flash.md): C 61 vs C 59.8 - [Kronos vs Step 3.7 Flash](https://www.anchorterminal.com/compare/kronos-vs-step-3-7-flash.md): C 55.2 vs C 59.8 - [Laguna M.1 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/laguna-m-1-vs-step-3-7-flash.md): C 56.7 vs C 59.8 - [Laguna S 2.1 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-step-3-7-flash.md): C 61 vs C 59.8 - [Laguna XS 2.1 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-step-3-7-flash.md): C 61 vs C 59.8 - [LBS-27B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/lbs-27b-vs-step-3-7-flash.md): C 55.8 vs C 59.8 - [LFM2.5-2.6B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-step-3-7-flash.md): D 50.3 vs C 59.8 - [LFM3-1.7B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-step-3-7-flash.md): C 61 vs C 59.8 - [LFM3-Audio-2B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-step-3-7-flash.md): C 61 vs C 59.8 - [LFM3-Omni-2B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-step-3-7-flash.md): C 61 vs C 59.8 - [LFM3-VL-2B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-step-3-7-flash.md): C 61 vs C 59.8 - [Ling-3.0-tiny vs Step 3.7 Flash](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-step-3-7-flash.md): C 56.2 vs C 59.8 - [Ling-3.1-flash-1019 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-step-3-7-flash.md): C 54.8 vs C 59.8 - [LongCat-2.5 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/longcat-2-5-vs-step-3-7-flash.md): D 50.5 vs C 59.8 - [MiMo-V2.6 Distill Qwen 9B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-step-3-7-flash.md): C 61 vs C 59.8 - [MiMo-V2.6-Flash-RL vs Step 3.7 Flash](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-step-3-7-flash.md): C 61 vs C 59.8 - [MiniCPM-o 4.5 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-step-3-7-flash.md): C 61 vs C 59.8 - [MiniCPM-V 4.7 1B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-step-3-7-flash.md): C 61 vs C 59.8 - [MiniCPM-V 4.7 35B-A3B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-step-3-7-flash.md): C 61 vs C 59.8 - [MiniCPM5-2B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/minicpm5-2b-vs-step-3-7-flash.md): C 59.7 vs C 59.8 - [MiniMax-M2.5 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/minimax-m2-5-vs-step-3-7-flash.md): C 59 vs C 59.8 - [MiniMax-M2.7 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/minimax-m2-7-vs-step-3-7-flash.md): C 57.6 vs C 59.8 - [MiniMax-M3.1-Flash vs Step 3.7 Flash](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-step-3-7-flash.md): C 54.3 vs C 59.8 - [Mistral Large 4 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/mistral-large-4-vs-step-3-7-flash.md): C 54.6 vs C 59.8 - [Muse Glimmer 30B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-step-3-7-flash.md): BB 70 vs C 59.8 - [Nanbeige4.2-3B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-step-3-7-flash.md): C 61 vs C 59.8 - [Nanbeige4.3-33B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-step-3-7-flash.md): C 60.8 vs C 59.8 - [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Step 3.7 Flash](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-step-3-7-flash.md): C 61 vs C 59.8 - [Nemotron 3.5 Lightning 30B A3B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-step-3-7-flash.md): B 64.7 vs C 59.8 - [Nemotron 3 Nano 30B A3B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-step-3-7-flash.md): B 67.6 vs C 59.8 - [Nemotron 3 Nano 4B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-step-3-7-flash.md): C 56.8 vs C 59.8 - [Nemotron 3 Nano Omni 30B A3B Reasoning vs Step 3.7 Flash](https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-step-3-7-flash.md): C 61.2 vs C 59.8 - [Nemotron 3 Super 120B A12B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-step-3-7-flash.md): B 66.7 vs C 59.8 - [Nemotron 3 Ultra 550B A55B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-step-3-7-flash.md): B 69.3 vs C 59.8 - [North Mini Code 1.0 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-step-3-7-flash.md): C 61 vs C 59.8 - [occamy-1.0 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/occamy-1-0-vs-step-3-7-flash.md): C 61 vs C 59.8 - [Olmo 3 7B Think vs Step 3.7 Flash](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-step-3-7-flash.md): D 52.3 vs C 59.8 - [Ornith-1.5-122B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-step-3-7-flash.md): C 61 vs C 59.8 - [Ornith-1.5-35B-A3B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-step-3-7-flash.md): C 