多模态

MiniMax-M3 by @MiniMax_AI is now live on Venice. The first open-weight model to deliver frontier coding and agentic performance with native multimodality. Built for long-horizon, complex workflows. Available anonymously.

MiniMax-M3 is live on OpenRouter! A frontier-class open-weight model that combines a 1M-token context window, frontier coding and agentic performance, and native multimodality (image & video) in one model.

⚡️ Step 3.7 Flash is here: The new frontier is agent efficiency. #1 ClawEval-1.1 (67.1), #1 SimpleVQA Search (79.2), #2 SWE-PRO (56.3), 95.3 on V* Python. Open weights under Apache 2.0. Built for agentic, coding, search, and multimodal workflows — balancing speed, cost, and reliable execution. - 400 TPS. 198B sparse MoE, ~11B active. 256K context, 3 reasoning levels. - Understands UIs, charts, docs, images — then writes code or calls tools to act on what it sees. - Web + visual search reaches further: more sources, deeper follow-up. - Reliable tool use — less drift, fewer broken toolcalls. 98%+ on τ²-bench across all difficulty levels. - Works with Claude Code, KiloCode, Hermes Agent, OpenClaw, and protocols like MCP. - Runs locally on Mac Studio M4 Max, DGX Spark, AMD AI Max+ 395. GitHub: github.com/stepfun-ai/Step-3… HuggingFace: huggingface.co/stepfun-ai/St… GGUF: huggingface.co/stepfun-ai/St… ModelScope: modelscope.cn/models/stepfun… API: platform.stepfun.ai Blog: static.stepfun.com/blog/step…

Google’s new Gemini 3.5 Flash is the clear leader on the Intelligence vs Speed Pareto frontier and makes large gains on GDPval-AA (real-world agentic tasks), but is 5x the cost of Gemini 3 Flash @GoogleDeepMind gave us pre-release access to Gemini 3.5 Flash, the latest model in its Flash family, which has traditionally has offered faster, lower-cost alternatives to Gemini Pro models. Gemini 3.5 Flash scores 55 on the Artificial Analysis Intelligence Index, up 9 points from Gemini 3 Flash, driven primarily by agentic performance gains and hallucination reduction. It achieves speeds of over 280 output tokens/s, but higher token usage and token pricing make it over 5x more costly to run the Intelligence Index than Gemini 3 Flash, and 75% more costly than Gemini 3.1 Pro. Gemini 3.5 Flash is $1.50/1M input and $9/1M output tokens, Gemini 3 Flash was $0.5/$3 per 1M input/output tokens, a 3x increase. The rest of the increase was driven by higher token usage when running our benchmarks Key results for Gemini 3.5 Flash with ‘high’ thinking level: ➤ 9 point Intelligence Index improvement: Gemini 3.5 Flash scores 55 on the Artificial Analysis Intelligence Index, up 9 points from Gemini 3 Flash. This places it ahead of Grok 4.3 (high, 53) and Claude Sonnet 4.6 (max, 52). The model improves across nearly all evaluations, with the largest gains coming from agentic evaluations and AA-Omniscience (knowledge and hallucination). On AA-Omniscience, Gemini 3.5 Flash improves by 11 points, driven primarily by reduced hallucinations, with its hallucination rate falling to 61%, a 31 point decrease compared to Gemini 3 Flash ➤ Agentic capability improvements: Gemini 3.5 Flash improves substantially over Gemini 3 Flash across our agentic evaluations, in both GDPval-AA (real-world agentic tasks) and Tau2-Bench Telecom (agentic tool use). Its GDPval-AA result is especially notable, achieving an Elo of 1656, well ahead of Gemini 3 Flash (1204) and Gemini 3.1 Pro (1314), and just behind GPT-5.4 (xhigh, 1674). This represents a meaningful step forward for Google in agentic performance, which has historically been a relative weakness for Gemini models ➤ Speed-intelligence frontier: Gemini 3.5 Flash achieves speeds of over 280 output tokens per second, ~70% faster than Gemini 3 Flash and models such as gpt-oss-120b and GPT-5.4 mini (xhigh). With its 55 Intelligence Index score, this places Gemini 3.5 Flash on the speed-intelligence Pareto frontier alongside Gemini 3.1 Pro and Gemini 3.1 Flash-Lite, reinforcing Google’s strength in models balancing speed and intelligence ➤ 5.5x increase in cost to run: Gemini 3.5 Flash costs $1,552 to run the Artificial Analysis Intelligence Index, 5.5x more than Gemini 3 Flash and 75% more than Gemini 3.1 Pro. This is driven by increases in both token usage and token prices. Output token usage is broadly unchanged from Gemini 3 Flash (73M vs. 72M), but input token usage increases significantly, driven primarily by an increase in the number of turns in agentic evaluations. Gemini 3.5 Flash is priced 3x higher than Gemini 3 Flash at $1.50/$9.00 per 1M input/output tokens, with a 90% discount for cached input tokens ➤ Google continues to lead multimodal performance: Gemini 3.5 Flash is multimodal, supporting image, video, and speech input alongside text. This differs from many proprietary models, including Claude Opus 4.7, Grok 4.3, and GPT-5.5, which support image input only. In our multimodal evaluation, MMMU-Pro, Gemini 3.5 Flash scores 84% - the highest score recorded. This puts models from Google in the top two spots, with Gemini 3.1 Pro scoring 82% Key model details: ➤ Context window: Retains the same 1M context window as Gemini 3 Flash ➤ Multimodality: Text, image, video and speech input with text output only ➤ Pricing: $1.50/$9.00 per million input/output tokens, with a 90% discount for cached input tokens Congratulations @GoogleDeepMind , @sundarpichai and @demishassabis on the great release!

We’re dropping Gemini Omni: our first step towards a model that can create anything from anything - starting with video. It combines Gemini’s intelligence with our generative media systems - representing a leap forward in world understanding, multimodality, and editing 🧵 Video

Introducing Gemini Omni 🔮........ Omni is our new model that can create anything from any input — starting with video (think Nano Banana but for video). Available in the Gemini App, Flow, and YouTube, with API support coming soon! Video