Arena.ai

MiniMax M3.1 Flash: Space Performance + Bunny Speed 🚀🐰 Yes, Space Bunny - which has topped global leaderboards with 200T+ tokens - was an earlier version of M3.1 Flash. 💰Redefining the performance economics of agentic AI. M3.1 Flash achieves frontier-level intelligence across key tasks such as Cowork (Automation-Bench: 62%; ALE: 30%), Coding (DeepSWE: 74%) and Visual Reasoning (BabyVision: 91%), at 1/3 the cost of M3 and 3x the speed. API pricing (flat rate): $0.15 / $0.60 per 1M input/output tokens, with cache reads at $0.015 / 1M tokens. Model weights will arrive on Oct 23. API: platform.minimax.io M Plan: minimax.io/m-plan MiniMax Code for desktop: agent.minimax.io/download

🚀 DeepSeek-V4.1-Flash-1017 is here! Stronger benchmark performance, faster task completion, and shorter outputs than the previous version—a Pareto improvement in performance and cost. Now live on our website, app, and API. 🛠️ Optimized for @DeepSeekHarness, Tencent CodeBuddy, WorkBuddy, and more. Upgrade to DeepSeek Harness v0.2.1-rc.1 or later for the best experience with the new model. 1/4

Introducing the Arena Alignment Index, our new benchmark measuring safety and alignment of AI agents in real-world use. Built from 90K+ real-world agent sessions across 27 models, the index measures three critical signals: - Unauthorized Action (UA): Taking actions beyond the user's instructions or permissions - False Attribution (FA): Attributing statements or actions that are contradicted by user-provided evidence - Deceptive Completion (DC): Claiming a task was completed when it was not. Key findings: - OpenAI models currently lead the Alignment Index - Rogue actions are rare, but can have serious consequences when they occur - Agents can mislead users about task progress - Misalignment risks increase with conversation length - Safety and alignment are improving across model generations As shown in the leaderboard below (sorted by lab), @OpenAI’s GPT-6.1-Sol leads the Arena Alignment Index with a score of 87.9, followed by @AnthropicAI’s Claude-Opus-5.5 at 83.2 and @spacexai's Grok-4.7 at 82.7. OpenAI also has the best observed rates across all three signals: 0.89% Unauthorized Action, 1.98% False Attribution, and 2.34% Deceptive Completion. Across all four labs, newer models consistently outperform their predecessors, suggesting broad progress in agent safety and alignment. As agents take on longer, more complex, and higher-stakes tasks, measuring not just what they can accomplish, but how safely and reliably they act, becomes increasingly important. This marks an important step toward making safety and alignment a core part of how Arena evaluates AI. The index is an initial starting point, and we'll continue expanding the index with additional safety signals and models over time. More analysis below👇

Today we’re releasing Muse Spark Open, the most capable US open-weight model to date. Built for coding and agentic work, it excels at long-horizon tasks, complex software engineering, and multi-step reasoning. Key Model Specs: → 816B total / 42B active parameters → 1M token context window → Natively multimodal across text, image, video, audio

Introducing our new agentic version of Max: Arena’s model router that achieves frontier-level performance at 37% lower cost. Built on 2.5M real-world agentic sessions from Agent Arena, Max learns to dynamically select the best model for each task, optimizing for both performance and cost. Max achieves a +14.7% net improvement score in Agent Arena at a median cost of $3.27 per task, matching the performance of Claude Fable 5.1 (Max) at 37% lower cost. Try Max today for a limited time, and dive into more technical details in the thread.

Exciting news: Beam-1.0-501B-A23B by @Reflection_AI just landed #12 among open weight models in Agent Arena with -3.67% Net Improvement, and #22 among open in Code Arena: WebDev with 1438 pts. It’s also fully open under Apache 2.0. On the Agent Arena leaderboard, Beam 1.0 is the top-ranked US model among open models, landing at #12. That puts it ahead of Hy3, MiMo V2.5 Pro and MiniMax M3. Its margin of error is wide (±3.84% on Net Improvement), so it’s statistically closer to the two Qwen3.8 models just above it than its rank suggests. On Code Arena: WebDev, 24 of the top 25 open models come from Chinese labs. The one exception is Reflection’s Beam 1.0 (Apache 2.0). It ranks #22 among open models, which makes it the top US open model on the board, just ahead of Kimi K2.5 Thinking and GLM-5. Congrats to the @Reflection_AI team on this solid open weights release!

Introducing Beam: a highly efficient agentic open model with 501B total parameters and 23B active. - Frontier reasoning efficiency - Advances the Western open frontier on coding & agentic tasks - Trained end-to-end from scratch Full weights release this month. Learn more about Beam: reflection.ai/beam

Agent Arena update: we’ve evolved the Bash Recovery signal, with Bash Reliability. Here’s how it impacts the leaderboard. At the top for Agent Arena: - #1 Claude Opus 5.5 (Max), @AnthropicAI holds the top - #2 GPT-6.1 Sol (Max) by @OpenAI - #3 Claude Fable 5.1 (Max) by @AnthropicAI - #4 GPT-6 Astra (Max) by @OpenAI - #5 Gemini 4 Argon (High) by @GoogleDeepMind The first 14 spots are all proprietary models with the best open weights models being GLM 5.2 at #15, followed by MiMo V2.6 Pro at #18. Read more about Bash Reliability and check out the Agent Arena leaderboard in thread.

Today we’re releasing Muse Spark Open, the most capable US open-weight model to date. Built for coding and agentic work, it excels at long-horizon tasks, complex software engineering, and multi-step reasoning. Key Model Specs: → 816B total / 42B active parameters → 1M token context window → Natively multimodal across text, image, video, audio

Muse Wonder (Max) by @AIatMeta has entered Code Arena: WebDev at #15 with 1651 pts and Text Arena at #2 with 1513 pts! In Code Arena: WebDev, Muse Wonder (Max) landed at #15 with 1651 pts. This result is slightly lower thanMuse Spark 1.3 (Max) at #12 with 1659 pts, within 4 pts of Kimi K3 (Max) at #14 with 1655 pts, and 11 pts above Grok 4.7 (xHigh) at #16 with 1640 pts. This release is a significant improvement over past Muse models in Text Arena. Compared with Muse Spark 1.3 (Max) at #11 with 1494 pts, Muse Wonder (Max) gained +19 pts and 9 places to #2. It's 12 pts behind the #1-ranked Gemini 4 Argon (High) with 1525 pts. Congrats to the @AIatMeta team on this release!

Real-world results are in for Claude Haiku 5.5 (High) by @AnthropicAI. Debuting at #30 with 1587 pts in Code Arena: WebDev, this release sits just outside the Pareto frontier while still offering strong cost efficiency. It matches GPT‑6 Luna’s price at $0.10/$0.50 per 1M input/output tokens while scoring six pts higher. Compared with other Claude models, Haiku 5.5 offers: - 90% lower cost than Haiku 4.5 at +257 pts - 95% lower cost than Sonnet 5.5 (High) at -128 pts - 98% lower cost than Opus 5 (High) at -70 pts - 99% lower cost than Fable 5.1 (Max) at -157 pts Congrats to the team at @AnthropicAI on this release!