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Thursday

  1. ARC PrizeSelected

    ARC Prize publishes Meta Muse Spark Open's evaluation results on ARC-AGI

    ARC Prize published the validation results of Meta's Muse Spark Open on ARC-AGI: ARC-AGI-1 at 89.0% with $0.29 per task, ARC-AGI-2 at 57.5% with $0.73 per task, and ARC-AGI-3 at 0.1% under the standard harness with $3.9K. ARC Prize states that its v1/2 scores are close to those of Haiku 5.5 and GPT-6 Luna, but the cost is about 9 times that of the latter; the model is priced on OpenRouter at $1.25 per million input tokens and $4.25 per million output tokens. As the reasoning tier increases, ARC-AGI-2 rises from 29.4% at medium (averaging 55k tokens/task) to 57.2% at xhigh (180k tokens/task).

    ARC Prize published the validation results of Meta's Muse Spark Open on ARC-AGI: ARC-AGI-1 at 89.0% with $0.29 per task, ARC-AGI-2 at 57.5% with $0.73 per task, and ARC-AGI-3 at 0.1% under the standard harness with $3.9K. ARC Prize states that its v1/2 scores are close to those of Haiku 5.5 and GPT-6 Luna, but the cost is about 9 times that of the latter; the model is priced on OpenRouter at $1.25 per million input tokens and $4.25 per million output tokens. As the reasoning tier increases, ARC-

    Matched fieldsTitle

    #company#Evaluations / benchmarks#Reasoning#Meta
  2. ARC PrizeSelected

    ARC Prize tests Meta Spark Open: ARC-AGI-2 score 57.5%, cost about 9 times that of models with the same score

    ARC Prize tested Meta Spark Open on ARC-AGI: it scored 89.0% on ARC-AGI-1, 57.5% on ARC-AGI-2, and only 0.4% on ARC-AGI-3. Its v1/v2 scores are close to those of Haiku 5.5 and GPT-6 Luna, but its cost is about 9 times theirs. Across providers on OpenRouter, the model costs $1.25/M for input and $4.25/M for output, while Haiku 5.5 and GPT-6 Luna both cost $0.10/M for input and $0.50/M for output. As the reasoning tier increases, ARC-AGI-2 rises from 29.4% at medium (averaging 55k tokens/task) to 57.2% at xhigh (180k tokens/task).

    ARC Prize tested Meta Spark Open on ARC-AGI: it scored 89.0% on ARC-AGI-1, 57.5% on ARC-AGI-2, and only 0.4% on ARC-AGI-3. Its v1/v2 scores are close to those of Haiku 5.5 and GPT-6 Luna, but its cost is about 9 times theirs. Across providers on OpenRouter, the model costs $1.25/M for input and $4.25/M for output, while Haiku 5.5 and GPT-6 Luna both cost $0.10/M for input and $0.50/M for output. As the reasoning tier increases, ARC-AGI-2 rises from 29.4% at medium (averaging 55k tokens/task) to

    Matched fieldsTitle

    #company#Evaluations / benchmarks#Reasoning#OpenAI
  3. ARC Prize

    ARC Prize announces Meta Muse Spark Open's evaluation results on various versions of ARC-AGI

    ARC Prize announced the validation results of Meta Muse Spark Open on ARC-AGI: ARC-AGI-1 is 89.0% at $0.29 per task, ARC-AGI-2 is 57.5% at $0.73 per task, and ARC-AGI-3 is 0.4% at $3.0K under the provider adapter harness and 0.1% at $3.9K under the standard harness. ARC Prize states that its scores on v1/v2 are similar to those of Haiku 5.5 and GPT-6 Luna, but the cost is about 9 times that of the latter; the model is priced on OpenRouter at $1.25 per million input tokens and $4.25 per million output tokens, while Haiku 5.5 and GPT-6 Luna are both $0.10 per million input tokens and $0.50 per million output tokens. On public tasks, as the reasoning scale increases, the ARC-AGI-2 score rises from an average of 55k tokens/task corresponding to 29.4% at the medium tier, to 150k corresponding to 51.0% at the high tier, and then to 180k corresponding to 57.2% at the xhigh tier; ARC-AGI-1 has already reached 92.8% at the high tier and rises to 94.0% at the xhigh tier.

    ARC Prize announced the validation results of Meta Muse Spark Open on ARC-AGI: ARC-AGI-1 is 89.0% at $0.29 per task, ARC-AGI-2 is 57.5% at $0.73 per task, and ARC-AGI-3 is 0.4% at $3.0K under the provider adapter harness and 0.1% at $3.9K under the standard harness. ARC Prize states that its scores on v1/v2 are similar to those of Haiku 5.5 and GPT-6 Luna, but the cost is about 9 times that of the latter; the model is priced on OpenRouter at $1.25 per million input tokens and $4.25 per million o

    Matched fieldsTitle

    #company#Evaluations / benchmarks#Reasoning#Open-source ecosystem

Saturday

  1. Artificial Analysis

    Artificial Analysis measured Meta Muse Spark Open inference throughput

    Artificial Analysis published preliminary test results on AA-AgentPerf for Meta's new open-weight model Muse Spark Open, an 816B-parameter, 42B-active MoE that scores 39 on the Artificial Analysis Intelligence Index. Tests show NVIDIA B300 and GB300 each reach about 150M tokens/s per megawatt under throughput-first configurations, with GB300 at about 108M and B300 at about 92M at 100 tok/s output speed; AMD MI355X reaches 70M tokens/s per megawatt at 65 tok/s. 46 of the model's 62 layers use 2,048-token sliding window attention, reducing KV cache requirements at 1M context by about 75% versus full attention; Meta released an NVFP4 checkpoint and AMD released an MXFP4 checkpoint.

