Terminal-Bench 2.1
PulseAugur coverage of Terminal-Bench 2.1 — every cluster mentioning Terminal-Bench 2.1 across labs, papers, and developer communities, ranked by signal.
- instance of GLM-5.2 90%
- competes with Claude Fable-5 90%
- used by GPT 5.6 "Sol" 90%
- used by Laguna S 2.1 90%
- instance of GPT 5.6 "Sol" 90%
- competes with GPT 5.6 "Sol" 80%
- competes with Claude (Opus 4.8) 70%
- used by Claude (Opus 4.8) 70%
- competes with Opus 4.8 70%
- used by GLM-5.2 70%
- used by Deepsweg 70%
- used by Opus 4.8 70%
14 day(s) with sentiment data
Terminal-Bench 2.1 will see increased usage by open-source LLM developers
The recent surge in powerful open-source LLMs (e.g., from Chinese labs and Nex AGI) that rival closed-source models necessitates robust evaluation. Terminal-Bench 2.1 is emerging as a reliable benchmark, replacing older metrics. As these open-source models are increasingly used for complex tasks, developers will likely adopt Terminal-Bench 2.1 to validate their performance against real-world agentic workflows.
Terminal-Bench 2.1 adoption driven by shift to real-world agent use cases
Recent evidence highlights a growing emphasis on evaluating agent performance based on real-world use cases rather than simple scores. Terminal-Bench 2.1 is explicitly mentioned as an upgraded benchmark designed for this purpose, alongside a 250-turn limit. This suggests that its adoption is likely to increase as the community prioritizes more practical evaluation methods.
Terminal-Bench 2.1 gaining traction as a key agent evaluation benchmark
The recent cluster evidence highlights Terminal-Bench 2.1 multiple times in the context of updated agent benchmarks that reflect real-world use cases. This suggests it is becoming a more prominent and reliable metric for evaluating AI agent performance, moving beyond older benchmarks like HumanEval.
Terminal-Bench 2.1 will be integrated into more agent frameworks within 3 months
Given its increasing mention as a benchmark for real-world use cases and its inclusion in updated agent benchmarks, it's plausible that Terminal-Bench 2.1 will see broader adoption. Developers of agent frameworks may integrate it to provide more robust performance evaluations for their users.
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Claude Code skill boosts DeepSeek V4 Flash performance on Terminal-Bench 2.1
A new skill for Claude Code, named Autoprompt, has significantly improved the performance of the DeepSeek V4 Flash model on the Terminal-Bench 2.1 benchmark. This skill, which automates much of the coding loop by planni…
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Unsloth releases Dynamic v3.0 quants for Qwen3.8-27B, boosting accuracy
Unsloth has released Dynamic v3.0 quantization for Qwen3.8-27B GGUFs, offering over 10% improved accuracy at the same model size compared to other providers. This new version utilizes an improved methodology with a high…
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New FACET framework enhances AI agent training with executable tasks
Researchers have developed FACET, a framework designed to improve the synthesis of executable terminal tasks for training AI agents. FACET addresses challenges in creating high-quality tasks by preserving source intent …
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Ornith AI releases new 9B and 35B open-source LLMs · 4 sources tracked
Ornith AI has released a new suite of open-source large language models under the Ornith-1.5 designation, featuring a 9B dense model with vision capabilities and a 35B Mixture-of-Experts (MoE) model. These models were t…
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Claude multi-model orchestration backfires on benchmark, costing more for less
An experiment wiring four Claude models together for the Terminal-Bench 2.1 benchmark revealed significant drawbacks to multi-model orchestration. The setup, which aimed to use a powerful model for oversight and cheaper…
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StateM runtime boosts AI agent accuracy via harness scaling · 2 sources tracked
Researchers have developed StateM, a new runtime system designed to enhance the performance of long-horizon AI agents without modifying their underlying model weights. This system organizes agent execution around durabl…
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DeepSeek V4 Pro launches, challenging Fable 5 with strong coding benchmarks · 2 sources tracked
DeepSeek has officially released its V4 Pro model, a significant advancement in its open-source AI offerings. This new model demonstrates impressive performance, particularly in agentic coding tasks, where it rivals or …
