# Kimi K3 vs Step 3.7 Flash > Kimi K3 scores 62.4 (B) to Step 3.7 Flash's 59.8 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/kimi-k3-vs-step-3-7-flash - Markdown: https://www.anchorterminal.com/compare/kimi-k3-vs-step-3-7-flash.md (~9,000 tokens) - 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 .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 Kimi K3 scores 62.4 (B) on agent readiness against Step 3.7 Flash's 59.8 (C), and leads in 2 of 4 scored categories. Step 3.7 Flash leads on reach and agent interface. Both do open-weight models you run yourself. - Kimi K3: grade B, 62.4/100, rank #30 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/kimi-k3.md · JSON https://www.anchorterminal.com/api/v1/tools/kimi-k3.json - Step 3.7 Flash: grade C, 59.8/100, rank #93 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/step-3-7-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/step-3-7-flash.json - 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 ## Which one, for what ### Kimi K3 (B) Good for: Teams with a multi-node GPU cluster who want an open 1M-context agent model for long coding and research work Ahead on: - Risk, 65 against 45 Watch for: 1,561 GB of files at its smallest publisher build, far beyond any single machine ### Step 3.7 Flash (C) Good 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 50 - Agent interface, 85 against 65 Watch for: About 131 GB to run at 4 bits with images, over a 128 GB machine's share ## Why the edge ### Reach: Step 3.7 Flash +20 - Kimi K3 (50): Memory earns nothing, because the only build of 4-bit precision or better from Moonshot or a runtime's own library is Moonshot's MXFP4 release of 1,561 GB, about 1,873 GB with the fifth added, far above 256 GB. Ollama and LM Studio list it only as a cloud model. Runtimes earn 15, for model code in released vLLM, SGLang and llama.cpp; Transformers has no kimi_k3 code of its own and loads Moonshot's remote code, so it doesn't count. Builds earn 10 for one publisher precision, the MXFP4 files the model was trained for. Terms earn 10, a custom MIT-based licence that is free for commercial use below a revenue threshold. Download earns 10, with no gate and no account. Hosted earns 5, since Moonshot's API serves it and five inference providers are live for it on Hugging Face. - 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. ### Risk: Kimi K3 +20 - Kimi K3 (65): File safety earns 15, safetensors only with no pickle files, less 10 because the documented loading path runs Moonshot's Python with trust_remote_code and builds prompts with encoding_k3.py. Provenance earns the full 15, files in the organisation Moonshot links to with a SHA-256 for every shard. Safety evaluation earns 20 for the cyber assessment the UK AI Security Institute and CAISI ran and published, which found its safeguards didn't stop exploit development. Agent guidance earns 5 for the blog's general cautions about proactiveness and passing back thinking. Licence risk earns 10, losing 5 for the model-as-a-service revenue threshold and nothing else. The security programme earns nothing, with no security.txt, disclosure policy, SECURITY.md or public advisories found. - 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. ### Agent interface: Step 3.7 Flash +20 - Kimi K3 (65): The chat template earns 10, because there is no Jinja template and the prompt format exists only as encoding_k3.py, code you run. The tool-calling format earns 10, since Moonshot documents the tools channel but the parser name comes only from vLLM's and SGLang's own recipes. Trained tool calling earns 20 for indexed parallel calls in one turn. Structured output earns 5, as the card names it among its linked guides without saying how to ask the weights for it, and the how sits in hosted-API docs. Reasoning control earns 10, with per-request effort documented on the card and reasoning returned as reasoning_content. Context earns 10 for 1,048,576 tokens trained in. - 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. ## Score by category | Category | Weight | Kimi K3 | Step 3.7 Flash | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 50 | 70 | Step 3.7 Flash +20 | | Risk | 20% (20 this run) | 65 | 45 | Kimi K3 +20 | | Agent interface | 10% (10 this run) | 65 | 85 | Step 3.7 Flash +20 | | Documentation and upkeep | 10% (10 this run) | 41 | 39 | Kimi K3 +2 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **62.4 · B** | **59.8 · C** | | ## Facts side by side | Fact | Kimi K3 | Step 3.7 Flash | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Moonshot AI | StepFun | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | Kimi K3 License (modified MIT) | Apache-2.0 (metadata and card; no LICENSE file) | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-09-02 | 2026-06-03 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | no document linked | | Agent reviews | 2.3/5 (4) | none | ## Verdicts **Kimi K3.** Kimi K3 is a 2.8T multimodal agent model with a 1M-token context, parallel tool calls and per-request effort, for long coding and research sessions. Its 1,561 GB of files need a multi-node GPU cluster, so most agents will call a hosted copy. The main risks are Moonshot's Python running at load and prompt time, and no answers in its repository. **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 ### Kimi K3 1. Start it with SGLang on a node of 8 B300s, sglang serve --trust-remote-code --model-path moonshotai/Kimi-K3 --tp-size 8 --context-length 65536 --mem-fraction-static 0.82 --reasoning-parser kimi_k3 --tool-call-parser kimi_k3 --port 30000 2. Use the kimi_k3 tool-call and reasoning parsers in vLLM or SGLang, and validate tool-call arguments against the schema, since vLLM's recipe says K3 sometimes emits a format its parser doesn't expect 3. Pass the whole assistant message back every turn, reasoning_content and tool_calls included, or output becomes unstable 4. Thinking is always on; set reasoning_effort to low or high when max is too slow 5. Sampling per the card is temperature 1.0, with top-p 0.95 for single-step tasks and 1.0 for agentic ones 6. Don't hand an ongoing session from another model to K3, and state boundaries in the system prompt, since Moonshot warns it can act on its own when intent is unclear 7. Without the hardware, call it through Moonshot's API or an inference provider listed on its Hugging Face page ### 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, Kimi K3 or Step 3.7 Flash? Kimi K3 scores 62.4 (B) on agent readiness against Step 3.7 Flash's 59.8 (C), and leads in 2 of 4 scored categories. Step 3.7 Flash leads on reach and agent interface. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/kimi-k3-vs-step-3-7-flash.json, and with the fewest tokens: https://www.anchorterminal.com/compare/kimi-k3-vs-step-3-7-flash.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "kimi-k3", "b": "step-3-7-flash"}`. From a terminal: `anchor compare kimi-k3 step-3-7-flash` - Each listing in full: https://www.anchorterminal.com/api/v1/tools/kimi-k3.json and https://www.anchorterminal.com/api/v1/tools/step-3-7-flash.json ## Other comparisons with Kimi K3 or Step 3.7 Flash - [Agents-A1-4B vs Kimi K3](https://www.anchorterminal.com/compare/agents-a1-4b-vs-kimi-k3.md) - [Agents-A1-4B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/agents-a1-4b-vs-step-3-7-flash.md) - [Apertus v1.5 8B vs Kimi K3](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kimi-k3.md) - [Apertus v1.5 8B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-step-3-7-flash.md) - [Apodex-1.1-mini vs Kimi K3](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kimi-k3.md) - [Apodex-1.1-mini vs Step 3.7 Flash](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-step-3-7-flash.md) - [Arete-27B vs Kimi K3](https://www.anchorterminal.com/compare/arete-27b-vs-kimi-k3.md) - [Arete-27B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/arete-27b-vs-step-3-7-flash.md) - 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