Search “Reasoning”

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TinyFish makes Search and Fetch free

TinyFish announced that it will waive retrieval fees for Search and Fetch within the limits. According to a MarkTechPost report, Search's free limit is 30 requests per minute and 500 per hour, while Fetch's is 150 URLs per minute and 1,000 per day. The report also notes that free retrieval does not eliminate the inference costs of downstream models: the same article of about 4,300 characters returns an estimated 1,184 to 42,774 tokens across different providers, and three pages of unfiltered content could fill a 128K context window. In the September 28, 2026 Artificial Analysis snapshot, TinyFish Search scored 71 at $9.30 per 1,000 tasks, the lowest total cost among the displayed configurations but also the lowest score, with each task taking about 59 seconds. There are currently no subsequent developments.

The author announced that the practical experience had been compiled into "Eight Lectures on Agent Engineering in Practice"

On October 18, 2026, the author announced that the practical experience had been compiled into "Eight Lectures on Agent Engineering in Practice". On the same day, the Tencent Cloud Developer Community published an introduction to the series, describing how it focuses on upgrading prototypes that can call models and tools into systems that can complete business tasks, explain failures, control costs, and iterate continuously. The introduction states that the entire series runs through two fictional teaching cases—a case-filing assistant Agent and a postoperative follow-up Agent—covering eight topics: task contracts, reasoning and collaboration, context and memory, reliable tool execution, observability and replay, evaluation and calibration, security boundaries, and production operations; the text emphasizes that a model being able to suggest the next step does not mean the system can deliver business results, and that the example fields, budgets, and figures are all teaching calculations and do not constitute legal advice. Currently, only the introduction to the series has been published, and no factual evidence has yet been seen for the content of the subsequent lectures.

Aleph Alpha releases the Kolibri-1 open-weight model

On October 16, 2026, Aleph Alpha released the open-weight model Kolibri-1. According to heise online, the model has 78B total parameters and uses a MoE architecture with 384 experts and 6 routed experts, activating only 3.46B parameters during inference, with a context of 256K tokens that can be extrapolated to about 1 million tokens. The model focuses on German and English, and its tokenizer segments German words more token-efficiently; for example, the German Bundestag glossary requires only 1,137 tokens, whereas the compared DeepSeek-V4.1-Flash requires 1,910 tokens. Pretraining used 20 trillion tokens, with data up to June 18, 2026. The report's author tested it and said its answers were correct and its German reasoning process evident, and said the Merlin-Arthur protocol makes the model hallucinate less in benchmarks, but its actual performance still needs to be tested in real-world use. There are currently no subsequent developments.

Google opens Gemini 4 Argon to developers

On October 13, 2026, Google announced that developers can now use Gemini 4 Argon to build applications. This frontier model targets complex workflows such as real software engineering, enterprise knowledge work, and cybersecurity defense, and is designed for long-horizon tasks requiring deep reasoning and structural precision. Google also provided the Gemini API developer guide and entry points for using it in Google Antigravity and Android Studio. On the same day, Google disclosed an unattended experiment: letting Argon optimize the open-source framework google/adk-python, which has about 205,000 lines of code, within 11 hours. The model proposed 6 optimizations; after human review, 2 were merged into the main branch, memory session preparation time for long sessions was reduced by about 40%, and cold-start import speed improved by 21%. On October 14, Sundar Pichai called this a good summary of the safety and alignment work on Argon 4; Ethan Mollick commented that Gemini 4 is a major leap compared with previous Gemini models but still lags on complex multi-agent tasks. On October 15, Google began rolling out Gemini 4 Argon support to the AI programming tool Antigravity.

Meta launches Muse Wonder and Muse Wonder Full models

On October 13, 2026, Meta released the Muse Wonder series, comprising two models, Muse Wonder and Muse Wonder Full, which it called its most capable models to date, achieving SOTA on personal and professional agent tasks, covering professional domains such as law, finance, and health as well as frontier coding capabilities. Both models feature Active Memory and Active Focus, and were trained, evaluated, and released according to the Meta Superintelligence Scaling Framework. According to Artificial Analysis data, the two models scored 52 and 56 respectively on Intelligence Index v4.3 at maximum reasoning effort, higher than the 48 of Muse Spark 1.3 (max); Muse Wonder is already available on Meta's first-party API and Muse Code, while Muse Wonder Full is available only to Muse Code users. On the same day, Arena added the two models to Agent Arena testing, and Simon Willison released the llm-meta-ai 0.2 plugin supporting the new models. On October 14, Arena announced Muse W

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Chollet says Argon performs similarly to Astra on ARC-AGI-3

François Chollet commented that Gemini 4 Argon performs similarly to Astra on ARC-AGI-3, with Google returning to the frontier. On the same day, ARC Prize published the verified results for Gemini 4 Argon on the ARC-AGI series: ARC-AGI-3 scored 97.5% under the provider adapter harness at a cost of $4.8K; under the standard harness it scored 49.8% at a cost of $5.8K. ARC-AGI-2 scored 91.3% at $0.32 per task; ARC-AGI-1 scored 98.5% at $0.16 per task. ARC Prize said Google DeepMind is the second frontier AI lab to exceed 95% on ARC-AGI-3, and Argon's 97.5% is second only to GPT-6 Astra's 99.9%. The standard harness test could not complete all 55 games due to API issues; the high-reasoning setting completed only 19 games, so no score was published.

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Anthropic releases Claude Opus 5.5

On September 22, 2026, Anthropic released Claude Opus 5.5. According to official materials, the model ranks first on Artificial Analysis's Intelligence Index v4.3 with a score of 58, 7 points higher than Claude Opus 5, and tops Vals AI's Vals Index at 69.69%; it supports up to 1 million tokens of input and 128,000 tokens of output, with reasoning enabled by default across five levels, and API pricing at $4/$0.25/$20 per million input/cached/output tokens. Earlier reports said its output price was $20 per million tokens, lower than the previous generation's $25. On September 23, Yuchen Jin said it was about 30% faster and about 40% cheaper per task than Opus 5. On September 25, Thomas Wolf said he used Opus 5.5 to generate a video from a Google Doc. On October 4, Ado Kukic said that using Opus 5.5 at medium and high effort he did not hit the 5-hour limit, using at most about 74% in a single session. On October 9, Ado again said that the weekend after Opus 5.5's release was his most productive coding day of the year so far. During the same period, Anthropic also released Sonnet 5.5 and Haiku 5.5, and lowered Sonn

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