Chinese AI model Kimi K3.1 Pro is closing in on Anthropic’s Claude Opus 5.5
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Moonshot AI's Kimi K3.1 Pro scores 55 points on the Artificial Analysis Intelligence Index, an 11-point jump over its predecessor that puts it in fourth place. Only three Anthropic and Meta models rank higher. If Moonshot releases the weights as planned, the gap between open and proprietary models shrinks to just three points. The article Chinese AI model Kimi K3.1 Pro is closing in on Anthropic's Claude Opus 5.5 appeared first on The Decoder.
Moonshot AI’s Kimi K3.1 Pro scores 55 points on the Artificial Analysis Intelligence Index, an 11-point jump over its predecessor that puts it in fourth place. Only three Anthropic and Meta models rank higher. If Moonshot releases the weights as planned, the gap between open and proprietary models shrinks to just three points.
The article Chinese AI model Kimi K3.1 Pro is closing in on Anthropic's Claude Opus 5.5 appeared first on The Decoder.
Kyutai has released CADRE, open causal diffusion representation autoencoders for interactive world models. The Kyutai CADRE video autoencoder targets one problem: action lag. Many world models borrow autoencoders from text-to-video generators. Those autoencoders pack many frames into one latent, so every action waits. Per the research paper, CADRE world models run at 20 fps on 1 H100 and react within 230 ms. TL;DR Size: 7 autoencoders, each about 476M trained parameters plus a frozen 86M DINOv3 ViT-B/16. The world model is 1.2B parameters. Video runs at 288×512, 20 fps. Runs on: 1 H100 (bf16, batch 1). Quantized variants: Not disclosed. Performance: CADRE forms most of the latency-quality frontier. It is as fast and better in 42 of 48 comparisons with MIRA, Wan 2.2, Cosmos and LTX-2. Best: Driving FVD of 162.6 (CADRE-2×16×16, 1 step) against 599.7 for Wan 2.2. Worst: Robot manipulation action following (ARR) of 0.45 for CADRE-2×32×32, against 0.85 for Wan 2.2. What is CADRE? CADRE is a family of video autoencoders from Kyutai built for action-conditioned world models. A world model generates in the autoencoder’s latent space. So the autoencoder’s compression sets both cost per frame and action delay. Compression is written τ×s×s. τ is frames per latent, and s is the spatial factor. The research gives the latency as τ/f plus generation and decode time. At τ = 8 and 20 fps, the chunk alone adds 400 ms. Humans notice their own movement lagging beyond about 230 ms. How does CADRE work? The encoder reuses the MIRA recipe. A frozen DINOv3 extracts per-patch features from each frame. One trained 3D convolution then compresses them into a 64-channel latent. Only this bottleneck changes between the 7 members. The decoder is a causal diffusion transformer with 24 blocks. It streams chunk by chunk. Progressive self-distillation teaches it to decode in 1 or 2 steps. This happens in a single training stage, without an adversarial loss. The autoencoders train on 23 public video sources, about 76k hours. The mix spans web, robot, driving and game video. How was CADRE evaluated? Kyutai trained the same 1.2B causal diffusion transformer on each autoencoder. It used 4 environments: robot manipulation (ABC), human manipulation (EgoDex), driving (PhysicalAI AV) and indoor navigation (RealEstate10K). Each model sees 2 s of real video, then generates 4.8 s. Scores cover image quality (FID), motion quality (FVD) and action following (ARR). ARR uses a frozen inverse dynamics probe. An ARR of 1 matches real video. (function(){var f=document.getElementById('mtp-cadre-frame');window.addEventListener('message',function(e){if(e.data&&e.data.mtpEmbed==='cadre'&&e.data.height){f.style.height=e.data.height+'px';}});})(); What do the benchmarks show? On one H100, CADRE-2×32×32 reacts in 185 ms and sustains 23.4 fps with 1 decoding step. The Wan 2.2 autoencoder needs 317 ms, LTX-2 492 ms and Cosmos 605 ms. DC-AE is faster at 118 ms but frame-by-frame. The gap is widest with a moving camera. In driving, Wan 2.2 FVD reaches 599.7 and LTX-2 913.3. In indoor navigation, LTX-2 ARR falls to 0.04. Against the retrained MIRA decoder, the 1-step diffusion