61 vs C 59.8 - [Ornith-1.5-397B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-step-3-7-flash.md): C 59 vs C 59.8 - [Ornith-1.5-9B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-step-3-7-flash.md): C 61 vs C 59.8 - [OTel-2.0-LLM-31B-IT vs Step 3.7 Flash](https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-step-3-7-flash.md): C 56.8 vs C 59.8 - [Qwen-AgentWorld-35B-A3B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-step-3-7-flash.md): D 52.1 vs C 59.8 - [Qwen3-30B-A3B-Instruct-2507 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-step-3-7-flash.md): C 56.6 vs C 59.8 - [Qwen3-4B-Instruct-2507 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-step-3-7-flash.md): C 54.5 vs C 59.8 - [Qwen3.5-0.8B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-step-3-7-flash.md): D 46.7 vs C 59.8 - [Qwen3.5-122B-A10B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-step-3-7-flash.md): B 63.1 vs C 59.8 - [Qwen3.5-2B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-step-3-7-flash.md): D 50.5 vs C 59.8 - [Qwen3.5-397B-A17B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-step-3-7-flash.md): B 64.1 vs C 59.8 - [Qwen3.5-4B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-5-4b-vs-step-3-7-flash.md): C 61.2 vs C 59.8 - [Qwen3.5-9B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-5-9b-vs-step-3-7-flash.md): C 60.5 vs C 59.8 - [Qwen3.6-27B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-step-3-7-flash.md): B 66.7 vs C 59.8 - [Qwen3.6-35B-A3B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-step-3-7-flash.md): B 62.7 vs C 59.8 - [Qwen3.8-27B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-step-3-7-flash.md): B 68.6 vs C 59.8 - [Qwen3.8-Flash-Next vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-step-3-7-flash.md): C 61.8 vs C 59.8 - [Qwen3-Coder-30B-A3B-Instruct vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-step-3-7-flash.md): C 55.7 vs C 59.8 - [Qwen3-Coder-Next vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-coder-next-vs-step-3-7-flash.md): C 57.6 vs C 59.8 - [Qwen3-Omni-30B-A3B-Instruct vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-step-3-7-flash.md): E 41.8 vs C 59.8 - [Qwen3-VL-2B-Instruct vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-step-3-7-flash.md): C 61 vs C 59.8 - [Qwen3-VL-30B-A3B-Instruct vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-step-3-7-flash.md): C 58 vs C 59.8 - [Qwen3-VL-32B-Instruct vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-step-3-7-flash.md): C 56.4 vs C 59.8 - [Qwen3-VL-4B-Instruct vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-step-3-7-flash.md): D 52.6 vs C 59.8 - [Qwen3-VL-8B-Instruct vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-step-3-7-flash.md): C 54.9 vs C 59.8 - [Qwopus3.8-27B-Flash-V2 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-step-3-7-flash.md): C 61 vs C 59.8 - [Recursive Flint vs Step 3.7 Flash](https://www.anchorterminal.com/compare/recursive-flint-vs-step-3-7-flash.md): C 61 vs C 59.8 - [Ripple-65B Preview vs Step 3.7 Flash](https://www.anchorterminal.com/compare/ripple-65b-preview-vs-step-3-7-flash.md): D 53.3 vs C 59.8 - [SenseNova-Loop-9B-Preview vs Step 3.7 Flash](https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-step-3-7-flash.md): D 46.8 vs C 59.8 - [SmolLM3-3B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/smollm3-3b-vs-step-3-7-flash.md): C 61 vs C 59.8 - [Spark-X2.6-4B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-step-3-7-flash.md): C 61 vs C 59.8 - [Step 3.7 Flash vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/step-3-7-flash-vs-swift-1-5-qwen3-8-27b.md): C 59.8 vs C 61 - [Step 3.7 Flash vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/step-3-7-flash-vs-swift-1-5-qwen3-8-flash-next.md): C 59.8 vs C 54.2 - [Step 3.7 Flash vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/step-3-7-flash-vs-thinkingcap-qwen3-8-27b.md): C 59.8 vs C 61 - [Step 3.7 Flash vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/step-3-7-flash-vs-unlimited-ocr-2.md): C 59.8 vs C 54.2 - [Step 3.7 Flash vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-2b.md): C 59.8 vs C 61 - [Step 3.7 Flash vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-4b.md): C 59.8 vs C 61 - [Step 3.7 Flash vs ZAYA1-8B](https://www.anchorterminal.com/compare/step-3-7-flash-vs-zaya1-8b.md): C 59.8 vs C 61 ## Verify this listing For the vendor. 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