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Terminal-Bench 2.1

Benchmark ● Covered in 7 stories + Follow

Terminal-Bench 2.1 is a coding and agent benchmark used to evaluate long-horizon, multi-step software engineering performance. In recent coverage, multiple model releases reported Terminal-Bench 2.1 results, including Google’s Gemini 3.8 Flash (89.4%), DeepSeek’s V4-Flash-0731 (82.7, with independent testing noting 79%), and Meta’s Muse Code beta (evaluated on 89 tasks). The benchmark is also referenced as a metric for comparing agentic coding systems and guiding availability decisions across model providers.

Updated 12 September 2026

Latest developments

Gemini 3.8 Flash Product Hunt · 2 weeks ago · 3 Z.ai Releases GLM-5.3-Flash: A 320B-A18B Natively Multimodal MoE With a 1M-Token Context MarkTechPost · 3 weeks ago · 4 IBM Releases Granite 4.2: Bringing Native Reasoning and Agentic RL to Open Enterprise Models MarkTechPost · 3 weeks ago · 12 Pokee AI Releases Pokee-Isaac 28B: A 10M-Token Context Agentic Model Built to Run Inside the Customer Boundary MarkTechPost · 1 month ago · 9
Meta AI Releases Muse Code (Beta): A Terminal Coding Agent Powered by the New Muse Spark 1.2 Model MarkTechPost · 1 month ago · 7
DeepSeek’s smaller model just outperformed its own flagship The New Stack · 1 month ago · 29
Gemini 3.5: frontier intelligence with action Google DeepMind · 4 months ago · 18

Timeline

Month Quarter Year

September 2026

Google launches Gemini 3.8 Flash and related agentic video understanding capabilities for its Gemini Flash models Model release

August 2026

Z.ai released and open-sourced GLM-5.3-Flash, a natively multimodal mixture-of-experts GLM model with a 1M-token context Open source release

IBM released Granite 4.2, an open-weight family of dense, decoder-only reasoning language and speech models for self-hosted enterprise agent workflows Model release

Meta releases Muse Code terminal agent and Muse Spark 1.2 coding model Model release

May 2026

Google releases Gemini 3.5 Flash and Gemini Omni models with enhanced agentic capabilities and video generation Model release

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