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MiniMax-M3.1-Flash scores 42 on the Artificial Analysis Intelligence Index
MiniMax-M3.1-Flash scores 42 on the Artificial Analysis Intelligence Index, 13 points higher than MiniMax-M3, at less than half the latter's per-task cost. It scores 48% on Humanity's Last Exam and 22% on CritPt, both higher than GLM-5.3-Flash and Gemini 3.8 Flash, and slightly lower than DeepSeek V4.1 Flash 1017. The model has a 1M-token context window, supports text and image input, is priced at $0.15/$0.60 per 1M input/output tokens, and its weights are currently closed-source; MiniMax announced that it will open the weights on October 23.
MiniMax-M3.1-Flash scores 42 on the Artificial Analysis Intelligence Index, 13 points higher than MiniMax-M3, at less than half the latter's per-task cost. It scores 48% on Humanity's Last Exam and 22% on CritPt, both higher than GLM-5.3-Flash and Gemini 3.8 Flash, and slightly lower than DeepSeek V4.1 Flash 1017. The model has a 1M-token context window, supports text and image input, is priced at $0.15/$0.60 per 1M input/output tokens, and its weights are currently closed-source; MiniMax announ
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prime-rl 0.10.0 Released: Faster Training, New RL Loss IPO, and a Unified Evaluation Stack
Prime Intellect has released prime-rl v0.10.0 and verifiers v0.4.0, adding the RL algorithm IPO and setting it as the default, replacing the previous DPPO-based default loss. In tests comparing Qwen3-235B-A22B SFT against torchtitan, prime-rl achieved a 1.15× speedup (3732 vs 3253 tokens/s/GPU), and the MFU of full GLM-5.3 SFT rose from 22.3% to 40.1%. The evaluation CLI has moved from verifiers into prime-rl, with added support for models such as DeepSeek V4 Flash, Qwen3.8 Flash Next, and Nemotron-H.
Prime Intellect has released prime-rl v0.10.0 and verifiers v0.4.0, adding the RL algorithm IPO and setting it as the default, replacing the previous DPPO-based default loss. In tests comparing Qwen3-235B-A22B SFT against torchtitan, prime-rl achieved a 1.15× speedup (3732 vs 3253 tokens/s/GPU), and the MFU of full GLM-5.3 SFT rose from 22.3% to 40.1%. The evaluation CLI has moved from verifiers into prime-rl, with added support for models such as DeepSeek V4 Flash, Qwen3.8 Flash Next, and Nemot
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MiniMax M3.1 Flash released: output 200+TPS, price halved
MiniMax released M3.1 Flash, a lightweight model positioned for Coding/Agent optimization, with a 1 million token context, output speed reaching 200+TPS in everyday scenarios, with P50 stable at 170+TPS. Its input price is 1 yuan per million tokens, output 4 yuan per million tokens, and cache read 0.1 yuan per million tokens, roughly half of DeepSeek V4.1 Flash-1017's peak-hour price. On Automation-Bench v1.0.6 it scored 62.2 points, surpassing DeepSeek's 60.5 points; on DeepSWE v1.1 it scored 73.7 points, lower than DeepSeek's 77.0 points.
MiniMax released M3.1 Flash, a lightweight model positioned for Coding/Agent optimization, with a 1 million token context, output speed reaching 200+TPS in everyday scenarios, with P50 stable at 170+TPS. Its input price is 1 yuan per million tokens, output 4 yuan per million tokens, and cache read 0.1 yuan per million tokens, roughly half of DeepSeek V4.1 Flash-1017's peak-hour price. On Automation-Bench v1.0.6 it scored 62.2 points, surpassing DeepSeek's 60.5 points; on DeepSWE v1.1 it scored 7
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The iPhone Duo that sold out in one minute exposes Apple's foldable screen production capacity shortfall
The iPhone Duo opened pre-orders at 8 p.m. on October 16, with inventory across all channels selling out within one minute, over 3 million reservations on JD.com, official website shipments scheduled as late as January 2027, and industry estimates that first-batch volume across all channels does not exceed 200,000 units. The article attributes the shortage to the 0.01-millimeter-level tolerances of the self-developed hinge, industry yields for foldable OLED panels of less than 70%, and whole-device assembly yields of just over 60% as of September 17. Counterpoint expects Apple to reach second place globally in its first year with up to 6 million units and a 25% share, while Huawei drops to third with about 5 million units and 22%, and global foldable shipments in 2026 are expected to grow only 0.5% to 20.2 million units.
The iPhone Duo opened pre-orders at 8 p.m. on October 16, with inventory across all channels selling out within one minute, over 3 million reservations on JD.com, official website shipments scheduled as late as January 2027, and industry estimates that first-batch volume across all channels does not exceed 200,000 units. The article attributes the shortage to the 0.01-millimeter-level tolerances of the self-developed hinge, industry yields for foldable OLED panels of less than 70%, and whole-dev
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TinyFish Makes Search Free: Breaking Down the Real Cost of AI Agents Reading Web Pages
TinyFish announced that, within limits, it will waive retrieval fees for Search and Fetch: Search is limited to 30 requests per minute and 500 per hour, while Fetch is limited to 150 URLs per minute and 1,000 per day. The article 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 can 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 about 59 seconds per task.