    Artificial Analysis published preliminary test results on AA-AgentPerf for Meta's new open-weight model Muse Spark Open, an 816B-parameter, 42B-active MoE that scores 39 on the Artificial Analysis Intelligence Index. Tests show NVIDIA B300 and GB300 each reach about 150M tokens/s per megawatt under throughput-first configurations, with GB300 at about 108M and B300 at about 92M at 100 tok/s output speed; AMD MI355X reaches 70M tokens/s per megawatt at 65 tok/s. 46 of the model's 62 layers use 2,0

    Matched fieldsTitle

    #editorial#Evaluations / benchmarks#Reasoning#English text is being prepared.

Tuesday

  3. FactRelated story

    English translation is being prepared.

    Meta updated its commercial terms, removing language that had been interpreted as allowing it to scrape website content to train AI. Neiman Lab and Cornell University law professor James Grimmelmann previously argued that the clause was intended to let Meta use scraped content for free to train AI, and could bypass blocking instructions in robots.txt. Meta Vice President of Communications Andy Stone and spokesperson Daniel Roberts had responded that Meta's use of web crawlers had not changed, and this terms update directly removed the disputed language. Other reports said that when Meta updated its business services terms, it had stated it had the right to use content from business account domains, web pages or URLs to train AI models, and deleted that language after controversy; Meta said the update was merely a consolidation of existing advertising terms, not an expansion of crawler behavior, and that the revised terms limit crawlers and data collection to advertising and business products only, while site owners can still control crawler permissions via robots.txt.

    Matched fieldsTitle

    View story:Meta updates its commercial terms, removing language that could have allowed scraping of news website content

Monday

Saturday

  1. Arena

    Arena Alignment Index Update: Three New Models from Meta, Reflection, and StepFun Join the Leaderboard

    The Arena Alignment Index has been updated, adding two new labs and three new models. Meta's Muse Wonder ranks 6th at 77.5%, the highest score among the three new models, with an unauthorized action rate of 2.09% and a deceptive completion rate of 14.41%, both the lowest of the three; Reflection's Beam-1.0-501B-A23B ranks 7th at 75.3%, with a false attribution rate of 4.50%, the best of the three; StepFun's Step 5 Preview ranks 8th at 75.1%, with an unauthorized action rate of 2.11%, close to that of Muse Wonder.

    The Arena Alignment Index has been updated, adding two new labs and three new models. Meta's Muse Wonder ranks 6th at 77.5%, the highest score among the three new models, with an unauthorized action rate of 2.09% and a deceptive completion rate of 14.41%, both the lowest of the three; Reflection's Beam-1.0-501B-A23B ranks 7th at 75.3%, with a false attribution rate of 4.50%, the best of the three; StepFun's Step 5 Preview ranks 8th at 75.1%, with an unauthorized action rate of 2.11%, close to th

    Matched fieldsTitle

    #editorial#Evaluations / benchmarks#Agents#Meta

Friday

Thursday

Wednesday

  2. Story3 reports · 2 facts

    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

    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 Artifici

    Matched fieldsTitle

Saturday

Thursday

Wednesday

Sunday

  1. Story2 reports · 1 facts

    Meta updates its commercial terms, removing language that could have allowed scraping of news website content

    Meta updated its commercial terms, removing language that had been interpreted as allowing it to scrape website content to train AI. Neiman Lab and Cornell University law professor James Grimmelmann previously argued that the clause was intended to let Meta use scraped content for free to train AI, and could bypass blocking instructions in robots.txt. Meta Vice President of Communications Andy Stone and spokesperson Daniel Roberts had responded that Meta's use of web crawlers had not changed, and this terms update directly removed the disputed language. Other reports said that when Meta updated its business services terms, it had stated it had the right to use content from business account domains, web pages or URLs to train AI models, and deleted that language after controversy; Meta said the update was merely a consolidation of existing advertising terms, not an expansion of crawler behavior, and that the revised terms limit crawlers and data collection to advertising and business products only, while site owners can still control crawler permissions via robots.txt.

    Meta updated its commercial terms, removing language that had been interpreted as allowing it to scrape website content to train AI. Neiman Lab and Cornell University law professor James Grimmelmann previously argued that the clause was intended to let Meta use scraped content for free to train AI, and could bypass blocking instructions in robots.txt. Meta Vice President of Communications Andy Stone and spokesperson Daniel Roberts had responded that Meta's use of web crawlers had not changed, an

    Matched fieldsTitle