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DeepSeek V4 Pro 0813 released, competes on cost and capability
DeepSeek has released its V4 Pro 0813 model, an enhanced version with improved agentic capabilities and performance, particularly for production environments. This model is now available on platforms like Hugging Face a…
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DeepSeek V4-Flash upgraded for agents, maintains bargain pricing · 1 source tracked
DeepSeek has upgraded its V4-Flash model, enhancing its coding and agent capabilities while maintaining low API pricing. The re-trained 284B parameter model activates approximately 13B parameters per request, resulting …
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DeepSeek V4-Flash benchmark scores questioned due to unreleased testing harness
A recent analysis of DeepSeek's V4-Flash model reveals a significant discrepancy between its claimed performance on the Terminal-Bench 2.1 benchmark and independently verified results. DeepSeek's own published chart sho…
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DeepSeek V4-Flash enhanced via post-training, gains agent API support
DeepSeek has updated its DeepSeek-V4-Flash model, focusing on post-training enhancements rather than architectural changes or parameter scaling. This revised model, now available via API, shows improved performance on a…
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ThinkyMachines releases Inkling-Small multimodal model on Together AI
ThinkyMachines has released Inkling-Small, an open-weight multimodal model available on Together AI. This new model offers performance comparable to Inkling but at a quarter of the size, making it suitable for coding, a…
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DarwinX system uses natural selection to evolve LLM agent harnesses
Researchers have introduced DarwinX, a novel system that employs natural selection principles to evolve the "harnesses" of large language model (LLM) agents. Instead of modifying model weights, DarwinX focuses on optimi…
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Together AI launches Inkling-Small multimodal model with 1M context
Together AI has launched Inkling-Small, an open-weight multimodal model developed by ThinkyMachines. This model boasts 276 billion parameters with 12 billion active, offering native reasoning across text, image, and aud…
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OpenAI's GPT-5.6 launches with large context window and cost-saving features
OpenAI has released its GPT-5.6 model family, featuring three variants including the flagship Sol model. Despite a politically charged rollout involving government vetting, the model offers a substantial 1.05 million to…
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OpenAI, Moonshot, Anthropic launch flagship models; benchmarks show varied strengths
In a rapid succession of releases, OpenAI, Moonshot AI, and Anthropic have launched their latest flagship models: GPT-5.6 Sol, Kimi K3, and Claude Opus 5, respectively. While all three models offer substantial context w…
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Poolside's Laguna S 2.1 model prioritizes agent efficiency over scale
Poolside's Laguna S 2.1 model demonstrates that agent efficiency can outperform raw scale, achieving a 70.2% score on the Terminal-Bench 2.1 test. This 8-billion parameter model, utilizing a novel thinking mode, has suc…
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OpenAI's GPT-5.6 Luna offers value tier for routine AI tasks
OpenAI has introduced GPT-5.6 Luna, positioned as a value-tier model for routine tasks, with a significantly lower cost per token compared to its counterparts. This model is designed for bounded work such as classificat…
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Poolside's Laguna S 2.1 open model surpasses DeepSeek-V4-Pro-Max
Poolside has released Laguna S 2.1, an 118-billion parameter Mixture-of-Experts model that outperforms the 1.6-trillion parameter DeepSeek-V4-Pro-Max on the Terminal-Bench 2.1 benchmark. This American-made open model bo…
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Poolside AI releases Laguna S 2.1, a compact coding model with 1M context
Poolside AI has released Laguna S 2.1, an 118B parameter Mixture-of-Experts model with 8B activated parameters and a 1M token context window. The model was developed in under nine weeks and demonstrates strong performan…