An analysis of Gemini 4 Hydrogen (High) compares its intelligence, performance and price, using capability indexes that measure model performance on specific capabilities and industries. The comparison covers token use, cost and context window, alongside performance metrics including time per task in minutes, output speed in tokens per second, and time to first answer token and first token in seconds.
Google released Gemini 4 Hydrogen, a closed-weights model from Google DeepMind positioned below Gemini 4 Carbon and Argon as a daily driver for coding and agentic workflows. It offers a 1,048,576-token unified window, four thinking levels with high as the default, and API pricing of $1.50/$7.50 per 1M input/output tokens, flat across the full window. Google reports 57% on Terminal-Bench 4.1 versus 18.2% for Gemini 3.8 Flash and 51 on the Artificial Analysis Intelligence Index, but the model ranks only #5 on Agent Arena; pairing it with Carbon in Antigravity used 24% less quota and finished 19% faster than Carbon alone.
Same event; the pick carries“Google DeepMind releases Gemini 4 Hydrogen, a faster Gemini 4 model for coding and agentic workflows”
An analysis of Gemini 4 Hydrogen (Xhigh) covers its intelligence and capability indexes across specific capabilities and industries, along with token use cost and context window. Performance is measured by time per task in minutes, output speed in tokens per second, and time to first answer token and first token in seconds.
Google has released Gemini 4 Hydrogen, a fast general-purpose model for developers and everyday users, priced at $1.50 per million input tokens and $7.50 per million output tokens with a one-million-token context window. Google says Hydrogen scores 51 on the Artificial Analysis Intelligence Index at its default high thinking level and 54 at the new xhigh level, versus 50 for the larger Gemini 4 Argon. Independent testing by Artificial Analysis puts Hydrogen roughly on par with Claude Sonnet 5.5 at maximum thinking, but weaker on agentic terminal work, scoring 54 percent on Terminal-Bench 4.0 against 64 percent for Claude Sonnet 5.5.
Same event; the pick carries“Google DeepMind releases Gemini 4 Hydrogen, a faster Gemini 4 model for coding and agentic workflows”
Google has released Gemini Decision 1, a decision model API that supports yes/no, choice and rubric-scoring questions, joining Jev and OpenAI in the decision model category. It is priced at 10 cents per million input tokens with free output, and unlike Jev's text-only input it accepts text, images, audio, video and PDFs. In a test on a robot video, Gemini returned probabilities such as 0.9234 for robot and 0.0498 for dragon instead of the 1.0 or 0.0 that Jev and OpenAI return.
inclusionAI released LemonDecider 4B v3, a decision model fine-tuned from Qwen3.5-4B that returns choice, yes/no or ordinal probabilities via a single joint forward pass instead of generating answer tokens. It was trained on about 1.20M decision/QA rows and scored 61.67 on the public Decision Index 0.4 suite, with median request latency of 49.9 ms.
inclusionAI released SingVL S1, a 4B multimodal decision model built on Qwen3.5 that answers named questions over text or JSON and up to four images in a single Transformers forward pass. It supports choice, yes/no (noul) and 2–10 level score questions, scoring 73.94 on the Public Vision Index and 69.76 on the Public Text Index. The model ships with Python inference code and a FastAPI HTTP server.
Latent Space tested NaiveAI's open-weights Naive-N0.5-Flash, a coding model derived from Xiaomi's MiMo-V2.5 and paired with the NaiveRT inference engine, against DeepSeek V4.1 Flash 1017 and GLM 5.3 Flash. In the author's runs it peaked near 2,000 tokens per second and sustained about 1,000 during thinking-heavy tasks, with a median response time of 3.1 seconds versus 5.3 and 10.1 seconds for the two comparison models. On hard HLE questions it averaged about 108,500 completion tokens with thinking on, roughly nine times GLM 5.3 Flash and 11 times DeepSeek V4.1 Flash 1017, and the author notes the sample was 10 questions per difficulty level with no accuracy grading.
Meta AI launched Muse Ember, described as its smallest and most efficient model, aimed at tool calls, coding subtasks, routing, extraction and long document parsing. For shorter prompts it is priced at $0.10 per million input tokens and $0.50 per million output, the same as GPT-6 Luna and Claude Haiku 5.5, while its long-prompt rates of $0.30 input and $1.50 output above 200k tokens place it between the two. Simon Willison notes the model sits on the Pareto line of Artificial Analysis's Intelligence Index vs. Cost per Task chart, and that its six reasoning levels produced pelican SVGs of comparable quality to Muse Wonder at 40x the price.