decoder lowers FVD by 1.4 to 2.5×. What are the 4 findings? Decode by diffusion: at strong compression, perceptual regression blurs, while diffusion samples 1 sharp video. Compress time in the autoencoder: patching inside the world model loses detail. At 8 frames, ARR drops from 0.72 to 0.56. Camera motion decides compression: still cameras tolerate temporal compression. Moving cameras favor fewer frames and coarser spatial tokens. Coarse tokens hurt motion first: in robot manipulation, 32× spatial compression multiplies FVD by 2.4 over 16×. FID barely moves. Comparison: CADRE vs other video autoencoders Latency, fpsA, FVD, ARR and PSNR: CADRE paper, Tables 4, 6 and 7. CADRE uses 1 decoding step. All figures are self-reported by Kyutai. Where does CADRE fall short? CADRE does not win everywhere. In robot manipulation, Cosmos posts an FVD of 39.5 against 47.6 for the best 1-step CADRE. At the fast end, DC-AE beats CADRE-1×48×48 in manipulation. Wan 2.2 also reconstructs held-out video far better, at 35.02 dB PSNR. Kyutai team argues reconstruction does not predict rollouts. Wan 2.2 ranks last on indoor rollout FVD. Long latents leave visible seams between chunks. At τ = 8, boundary excess reaches 1.6 to 1.8 across environments. CADRE-4×24×24 is an outlier in navigation, with FVD 403 in driving. Each world model covers one environment at 288×512. Whether findings transfer to generalist models remains open. How can developers use CADRE? The code is MIT licensed. Weights on Hugging Face are CC BY-NC 4.0, so commercial use is excluded. The release covers 7 autoencoders, 4 longer-trained multi-resolution versions and 4 world models on CADRE-2×16×16. A live demo lets you drive them by keyboard. Key Takeaways 7 open autoencoders, one bottleneck apart, isolate compression as the only variable. World models on CADRE run at 20 fps on 1 H100, under 230 ms lag. Driving FVD drops to 162.6, against 599.7 for Wan 2.2. Still cameras favor temporal compression; moving cameras favor spatial compression. Weights are non-commercial (CC BY-NC 4.0); code is MIT. Check out the paper, the Kyutai blog, the GitHub repo and the model collection. All credit goes to the researcher of this project. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well. Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with us The post Kyutai CADRE: Video Autoencoders That Cut World Model Lag Below 230 ms appeared first on MarkTechPost.
Moonshot AI has just released Kimi K3.1 Pro, the first model in its K3.1 series. Kimi K3.1 Pro is a 3T-class open-weight* model with a 1-million-token context window. Moonshot team positions it against the strongest proprietary models on coding, agentic and knowledge benchmarks. For developers, it is cheaper API pricing than K3 and open weights yet not available. *open weights are expected to release in November 2026. TL;DR Size: 2.8T total, 104B active parameters (MoE), 1M-token context. Runs on: Kimi API today. For K3, Moonshot recommends supernodes of 64+ accelerators, with MXFP4 weights and MXFP8 activations. Not a single-GPU model. Performance: Near the proprietary frontier on terminal coding; behind it on scientific terminal tasks. Best: SWE-Marathon 51.7, ahead of every model in Kimi’s chart (self-reported). Worst: Terminal-Bench Science 0.1 at 38.1, versus 68.1 for GPT-6 Astra in Kimi’s chart. What is Kimi K3.1 Pro? Kimi K3.1 Pro is a post-trained version of Kimi K3 with much larger post-training compute. Moonshot team ran joint reinforcement learning across hundreds of downstream domains. To support this, the Moonshot team optimized the RL framework, long-context inference, sandbox infrastructure and rollout scheduling. The base K3 architecture uses Kimi Delta Attention (KDA) and Attention Residuals (AttnRes). It activates 16 of 896 experts through a Stable LatentMoE framework. The model accepts text, images and video through the API, per the quickstart docs. Moonshot team also shipped Kimi K3.1 Pro Highspeed. According to the official Moonshot blog, it runs 2 to 3x faster. How does Kimi K3.1 Pro perform on benchmarks? All numbers below are self-reported by Moonshot at max reasoning effort, mostly with the Claude Code harness. Coding: On Terminal-Bench 4.0, K3.1 Pro scores 65.7%. That is just behind Claude Opus 5.5 at 66.4% in Kimi’s chart. On SWE-Marathon it leads at 51.7, ahead of Opus 5.5 at 46.3. On Kimi Code Bench 3.0 Long-horizon, an internal benchmark, it scores 61.2, up from K3’s 55.95. Science: Terminal-Bench Science 0.1 is the clear gap. K3.1 Pro scores 38.1, versus 68.1 for GPT-6 Astra and 63.3 for Opus 5.5 in Kimi’s chart. 