TinyFish announced that, within limits, it will waive retrieval fees for Search and Fetch: Search is limited to 30 requests per minute and 500 per hour, while Fetch is limited to 150 URLs per minute and 1,000 per day. The article 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 can fill a 128K context window
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MiniMax Releases M3.1 Flash, Scoring 29.6% on Agents' Last Exam to Rank First Among Chinese Models
MiniMax has officially released M3.1 Flash, the model previously known as Space Bunny (Yutu) that topped the charts anonymously on OpenRouter. It scores 29.6% on Agents' Last Exam, ranking first among Chinese models. The model achieves 73.7% on DeepSWE v1.1, 82.2 points on CyberGym, and 61.7 points on OSWorld V2, with output speeds of up to 200+ TPS and a P50 maintained at 170+ TPS. Pricing is 1 yuan per million input tokens, 4 yuan for output, and 0.1 yuan for cache reads.
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MIT Course MAS.S62: Two Lectures on the Evolution of Harnesses and Multi-Agent Systems
Lectures 4-5 of the MIT course MAS.S62 "How to AI an AI" focus on Harness evolution, covering the evolution of harness components, memory, tools, and skills, as well as the evolution of planning, task decomposition, and multi-agent systems. The memory section uses a three-axis taxonomy to connect MemGPT, mem0, MEM1, and Reflexion/ExpeL, and uses CLM to unify context management as file operations; the tools section moves from Toolformer, ToolLLM, and CodeAct to Meta-Harness, which represents the entire harness as a file system and provides about 400 times the amount of feedback information of previous methods. The multi-agent section presents five empirical findings, including that MAS wins 81% on decomposable tasks and loses 70% on tasks with sequential dependencies, and that CORAL achieves new SOTA on 8 tasks, with multi-agent evolution improving over single-agent by 4%-20%.
Lectures 4-5 of the MIT course MAS.S62 "How to AI an AI" focus on Harness evolution, covering the evolution of harness components, memory, tools, and skills, as well as the evolution of planning, task decomposition, and multi-agent systems. The memory section uses a three-axis taxonomy to connect MemGPT, mem0, MEM1, and Reflexion/ExpeL, and uses CLM to unify context management as file operations; the tools section moves from Toolformer, ToolLLM, and CodeAct to Meta-Harness, which represents the
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Viggle Releases JST-2, a 3D-Native World Model
Viggle has released JST-2, building its entire model stack in 3D space and representing the world with Physics-Native Tokens that carry xyz coordinates, attribute vectors, and dynamics vectors, rather than implicitly inferring physical laws from video pixels. The architecture consists of three parts: the JST encoder, JST generator, and JST decoder. A single forward inference generates a complete 3D scene in about 1 second, at a cost below $0.01 (B200), and supports up to 64 input views as a spatial context window. Blind human preference tests published by Viggle show that character generation wins 58.4% against Meshy 7.1 and 73.2% against Tripo H3.1, while video-generated motion wins 89.1% against DeepMotion. JST-3 plans to extend the learned dynamics to object interaction.
Viggle has released JST-2, building its entire model stack in 3D space and representing the world with Physics-Native Tokens that carry xyz coordinates, attribute vectors, and dynamics vectors, rather than implicitly inferring physical laws from video pixels. The architecture consists of three parts: the JST encoder, JST generator, and JST decoder. A single forward inference generates a complete 3D scene in about 1 second, at a cost below $0.01 (B200), and supports up to 64 input views as a spat
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Ant Group's inclusionAI publishes the sglang_ling_v3 repository on GitHub
Ant Group's inclusionAI launches the sglang_ling_v3 repository on GitHub. Its contents are based on the open-source inference framework SGLang, which targets large language models, vision-language models, and diffusion models, and is optimized for agent workloads, large-scale serving, and RL rollout. The repository README shows that SGLang supports GPU, TPU, NPU, CPU, and Apple Silicon, and includes the built-in image and video generation engine SGLang Diffusion.
Ant Group's inclusionAI launches the sglang ling v3 repository on GitHub. Its contents are based on the open-source inference framework SGLang, which targets large language models, vision-language models, and diffusion models, and is optimized for agent workloads, large-scale serving, and RL rollout. The repository README shows that SGLang supports GPU, TPU, NPU, CPU, and Apple Silicon, and includes the built-in image and video generation engine SGLang Diffusion.
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Fireworks Launches Free Weights Club, Free SFT Training for Muse Glimmer 30B
Fireworks AI launches the Free Weights Club: SFT training for Muse Glimmer 30B is free until November 30, with no credit card required; inference and evaluation calls are billed separately. The tuned Glimmer trained on Kalshi market data achieved 58.3% prediction accuracy on unseen topics, 11.2 percentage points higher than Jev's 47.1%; in a historical backtest, a hypothetical $1,000 grew to $1,071 after simulated fees, while both base Glimmer and Jev posted losses. In another synthetic benchmark, the System One serving stack achieved a median server-side processing speed 2-3 times faster than standard BF16 chat completions.
Fireworks AI launches the Free Weights Club: SFT training for Muse Glimmer 30B is free until November 30, with no credit card required; inference and evaluation calls are billed separately. The tuned Glimmer trained on Kalshi market data achieved 58.3% prediction accuracy on unseen topics, 11.2 percentage points higher than Jev's 47.1%; in a historical backtest, a hypothetical $1,000 grew to $1,071 after simulated fees, while both base Glimmer and Jev posted losses. In another synthetic benchmar
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