#开源生态

Best AI Video Generation Models in 2026: Elo, Price per Second, Open Weights

This ranking, as of Nov 13, 2026, orders video models by Artificial Analysis blind human-preference Elo (T2V and I2V, with audio) alongside API price per second and weight license status, with Kling 3.0 1080p Pro fixed as the 1000-Elo anchor. Alibaba's Wan 3.0 leads text-to-video at 1175 Elo ($0.20/sec, closed), ByteDance's Seedance 2.5 leads image-to-video at 1161 Elo ($0.569/sec), and MiniMax H3 is the top open-weights model at #3 (1161 Elo, $0.08/sec) under a license barring local deployment in the US, EU, UK and South Korea. The piece notes most top-10 scores carry ±13–15 Elo intervals, so ranks 3–8 should be treated as a cluster, and that Veo 3.1 (964) now scores below the Kling 3.0 anchor.

The Iron(wood) Throne: Google's TPU Stakes Its Claim on Inference

SemiAnalysis reports first official long-context, multi-turn agentic inference benchmark results on TPUv7 (Ironwood) across four models on InferenceX/AgentX, finding TPUv7 either competitive or best-in-class on performance per dollar for external customers, including 1.2x to 2.7x the tokens per dollar of B200, B300 and MI355X on GLM-5.2 FP8. The results came through ecosystem partners including SGLang (RadixArk), vLLM (Inferact) and llm-d, and the piece argues NVIDIA's bureaucracy and stingy cluster contributions to open-source maintainers are among the biggest risks to its moat. It also notes NVIDIA's GB300 FP4 counter submission still trails TPUv7 FP8 on end-to-end performance per dollar, while FP4 GPUs retain a per-dollar lead on GLM-5.3 until TPUv8 adds FP4 support, expected in Q1 2027.

[AINews] LLMs can read & write neural network weights: "Weight Space Programming" cuts Autoresearch out of the RSI loop

AINews reports a paper in which a smart model directly reads and edits the weights of a smaller model, described as one-shot fixing bugs from failed traces, improving in a general direction over about 50 steps, and writing entire network weights from scratch that beat some independently trained models. The post frames this as a new recursive-self-improvement capability that emerged suddenly, notes an independent reproduction appears to have already found it, and cautions it is unlikely to scale beyond simple games and RL policies for now. The same issue also covers DeepSeek's open-sourced "super-agent" harness prototype, the GDPx benchmark where the best model scores 12%, and October model releases.

Open-weight AI models can exploit the patch gap

Researchers found that GLM 5.3, a widely available open-weight model released by Z.ai in August, could autonomously build exploits against all six Firefox releases between September 17 and October 26 using only information available at each version's release, at a marginal cost of about $1,000 per version. Visiting the model-built webpage caused the browser to read a local file and send it to an attacker's server without further user interaction, and the attacks also relied on an unpublished vulnerability that Mozilla has since patched in Firefox 159.0 (released October 27, 2026). Similar attacks on Google Chrome did not succeed as of writing, and the authors expect the threat to worsen as open-weight cyber capabilities improve.

Featured coverage of this story: 《Open-weight model GLM 5.3 built full Firefox exploit chains with zero human input in under 48 hours》