Anthropic released Claude Fable 5.2 and Claude Mythos 5.2, which top most independent leaderboards, including Artificial Analysis Intelligence Index v5 at 65 and SemiAnalysis GDPx at 16.4%. Pricing drops 20% to $8/$40 per 1M input/output tokens, but SemiAnalysis reports cost per GDPx task rose from $67.16 to $88.15 because the model writes hundreds to thousands of test cases and spawns subagents to check its work. Multiple evals show the max effort level underperforming high, and Fable is capped at 50% of a weekly subscription limit.
Why it matters: Anthropic releases Fable 5.2 and Mythos 5.2 with a 20% per-token price cut, yet independent evals show higher per-task cost and a max-effort setting that underperforms high.
Every's hands-on review finds Gemini 4 Carbon, released today by Google DeepMind, is a much stronger coder than Gemini 4 Argon and can build better 3D objects and work for hours with little supervision. Testers disagree on how much supervision it needs, and the review says Anthropic's Fable 5.2, released a day earlier, has raised expectations that a flagship agent also pick the right work, respect limits and know when it is finished. Google gave Every early access and had no input on the review.
Meta's Muse Ember scores 36 on the Artificial Analysis Intelligence Index, matching Claude Haiku 5.5 (max) at $0.14 per task versus $0.41 for Claude Haiku 5.5. It stands out on AutomationBench-AA at 68.1% and scores 46 on the Coding Agent Index in Muse Code, ahead of Claude Haiku 5.5 in Claude Code (37) and GPT-6 Luna in Codex (31). Its main weakness is knowledge: AA-Omniscience accuracy of 21.1% versus 34.7% for Claude Haiku 5.5, largely because it declines to answer 64% of questions, which holds its hallucination rate to 17.4%.
Nanbeige has released Nanbeige4.3-33B, an open-weights dense reasoning model under Apache 2.0 that scores 20 on the Artificial Analysis Intelligence Index v5, matching Qwen3.8 27B (xhigh) at 20. The 33B model has a 524K-token context window, text-only input and output, and no confirmed public APIs, and it scores 30% on Terminal-Bench 4.0 versus 6% for Qwen3.8 27B while trailing on HLE-Diamond (7% vs. 15%). On AA-Omniscience v1.1 it records an 18% hallucination rate versus 29% for Qwen3.8 27B, with a 71% decline rate, and it uses 158K output tokens per Intelligence Index task versus 110K for Qwen3.8 27B.
Google's Gemini 4 Carbon is the new No. 5 on the Artificial Analysis Coding Agent Index. 5 (max, 62), and it leads 4 of the 9 Index evaluations including HLE-Diamond and Terminal-Bench-Science. 1 rises from 4% to 11%; the model is being rolled out to selected users and is not yet publicly available.
Why it matters: Gemini 4 Carbon tops Artificial Analysis Intelligence Index v5 at 59 and trails Claude Opus 5.5 by 3 points on the Coding Agent Index, with per-task cost and hallucination figures for comparison.
inclusionAI released Ming-3.0-tts, an open-weight multilingual speech synthesis family with two models: Ming-3.0-tts-0.5b, a 0.5B dense model, and Ming-3.0-tts-tiny, a MoE model built on Ling V3 with 7.9B total and 1.3B active parameters. Both ship with inference code, a task cookbook and benchmark tables, and support voice cloning, text-based voice design, free-form natural-language delivery control, 14 inline paralinguistic tags, emotion and speed/pitch/volume control, plus Chinese, English, German, Spanish, French, Korean, Japanese, Sichuanese and Cantonese. On the October 2026 Artificial Analysis text-to-speech leaderboards, Ming-3.0-tts ranks 1st among open-weights models on Controlled Voice, 1st overall on Pronunciation Robustness at 93.2%, and 3rd among open-weights models on Provider Voice.
Every published a Vibe Check comparing OpenAI's GPT-6 Sol and Anthropic's Claude Opus 5.5, both released the same day, to help readers decide which model to use. The comparison draws on the outlet's own experience and evaluations of both models on its daily work, and the full article is available to subscribers.
A new lab argues that the sameness of AI prose is a training problem and has built a writing-focused model to address it. In testing, the model's output proved less predictable, though not necessarily better. The piece frames the effort against what it calls stalled progress on writing at major labs.
Vapi's Humanness Index, a blind crowdsourced leaderboard where listeners pick which of two cloned voices sounds more human, places xAI's Grok TTS at 95 and MiniMax Speech 2.5 at 93 against a real human recording scored at 100 after more than 11,000 votes. Every battle uses the same cloned voice and script across models, and the real human recording is included in the comparison set, so a 95 means listeners chose Grok TTS at nearly the same rate as the human on the same quote. Vapi argues naturalness is no longer the bottleneck for voice agents, since callers experience the voice rather than the reasoning behind it.