3D and CAD: On BenchCAD with tools, K3.1 Pro scores 92.7. Opus 5.5 scores 96.2 and GPT-6 Astra 95.9. Without tools, K3.1 Pro scores 72.8, close to Opus 5.5 at 73.0. Finance: K3.1 Pro reports a state-of-the-art 64.4 on OfficeQA Pro v2. It scores 58.6 on Frontier Finance. On internal finance tests, it gains 7.2 points in modeling and 11.6 in research over K3. Hallucination: On Kimi Hallu Bench Hard, an internal stress test, claim-level hallucinations fell from 23.4% (K3) to 8.3%. That is roughly the 65% drop Moonshot cites. GPT-6 Astra still posts a lower 5.5% on the same test. window.addEventListener('message',function(e){if(e.data&&e.data.id==='mtp-k31'&&e.data.mtpH){var f=document.getElementById('mtp-k31-frame');if(f)f.style.height=e.data.mtpH+'px';}}); How does Kimi K3.1 Pro handle cyber risk? Moonshot removed cyber-related data from mid-training and RL for the open-weight release. On its internal xdev-bench-hard, an unfiltered checkpoint solved 90.5% at pass@3. The shipped K3.1 Pro solves 81.0%. Moonshot says it stays behind frontier proprietary models on offensive cyber tasks. How can developers access Kimi K3.1 Pro? The model is live on Kimi.ai, Kimi Work, Kimi Code and the Kimi API. The API IDs are kimi-k3.1-pro and kimi-k3.1-pro-highspeed. The endpoint is OpenAI-compatible. Pricing for K3.1 Pro is $2.00 input and $10.00 output per million tokens. Cache hits cost $0.20. Highspeed costs $4.00 input and $20.00 output. K3 itself was priced at $3.00 and $15.00. Practical limits from the quickstart: Thinking mode is always on. Reasoning effort is low, high or max (default max). Temperature 1.0 and top-p 0.95 are fixed. Output defaults to 131,072 tokens and can go up to 1,048,576. Access unlocks after a minimum $1 top-up. Vision input does not accept public image URLs. Use base64 or uploaded file IDs. How does it compare with competing models? window.addEventListener('message',function(e){if(e.data&&e.data.id==='mtp-k31c'&&e.data.mtpH){var f=document.getElementById('mtp-k31c-frame');if(f)f.style.height=e.data.mtpH+'px';}}); *Figure taken from Kimi’s comparison chart; not confirmed on the competitor’s own page. What are the limitations? Weights are not public yet, so self-hosting is not possible today. The 2.8T size points to multi-node, supernode-class serving. Science-heavy terminal work trails GPT-6 Astra and Opus 5.5 by 25+ points. Several claimed wins come from internal benchmarks. For K3, Moonshot warned that dropping reasoning history in a harness destabilizes output. The K3.1 Pro docs also say to return the full assistant message. Key Takeaways Kimi K3.1 Pro: 2.8T total, 104B active, 1M context. Terminal-Bench 4.0 at 65.7%, 0.7 points behind Opus 5.5. Leads SWE-Marathon at 51.7; trails badly on Terminal-Bench Science (38.1). API costs $2/$10 per million tokens, cheaper than K3. Open weights are expected in November 2026. Check out the official tech blog, the API quickstart and the launch post on X. All credit goes to the researcher of this project. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well. Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with us The post Moonshot AI Releases Kimi K3.1 Pro: A 2.8T Total/104B Active Params (MoE) Model with 1M Context appeared first on MarkTechPost.
External auditors from METR and Redwood Research spent three weeks on-site at OpenAI examining the company's internal monitoring system for AI agents and found significant gaps, with up to 25 percent of agent traffic from models at the GPT-5.5 class and above going unmonitored. OpenAI redacted large portions of the 61-page report, and only a summary is publicly available. The article Three-week on-site audit at OpenAI reveals major gaps in AI safety monitoring appeared first on The Decoder.
